From 391c357c84a46f707d7d886a260e44d32c04f9bf Mon Sep 17 00:00:00 2001
From: Dawn <jiaxin.duan@psi.ch>
Date: Tue, 14 Apr 2026 16:07:31 +0200
Subject: [PATCH] migration and splitting AareLC

---
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 .../2026-03-06_seg_training_yolo26_split.txt  |  166 ++
 data/splits/best_v8_18092025_split.txt        |  769 +++++
 .../seg_training_yolo26_20260316_split.txt    |  409 +++
 environment.yml                               |   17 +
 experiments/axis_snapshots.py                 |   39 +
 experiments/debug_zmq_dump.py                 |   51 +
 experiments/export_depth_anything_onnx.py     |  186 ++
 experiments/pruning.py                        |   17 +
 experiments/tryyolo.py                        |   36 +
 models/active/production.json                 |    5 +
 models/registry/README.md                     |   36 +
 .../metadata.json                             |    8 +
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 .../metadata.json                             |    9 +
 pyproject.toml                                |   14 +
 requirements.txt                              |   11 +
 src/__init__.py                               |    0
 src/clip/bl_clip_manager.py                   |  701 +++++
 src/core/__init__.py                          |   12 +
 src/core/cv_logic.py                          |   71 +
 src/core/db_client.py                         |  296 ++
 src/core/image_processor.py                   |  586 ++++
 src/core/inference_client.py                  |  162 ++
 src/core/zmq_client.py                        |  201 ++
 src/gui/__init__.py                           |    5 +
 src/gui/canvas_interaction_mixin.py           |  697 +++++
 src/gui/canvas_panel.py                       |  144 +
 src/gui/class_bank_mixin.py                   |  555 ++++
 src/gui/control_panel.py                      |  337 +++
 src/gui/dataset_prep_mixin.py                 |  522 ++++
 src/gui/ml_gui.py                             | 2522 +++++++++++++++++
 src/gui/review_mixin.py                       |  474 ++++
 src/inference/inferenceserver.py              | 1391 +++++++++
 src/inference/z_m_q_inference_monitor.py      |   61 +
 src/inference/zmq_inference.py                | 1400 +++++++++
 src/server/focus_epics.py                     |  196 ++
 src/server/image_utils.py                     |   89 +
 src/server/model_utils.py                     |  128 +
 src/server/runtime_config.py                  |   42 +
 src/server/stream_utils.py                    |   70 +
 src/server/target_point.py                    |  145 +
 src/tools/__init__.py                         |    0
 src/tools/alc_integration_yolo.py             |   42 +
 src/tools/annotating_imgs.py                  |  188 ++
 src/tools/autofocus_tools.py                  |   59 +
 src/tools/file_names_record.py                |   53 +
 src/tools/generate_splits.py                  |   79 +
 src/tools/movetext_images.py                  |   29 +
 src/tools/pt2engine.py                        |   25 +
 ...repare_yolo_seg_dataset_20260306_095355.py |  344 +++
 ...repare_yolo_seg_dataset_20260316_120428.py |  344 +++
 src/training/train_yolo_seg.py                |   70 +
 tests/__init__.py                             |    0
 tests/test_generate_splits.py                 |   65 +
 tests/test_image_utils.py                     |   63 +
 tests/test_inference.py                       |   65 +
 tests/test_target_point.py                    |  139 +
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..8a747a8
--- /dev/null
+++ b/README.md
@@ -0,0 +1,275 @@
+# AareLC
+
+AareLC is an annotation and training helper for YOLO-based loop centering workflows.
+
+Main capabilities:
+- interactive GUI for image review and annotation
+- saving annotations for detection and segmentation
+- syncing image/label data from DB to local folders
+- generating a headless dataset preparation script (`train/val` split)
+- generating a matching training launcher script for Ultralytics YOLO
+
+## Quick Start (5 commands)
+```bash
+git clone <your-gitea-url>/AareLC.git
+cd AareLC
+python3 -m venv .venv && source .venv/bin/activate
+(on sls-gpu-003, recommend using uv for efficient package management: source yolo/bin/activate)
+pip install -U pip && pip install -r requirements.txt
+python3 ml_gui.py
+```
+
+on sls-gpu-003:
+after uv pip install the tensorrt-cu13-lib from the wheel file that was downloaded and saved in tmp folder (downloaded from nvidia: https://pypi.nvidia.com/)
+uv pip install --no-deps tensorrt-cu13
+uv pip install --no-deps tensorrt-cu13-bindings
+
+## Contributors
+- J. Dawn Duan
+- Filip Leonarski
+- Martin Appleby
+- Guillaume Gotthard
+- Claude (Anthropic) — AI pair programmer
+
+## Repository Structure
+
+```
+AareLC/
+├── src/
+│   ├── clip/                   clip related scripts
+│   ├── core/                   inference, DB, processing clients
+│   ├── gui/                    reusable GUI panels
+│   ├── server/                 server related scripts
+│   ├── inference/              inference related scripts
+│   ├── training/               training related scripts
+│   └── tools/                  data/annotation utilities
+├── scripts/                    production runnable entry points
+├── experiments/                exploratory, non-production
+├── config/                     yaml/config files
+├── models/
+│   ├── registry/               versioned trained models with metadata
+│   ├── base/                   pretrained upstream weights
+│   └── archived/               old/retired versions
+├── data/
+│   ├── datasets/               curated dataset images (source of truth)
+│   ├── splits/                 txt files recording split info
+│   └── training_runs/          per-run metrics/plots (gitignored)
+├── tests/                      unit tests (pytest)
+├── notebooks/
+├── environment.yml
+├── pyproject.toml
+├── requirements.txt
+└── README.md
+```
+
+## Model Registry
+
+The `models/registry/` directory is the canonical place for trained model versions.
+
+### Registry structure
+
+Each model version lives in its own directory:
+
+```
+models/registry/<model-id>/
+├── weights.pt           ← primary PyTorch checkpoint
+├── exports/             ← derived export formats
+│   ├── model.onnx
+│   ├── model.engine     ← TensorRT (hardware-specific, gitignored)
+│   └── pruned_weights.pt
+└── metadata.json        ← version info and metrics
+```
+
+### Naming convention
+
+`<architecture>-<task>-<variant>-<YYYY-MM-DD>`
+
+Examples:
+- `yolo26n-seg-overlap-false-2026-04-12` — yolo26n, segmentation, overlap_mask=False, trained 2026-04-12
+- `yolo26n-seg-multiscale-2026-03-16` — yolo26n, segmentation, multi-scale training
+
+### metadata.json fields
+
+| Field | Required | Description |
+|---|---|---|
+| `model_id` | yes | Same as directory name |
+| `family` | yes | Base architecture (e.g. `yolo26n`) |
+| `task` | yes | `segmentation`, `detection`, etc. |
+| `training_date` | yes | ISO date of training run |
+| `export_formats` | yes | List of available formats |
+| `metrics` | yes | mAP, precision, recall, etc. |
+| `dataset_version` | no | Dataset used for training |
+| `training_config` | no | Hyperparameters and settings |
+| `git_commit` | no | Commit hash at training time |
+| `framework_version` | no | Ultralytics/YOLO version |
+| `notes` | no | Free-form notes |
+| `status` | no | `active`, `deprecated`, or `archived` |
+
+### Workflow
+
+1. Train a model.
+2. Create a new registry folder using the naming convention.
+3. Save the main checkpoint as `weights.pt`.
+4. Export derived formats into `exports/`.
+5. Write `metadata.json`.
+6. If production-ready, update `models/active/production.json` to point to this version.
+7. Move outdated versions to `models/archived/` when no longer needed.
+
+### Rules
+
+- Do not overwrite an existing registry version — add a new one instead.
+- Treat each registry folder as immutable once finalized.
+- `models/active/production.json` is the source of truth for model selection.
+
+### AI-friendly summary
+
+- `models/registry/` = all versioned trained models; each subfolder = one release
+- `metadata.json` = structured description of the model
+- `models/active/` = current production pointer
+- `models/archived/` = retired models
+- Do not guess the correct model version; read the registry metadata instead
+
+## Getting Started
+
+### 1. Clone and enter project
+```bash
+git clone <your-gitea-url>/AareLC.git
+cd AareLC
+```
+
+### 2. Create environment
+Python 3.11+ is recommended.
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+### 3. Optional environment variable
+If DB access requires shared password:
+
+```bash
+export AAREDB_SHARED_PASSWORD="<your_password>"
+```
+
+### 4. Start GUI
+```bash
+python3 ml_gui.py
+```
+
+## GUI Main Functions
+
+### File / Review
+- Load local image
+- Fetch next image from DB
+- Open review folder and navigate (`N`/`P` or arrow keys)
+- Save edited review annotations
+
+### Annotation
+- Predict current image
+- Edit detections/polygons with select/brush/SAM tools
+- Save for training (stores local sets + DB annotation)
+
+### Sync
+- Retrieve missing local images/labels from DB
+- Generate YOLO segmentation preparation script (headless workflow)
+
+## Prepare Dataset For Training (Recommended Workflow)
+
+Use this when training happens on a machine without GUI.
+
+### Step A: Generate prep script from GUI machine
+In GUI:
+1. `Sync` -> `Generate YOLO Segmentation Prep Script...`
+2. Select source folder containing images/labels
+3. Choose destination dataset folder
+4. Confirm split ratio (default `0.85`) and random seed (default `42`)
+5. Choose input model:
+- local model file from `models/` (`.pt`/`.yaml`)
+- or Ultralytics model name (`yolo11n-seg.pt`, etc.)
+6. Save generated script (commit to Gitea if needed)
+
+### Step B: Run prep script on training machine
+```bash
+python3 prepare_yolo_seg_dataset_<timestamp>.py
+```
+
+Optional preview:
+```bash
+python3 prepare_yolo_seg_dataset_<timestamp>.py --dry-run
+```
+
+The script creates:
+- `<destination>/train/images`
+- `<destination>/train/labels`
+- `<destination>/val/images`
+- `<destination>/val/labels`
+
+> **Note:** In the future, training dataset folders will use links (symlinks or hardlinks) pointing to the raw images in `data/datasets/` instead of copying files. This avoids duplicating large image data on disk.
+- `<destination>/dataset.yaml`
+- `<destination>/train_yolo_seg.py`
+- `<destination>/logs/dataset_preparation_*.json`
+- `<destination>/logs/dataset_preparation_*.txt`
+
+Log includes:
+- preparation date/time
+- source/destination paths
+- train/val counts
+- class names and class counts
+- full file mapping list (source -> destination)
+- missing labels and orphan labels
+- selected model spec
+
+## Train Model
+Rule of thumb for data preperation:
+- When doing the splitting, always create links in the folder that refers to images in data/datasets/ (don't duplicate)
+- When done training, save the split into data/splits/ by only saving the names and paths
+- 
+After dataset prep:
+```bash
+python3 <destination>/train_yolo_seg.py
+```
+
+Notes:
+- if `--model` points to a local file, it will be used directly
+- if `--model` is an Ultralytics name, it will be downloaded at runtime (internet required)
+
+## Reproducibility Notes
+- split reproducibility is controlled by `--seed`
+- same input files + same seed => same train/val assignment
+- changing seed only changes distribution, not file integrity
+
+## Testing
+
+Unit tests live in `tests/` and cover pure-logic modules that have no GUI or hardware dependency.
+
+| Test file | Module under test |
+|---|---|
+| `tests/test_target_point.py` | `src/server/target_point.py` — target-point priority logic |
+| `tests/test_generate_splits.py` | `src/tools/generate_splits.py` — split-file generation |
+| `tests/test_image_utils.py` | `src/server/image_utils.py` — image decode helpers |
+
+### Run tests locally
+
+```bash
+# install test dependency (once)
+pip install pytest pytest-cov
+
+# run all unit tests
+pytest
+
+# with coverage report
+pytest --cov=src --cov-report=term-missing
+```
+
+### CI/CD
+
+A Gitea Actions workflow at `.gitea/workflows/ci.yml` runs the test suite automatically on every push and pull request to `master`/`main`.
+
+## Troubleshooting
+- `dataset.yaml not found`: run prep script first, or pass `--data /path/to/dataset.yaml`
+- no pairs found: check that image names match label names (`.txt`)
+- missing classes in logs: verify label files contain valid class ids in first column
+- model download fails: use a local `.pt` model from `models/`
diff --git a/config/args.yaml b/config/args.yaml
new file mode 100644
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--- /dev/null
+++ b/config/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: yolov8n.yaml
+data: /home/jungfrau/alc/yolo/dataset.yaml
+epochs: 800
+time: null
+patience: 300
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: train8
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.0
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/jungfrau/alc/yolo/runs/detect/train8
diff --git a/config/dataset.yaml b/config/dataset.yaml
new file mode 100644
index 0000000..ae629a5
--- /dev/null
+++ b/config/dataset.yaml
@@ -0,0 +1,14 @@
+# dataset.yaml
+path: /Users/duan_j/Applications/alc/tests/yolo  # Dataset root directory
+train: train/images     # Training images relative to 'path'
+val: val/images         # Validation images relative to 'path'
+test: test/images       # Test images relative to 'path' (optional)
+
+# Class names
+names:
+  0: loop_all
+  1: pin
+  2: crystal
+  3: loop_face
+  4: ice
+  5: needle
diff --git a/config/yolo26-seg.yaml b/config/yolo26-seg.yaml
new file mode 100644
index 0000000..c6ab487
--- /dev/null
+++ b/config/yolo26-seg.yaml
@@ -0,0 +1,52 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+scales: # model compound scaling constants, i.e. 'model=yolo26n-seg.yaml' will call yolo26-seg.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 309 layers, 3,126,280 parameters, 3,126,280 gradients, 10.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 309 layers, 11,505,800 parameters, 11,505,800 gradients, 37.4 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 329 layers, 27,112,072 parameters, 27,112,072 gradients, 132.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 441 layers, 31,515,528 parameters, 31,515,528 gradients, 150.9 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 441 layers, 70,693,800 parameters, 70,693,800 gradients, 337.7 GFLOPs
+
+# YOLO26n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Segment26, [nc, 32, 256]] # Segment26(P3, P4, P5)
diff --git a/data/.gitattributes b/data/.gitattributes
new file mode 100644
index 0000000..4b84726
--- /dev/null
+++ b/data/.gitattributes
@@ -0,0 +1 @@
+datasets/** filter=lfs diff=lfs merge=lfs -text
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diff --git a/data/splits/best_v8_18092025_split.txt b/data/splits/best_v8_18092025_split.txt
new file mode 100644
index 0000000..670d6ba
--- /dev/null
+++ b/data/splits/best_v8_18092025_split.txt
@@ -0,0 +1,769 @@
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+val/labels/E5S14_Fckase_7_0_200.txt
+val/labels/E5S14_Fckase_7_0_500.txt
+val/labels/F2S01_0_200.txt
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+val/labels/F5S03_PopoI_3_0_500.txt
+val/labels/F5S03_PopoI_3_90_200.txt
+val/labels/F5S04_PopoI_4_90_200.txt
+val/labels/F5S06_PopoI_6_0_200.txt
+val/labels/F5S06_PopoI_6_0_500.txt
+val/labels/F5S06_PopoI_6_90_500.txt
+val/labels/F5S08_PopoI_8_0_500.txt
+val/labels/F5S09_PopoI_9_90_500.txt
+val/labels/F5S10_PopoI_10_90_200.txt
+val/labels/F5S10_PopoI_10_90_500.txt
+val/labels/F5S11_PopoI_11_0_500.txt
+val/labels/F5S12_PopoI_12_90_200.txt
+val/labels/F5S13_PopoI_13_0_200.txt
+val/labels/F5S13_PopoI_13_0_500.txt
+val/labels/F5S14_PopoI_14_0_200.txt
+val/labels/sample1_0_200.txt
+val/labels/sample1_0_500.txt
+val/labels/sample1_90_500.txt
+val/labels/sample2_0_500.txt
+val/labels/sample4_90_200.txt
+val/labels/sample5_0_200.txt
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diff --git a/data/splits/seg_training_yolo26_20260316_split.txt b/data/splits/seg_training_yolo26_20260316_split.txt
new file mode 100644
index 0000000..386a025
--- /dev/null
+++ b/data/splits/seg_training_yolo26_20260316_split.txt
@@ -0,0 +1,409 @@
+train/labels/img_10283.txt
+train/labels/img_11524.txt
+train/labels/img_11680.txt
+train/labels/img_12397.txt
+train/labels/img_12521.txt
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+train/labels/img_38252.txt
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+train/labels/img_8592.txt
+train/labels/img_9414.txt
+train/labels/img_9566.txt
+train/labels/img_9905.txt
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+val/labels/img_14036.txt
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+val/labels/img_57324.txt
+val/labels/img_58441.txt
+val/labels/img_60284.txt
+val/labels/img_62008.txt
+val/labels/img_64711.txt
+val/labels/img_64812.txt
+val/labels/img_64951.txt
+val/labels/img_65079.txt
+val/labels/img_65185.txt
+val/labels/img_65231.txt
+val/labels/img_66073.txt
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diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..0a13b9f
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,17 @@
+name: "base"
+# WARNING : As Renku makes use of mamba for more efficient package builds, please use the popular "conda-forge" channel
+# For more details : https://mamba.readthedocs.io/en/latest/user_guide/troubleshooting.html#mixing-the-defaults-and-conda-forge-channels
+# Using the defaults channel from Anaconda can easily result in getting your
+# laptop, CI pipeline, etc. blacklisted by Anaconda which makes building packages
+# impossible. To get permission to use Anaconda again you need to either pay or prove that
+# you meet the requirements for a free license. Using the conda-forge channel avoids 
+# these restrictions completely.
+channels:
+  - conda-forge
+  - uv
+# dependencies:
+  - opencv-python~=4.10
+  - numpy
+  - requests
+# - one per line
+prefix: "/opt/conda"
diff --git a/experiments/axis_snapshots.py b/experiments/axis_snapshots.py
new file mode 100644
index 0000000..15149ca
--- /dev/null
+++ b/experiments/axis_snapshots.py
@@ -0,0 +1,39 @@
+import sseclient
+import time
+import requests
+
+# Direct internal network address
+TELL_URL = "http://PC17488:22222/events"
+
+
+def listen_to_events():
+    print(f"Connecting to SSE on internal network: {TELL_URL}")
+
+    while True:
+        try:
+            # We use stream=True for the persistent SSE connection
+            response = requests.get(TELL_URL, stream=True, timeout=None)
+            response.raise_for_status()
+
+            client = sseclient.SSEClient(response)
+
+            print("Successfully connected to TELL. Waiting for events...")
+
+            for event in client.events():
+                timestamp = time.strftime("%H:%M:%S")
+                print(f"[{timestamp}] {event.event}: {event.data}")
+
+        except requests.exceptions.ConnectionError:
+            print(f"Error: Could not reach {TELL_URL}. Check if PC17488 is online.")
+        except Exception as e:
+            print(f"Error: {e}")
+
+        print("Retrying in 5 seconds...")
+        time.sleep(5)
+
+
+if __name__ == "__main__":
+    try:
+        listen_to_events()
+    except KeyboardInterrupt:
+        print("\nStopped.")
\ No newline at end of file
diff --git a/experiments/debug_zmq_dump.py b/experiments/debug_zmq_dump.py
new file mode 100644
index 0000000..5d30c85
--- /dev/null
+++ b/experiments/debug_zmq_dump.py
@@ -0,0 +1,51 @@
+# debug_zmq_dump.py
+import zmq, time
+
+addr = "tcp://localhost:9091"
+topic = ""   # change if publisher uses a non-empty topic prefix
+
+ctx = zmq.Context()
+# Increase IO threads for 10Gbps link
+ctx.set(zmq.IO_THREADS, 4)
+
+sub = ctx.socket(zmq.SUB)
+# Massive 128MB buffer to ensure we don't drop frames
+sub.setsockopt(zmq.RCVBUF, 128 * 1024 * 1024)
+sub.setsockopt(zmq.RCVHWM, 5000)
+sub.connect(addr)
+sub.setsockopt_string(zmq.SUBSCRIBE, topic)
+
+print(f"ULTRA-FAST Benchmark: Connected to {addr}")
+
+# Tracking
+start_time = time.time()
+last_print_time = start_time
+frame_count = 0
+total_bytes = 0
+
+try:
+    while True:
+        # Step 1: Just suck the bytes off the wire as fast as physically possible
+        parts = sub.recv_multipart()
+        
+        # Step 2: Account for the data size
+        frame_size = sum(len(p) for p in parts)
+        total_bytes += frame_size
+        frame_count += 1
+        
+        # Step 3: Print stats every 2 seconds for stability
+        now = time.time()
+        if now - last_print_time >= 2.0:
+            elapsed = now - last_print_time
+            fps = frame_count / elapsed
+            mbps = (total_bytes / 1024 / 1024 * 8) / elapsed
+            
+            print(f"STREAM STATS: {fps:.2f} FPS | {mbps:.1f} Mbps | Frame: {frame_size/1e6:.2f} MB")
+            
+            # Reset
+            frame_count = 0
+            total_bytes = 0
+            last_print_time = now
+            
+except KeyboardInterrupt:
+    print("\nBenchmark stopped.")
\ No newline at end of file
diff --git a/experiments/export_depth_anything_onnx.py b/experiments/export_depth_anything_onnx.py
new file mode 100644
index 0000000..1157ec8
--- /dev/null
+++ b/experiments/export_depth_anything_onnx.py
@@ -0,0 +1,186 @@
+#!/usr/bin/env python3
+"""Export Depth Anything V1/V2 Hugging Face models to ONNX."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+import shutil
+import subprocess
+
+import torch
+from transformers import AutoModelForDepthEstimation
+
+
+class DepthWrapper(torch.nn.Module):
+    """Expose predicted_depth directly for ONNX export."""
+
+    def __init__(self, model: torch.nn.Module):
+        super().__init__()
+        self.model = model
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        return self.model(pixel_values=pixel_values).predicted_depth
+
+
+def export_model(model_id: str, out_path: Path, opset: int, size: int) -> None:
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    model = AutoModelForDepthEstimation.from_pretrained(model_id).eval()
+    wrapped = DepthWrapper(model).eval()
+    dummy = torch.randn(1, 3, size, size, dtype=torch.float32)
+
+    torch.onnx.export(
+        wrapped,
+        (dummy,),
+        str(out_path),
+        input_names=["pixel_values"],
+        output_names=["predicted_depth"],
+        dynamic_axes={
+            "pixel_values": {0: "batch", 2: "height", 3: "width"},
+            "predicted_depth": {0: "batch", 1: "height", 2: "width"},
+        },
+        opset_version=opset,
+        do_constant_folding=True,
+    )
+    print(f"Exported {model_id} -> {out_path}", flush=True)
+
+
+def build_trt_engine(
+    onnx_path: Path,
+    engine_path: Path,
+    trtexec_path: str,
+    min_hw: int,
+    opt_hw: int,
+    max_hw: int,
+    fp16: bool,
+) -> None:
+    engine_path.parent.mkdir(parents=True, exist_ok=True)
+    cmd = [
+        trtexec_path,
+        f"--onnx={onnx_path}",
+        f"--saveEngine={engine_path}",
+        f"--minShapes=pixel_values:1x3x{min_hw}x{min_hw}",
+        f"--optShapes=pixel_values:1x3x{opt_hw}x{opt_hw}",
+        f"--maxShapes=pixel_values:1x3x{max_hw}x{max_hw}",
+    ]
+    if fp16:
+        cmd.append("--fp16")
+    print("Running:", " ".join(cmd), flush=True)
+    subprocess.run(cmd, check=True)
+    print(f"Built TensorRT engine -> {engine_path}", flush=True)
+
+
+def main() -> None:
+    project_root = Path(__file__).resolve().parents[2]
+    default_models_dir = project_root / "models"
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--v1-model-id",
+        default="LiheYoung/depth-anything-small-hf",
+        help="HF model id for Depth Anything V1.",
+    )
+    parser.add_argument(
+        "--v2-model-id",
+        default="depth-anything/Depth-Anything-V2-Small-hf",
+        help="HF model id for Depth Anything V2.",
+    )
+    parser.add_argument(
+        "--v1-out",
+        default=str(default_models_dir / "depth_anything_v1_small.onnx"),
+        help="Output ONNX path for V1.",
+    )
+    parser.add_argument(
+        "--v2-out",
+        default=str(default_models_dir / "depth_anything_v2_small.onnx"),
+        help="Output ONNX path for V2.",
+    )
+    parser.add_argument(
+        "--which",
+        choices=["v1", "v2", "both"],
+        default="both",
+        help="Which model(s) to export.",
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=384,
+        help="Dummy export size (HxW) for tracing.",
+    )
+    parser.add_argument(
+        "--opset",
+        type=int,
+        default=17,
+        help="ONNX opset version.",
+    )
+    parser.add_argument(
+        "--build-trt",
+        action="store_true",
+        help="Also build TensorRT engine(s) via trtexec after ONNX export.",
+    )
+    parser.add_argument(
+        "--v1-engine-out",
+        default=str(default_models_dir / "depth_anything_v1_small.engine"),
+        help="Output TensorRT engine path for V1.",
+    )
+    parser.add_argument(
+        "--v2-engine-out",
+        default=str(default_models_dir / "depth_anything_v2_small.engine"),
+        help="Output TensorRT engine path for V2.",
+    )
+    parser.add_argument(
+        "--trtexec",
+        default="",
+        help="Path to trtexec binary (auto-detects /usr/src/tensorrt/bin/trtexec if empty).",
+    )
+    parser.add_argument("--min-shape", type=int, default=224, help="TRT min H/W.")
+    parser.add_argument("--opt-shape", type=int, default=384, help="TRT opt H/W.")
+    parser.add_argument("--max-shape", type=int, default=768, help="TRT max H/W.")
+    parser.add_argument(
+        "--no-fp16",
+        action="store_true",
+        help="Disable FP16 when building TensorRT engines.",
+    )
+    args = parser.parse_args()
+
+    v1_onnx = Path(args.v1_out)
+    v2_onnx = Path(args.v2_out)
+    if args.which in {"v1", "both"}:
+        export_model(args.v1_model_id, v1_onnx, args.opset, args.size)
+    if args.which in {"v2", "both"}:
+        export_model(args.v2_model_id, v2_onnx, args.opset, args.size)
+
+    if args.build_trt:
+        trtexec_path = args.trtexec.strip()
+        if not trtexec_path:
+            trtexec_path = shutil.which("trtexec") or "/usr/src/tensorrt/bin/trtexec"
+        if not Path(trtexec_path).exists():
+            raise FileNotFoundError(
+                f"trtexec not found at '{trtexec_path}'. "
+                "Provide --trtexec /path/to/trtexec."
+            )
+        fp16 = not args.no_fp16
+        if args.which in {"v1", "both"}:
+            build_trt_engine(
+                v1_onnx,
+                Path(args.v1_engine_out),
+                trtexec_path,
+                args.min_shape,
+                args.opt_shape,
+                args.max_shape,
+                fp16,
+            )
+        if args.which in {"v2", "both"}:
+            build_trt_engine(
+                v2_onnx,
+                Path(args.v2_engine_out),
+                trtexec_path,
+                args.min_shape,
+                args.opt_shape,
+                args.max_shape,
+                fp16,
+            )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/pruning.py b/experiments/pruning.py
new file mode 100644
index 0000000..340572f
--- /dev/null
+++ b/experiments/pruning.py
@@ -0,0 +1,17 @@
+import torch
+import torch.nn.utils.prune as prune
+from ultralytics import YOLO
+
+# Load trained YOLO model
+model_name = "best_yolo26n-seg-overlap-false_2026-04-12"
+model = YOLO(f"/Users/duan_j/repos/aare_suite/AareLC/models/{model_name}.pt")
+net = model.model  # underlying PyTorch model
+
+# Prune 30% of weights in Conv2d and Linear layers
+for module in net.modules():
+    if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
+        prune.l1_unstructured(module, name="weight", amount=0.3)
+        prune.remove(module, "weight")
+
+# Save pruned model weights
+torch.save(net.state_dict(), f"pruned_{model_name}_weights.pt")
\ No newline at end of file
diff --git a/experiments/tryyolo.py b/experiments/tryyolo.py
new file mode 100644
index 0000000..a43c5b7
--- /dev/null
+++ b/experiments/tryyolo.py
@@ -0,0 +1,36 @@
+from ultralytics import YOLO
+import os
+import torch
+# Load model
+# model = YOLO(f"/Users/duan_j/Applications/alc/tests/yolo/runs/detect/train{trainingset}/weights/best.pt")  # load pretrained model
+#model = YOLO(f"models/best_v12_22092025.pt")  # load pretrained model
+model_name = "best_yolo26n-seg-overlap-false_2026-04-12_nms"
+model_type = "engine"
+model = YOLO(f"/Users/duan_j/repos/aare_suite/AareLC/models/{model_name}.{model_type}")
+
+prune = False
+# Load the pruned state_dict
+if prune:
+   state_dict = torch.load(f"/Users/duan_j/repos/aare_suite/AareLC/models/pruned_{model_name}_weights.pt", map_location="cpu")
+   model.model.load_state_dict(state_dict, strict=True)
+
+#model.export(format="engine", conf=0.25, iou=0.45) this enables nms
+
+# model = YOLO("yolov8n.yaml")  # build new model from YAML
+
+# Export the model to TensorRT engine format
+# model.export(format="engine", imgsz=640, simplify=False)  # Adjust imgsz if needed
+
+# Continue training with new data
+# results = model.train(
+#     data="/Users/duan_j/Applications/alc/tests/yolo/dataset.yaml",
+#     epochs=100,
+# )
+results = model.predict(
+   # source = "/Users/duan_j/Applications/alc/tests/sample_img/20240109/only_images/A2",
+   source="/Users/duan_j/repos/aare_suite/AareLC/testresults/testimgs",
+   save=True,
+   project=f"/Users/duan_j/repos/aare_suite/AareLC/testresults",
+   name=f"{model_name}",
+   conf=0.5  # Confidence threshold
+)
\ No newline at end of file
diff --git a/models/active/production.json b/models/active/production.json
new file mode 100644
index 0000000..962633a
--- /dev/null
+++ b/models/active/production.json
@@ -0,0 +1,5 @@
+{
+  "version": "yolo26n-seg-overlap-false-2026-04-12",
+  "export": "model_nms.engine",
+  "promoted_on": "2026-04-13"
+}
diff --git a/models/registry/README.md b/models/registry/README.md
new file mode 100644
index 0000000..403d595
--- /dev/null
+++ b/models/registry/README.md
@@ -0,0 +1,36 @@
+# Model Registry
+
+Each trained model lives in its own versioned subdirectory:
+
+```
+models/registry/<version-id>/
+├── weights.pt           ← primary PyTorch checkpoint
+├── exports/             ← derived export formats
+│   ├── model.onnx
+│   ├── model.engine     ← TensorRT (hardware-specific, gitignored)
+│   └── pruned_weights.pt
+└── metadata.json        ← version info and metrics
+```
+
+## Naming convention
+
+`<architecture>-<task>-<variant>-<YYYY-MM-DD>`
+
+Examples:
+- `yolo26n-seg-overlap-false-2026-04-12` — yolo26n, segmentation, overlap_mask=False, trained 2026-04-12
+- `yolo26n-seg-multiscale-2026-03-16` — yolo26n, segmentation, multi-scale training
+
+## metadata.json schema
+
+| Field | Description |
+|---|---|
+| `model_id` | Same as directory name |
+| `family` | Base architecture (e.g. `yolo26n`) |
+| `task` | `segmentation`, `detection`, etc. |
+| `training_date` | ISO date of training run |
+| `export_formats` | List of available formats |
+| `metrics` | mAP, precision, recall, etc. |
+
+## Active production model
+
+See `models/active/production.json` for the currently deployed version.
diff --git a/models/registry/yolo26n-seg-2026-03-06-train4/metadata.json b/models/registry/yolo26n-seg-2026-03-06-train4/metadata.json
new file mode 100644
index 0000000..2bfe7c7
--- /dev/null
+++ b/models/registry/yolo26n-seg-2026-03-06-train4/metadata.json
@@ -0,0 +1,8 @@
+{
+  "model_id": "yolo26n-seg-2026-03-06-train4",
+  "family": "yolo26n",
+  "task": "segmentation",
+  "training_date": "2026-03-06",
+  "export_formats": ["pt", "onnx"],
+  "metrics": {}
+}
diff --git a/models/registry/yolo26n-seg-multiscale-2026-03-16/metadata.json b/models/registry/yolo26n-seg-multiscale-2026-03-16/metadata.json
new file mode 100644
index 0000000..f10e5ba
--- /dev/null
+++ b/models/registry/yolo26n-seg-multiscale-2026-03-16/metadata.json
@@ -0,0 +1,8 @@
+{
+  "model_id": "yolo26n-seg-multiscale-2026-03-16",
+  "family": "yolo26n",
+  "task": "segmentation",
+  "training_date": "2026-03-16",
+  "export_formats": ["pt", "onnx", "engine"],
+  "metrics": {}
+}
diff --git a/models/registry/yolo26n-seg-overlap-false-2026-03-06/metadata.json b/models/registry/yolo26n-seg-overlap-false-2026-03-06/metadata.json
new file mode 100644
index 0000000..7f8c706
--- /dev/null
+++ b/models/registry/yolo26n-seg-overlap-false-2026-03-06/metadata.json
@@ -0,0 +1,8 @@
+{
+  "model_id": "yolo26n-seg-overlap-false-2026-03-06",
+  "family": "yolo26n",
+  "task": "segmentation",
+  "training_date": "2026-03-06",
+  "export_formats": ["pt"],
+  "metrics": {}
+}
diff --git a/models/registry/yolo26n-seg-overlap-false-2026-04-12/metadata.json b/models/registry/yolo26n-seg-overlap-false-2026-04-12/metadata.json
new file mode 100644
index 0000000..6e9fd57
--- /dev/null
+++ b/models/registry/yolo26n-seg-overlap-false-2026-04-12/metadata.json
@@ -0,0 +1,9 @@
+{
+  "model_id": "yolo26n-seg-overlap-false-2026-04-12",
+  "family": "yolo26n",
+  "task": "segmentation",
+  "training_date": "2026-04-12",
+  "export_formats": ["pt", "engine"],
+  "metrics": {},
+  "notes": "current production candidate"
+}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..377960a
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,14 @@
+[project]
+name = "AareLC"
+version = "0.1.1"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "opencv-python>=4.12.0.88",
+    "ultralytics>=8.4.14",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-v --tb=short"
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..7598368
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,11 @@
+opencv-python~=4.10
+numpy
+requests
+unittest-xml-reporting
+fastapi
+ultralytics
+uvicorn
+python-multipart
+zmq
+pyepics
+caproto
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/clip/bl_clip_manager.py b/src/clip/bl_clip_manager.py
new file mode 100644
index 0000000..da4b5b1
--- /dev/null
+++ b/src/clip/bl_clip_manager.py
@@ -0,0 +1,701 @@
+import argparse
+import datetime
+import json
+import os
+import time
+from typing import Any
+
+import requests
+import urllib3
+from websocket import create_connection
+
+# Disable SSL warnings if using local IP without a certificate
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+# --- CONFIGURATION ---
+NAS_IP = os.getenv("NAS_IP")  # Synology IP
+NAS_USER = os.getenv("NAS_USER")
+NAS_PASS = os.getenv("NAS_PASS")
+AAREDB_SHARED_PASSWORD = os.getenv("AAREDB_SHARED_PASSWORD")
+
+# Where downloaded files are stored by this script runtime
+DEFAULT_OUTPUT_DIR = os.getenv("CLIP_OUTPUT_DIR", "./clips")
+CLIP_TMP_DIR = os.getenv("CLIP_TMP_DIR", "/sls/mx/misc")
+CLIP_PUBLIC_BASE = os.getenv("CLIP_PUBLIC_BASE", "").strip()
+NAS_EXPORT_DSTDIR = os.getenv("NAS_EXPORT_DSTDIR", "EventClips")
+
+# Dispatcher endpoints for clip jobs
+DISPATCHER_WS_URL = os.getenv(
+    "DISPATCHER_WS_URL",
+    "wss://mx-aaredb-dmz-01.psi.ch/dispatcher/protected_router/clip_runner/ws/jobs",
+    #"wss://127.0.0.1:8001/dispatcher/protected_router/clip_runner/ws/jobs",
+
+)
+DISPATCHER_STATUS_URL = os.getenv(
+    "DISPATCHER_STATUS_URL",
+    "https://mx-aaredb-dmz-01.psi.ch/dispatcher/protected_router/clip_runner/jobs/update_status",
+    #"https://127.0.0.1:8001/dispatcher/protected_router/clip_runner/jobs/update_status",
+
+)
+DISPATCHER_CA_CERT = os.getenv("DISPATCHER_CA_CERT", "").strip() or None
+DISPATCHER_CLIENT_CERT = os.getenv("DISPATCHER_CLIENT_CERT", "").strip() or None
+DISPATCHER_CLIENT_KEY = os.getenv("DISPATCHER_CLIENT_KEY", "").strip() or None
+# If a CA cert path is given, use it for verification; otherwise fall back to bool flag
+DISPATCHER_VERIFY_SSL: bool | str = DISPATCHER_CA_CERT or (
+    os.getenv("DISPATCHER_VERIFY_SSL", "false").lower() in ("1", "true", "yes")
+)
+
+
+def ensure_env_ready():
+    missing = []
+    for key, value in (
+        ("NAS_IP", NAS_IP),
+        ("NAS_USER", NAS_USER),
+        ("NAS_PASS", NAS_PASS),
+        ("AAREDB_SHARED_PASSWORD", AAREDB_SHARED_PASSWORD),
+    ):
+        if not value:
+            missing.append(key)
+    if missing:
+        raise RuntimeError(f"Missing required environment variables: {', '.join(missing)}")
+
+
+def get_sid(session: str = "SurveillanceStation", request_token: bool = False) -> tuple[str, str | None]:
+    """Logs in and returns (sid, synotoken). synotoken is required for POST requests on DSM 7+."""
+    url = f"http://{NAS_IP}:5000/webapi/auth.cgi"
+    params = {
+        "api": "SYNO.API.Auth",
+        "version": "3",
+        "method": "login",
+        "account": NAS_USER,
+        "passwd": NAS_PASS,
+        "session": session,
+        "format": "sid",
+    }
+    if request_token:
+        params["enable_syno_token"] = "yes"
+    r = requests.get(url, params=params, timeout=30)
+    r.raise_for_status()
+    payload = r.json()
+    if not payload.get("success"):
+        raise RuntimeError(f"NAS login failed: {payload}")
+    data = payload["data"]
+    return data["sid"], data.get("synotoken")
+
+
+def logout_sid(sid: str):
+    url = (
+        f"http://{NAS_IP}:5000/webapi/auth.cgi"
+        f"?api=SYNO.API.Auth&version=3&method=logout&sid={sid}"
+    )
+    try:
+        requests.get(url, timeout=15)
+    except Exception:
+        pass
+
+
+def start_range_export(sid: str, camera_id: int, start_time: int, end_time: int, event_name: str):
+    """
+    Starts a time-range export task and returns its download id (dlid).
+    """
+    url = f"http://{NAS_IP}:5000/webapi/entry.cgi"
+    params = {
+        "api": "SYNO.SurveillanceStation.Recording",
+        "method": "RangeExport",
+        "version": "6",
+        "_sid": sid,
+        "camId": camera_id,
+        "fromTime": start_time,
+        "toTime": end_time,
+        "fileName": event_name,
+    }
+    r = requests.get(url, params=params, timeout=60)
+    r.raise_for_status()
+    return r.json()
+
+
+def start_nas_save_export(
+    sid: str,
+    camera_id: int,
+    start_time: int,
+    end_time: int,
+    event_name: str,
+    nas_dstdir: str,
+):
+    """
+    Ask Surveillance Station to save export directly on the NAS (no local download).
+    """
+    url = f"http://{NAS_IP}:5000/webapi/entry.cgi"
+    # Pre-check export feasibility in the camelCase flavor used by SS docs examples.
+    check_params = {
+        "api": "SYNO.SurveillanceStation.Recording.Export",
+        "method": "CheckAvailableExport",
+        "version": "1",
+        "_sid": sid,
+        "camIdList": str(camera_id),
+        "startTime": start_time,
+        "stopTime": end_time,
+        "freeSize": 0,
+    }
+    check_payload = None
+    try:
+        chk = requests.get(url, params=check_params, timeout=60)
+        chk.raise_for_status()
+        check_payload = chk.json()
+        print(f"CheckAvailableExport: {check_payload}")
+    except Exception as exc:
+        print(f"CheckAvailableExport failed (continuing with Save attempts): {exc}")
+
+        # Try multiple variants because SS builds differ significantly in parameter naming
+        save_variants = [
+            {
+                "api": "SYNO.SurveillanceStation.Recording.Export",
+                "method": "Save",
+                "version": "1",
+                "_sid": sid,
+                "name": event_name,
+                "srcDsId": 0,
+                "dstDsId": 0,
+                "dstdir": nas_dstdir,
+                "startTime": start_time,
+                "stopTime": end_time,
+                "isoverwrite": 1,
+                "camIdList": str(camera_id),
+            },
+            {
+                "api": "SYNO.SurveillanceStation.Recording.Export",
+                "method": "Save",
+                "version": "1",
+                "_sid": sid,
+                "name": f"\"{event_name}\"",
+                "srcDsId": 0,
+                "dstDsId": 0,
+                "dstdir": f"\"{nas_dstdir}\"",
+                "start_time": start_time,
+                "stop_time": end_time,
+                "isoverwrite": 1,
+                "camlistid": f"\"{camera_id}\"",
+            },
+        ]
+
+        last_payload = None
+    for idx, params in enumerate(save_variants, start=1):
+        r = requests.get(url, params=params, timeout=60)
+        r.raise_for_status()
+        payload = r.json()
+        print(f"Save variant #{idx}: {payload}")
+        if payload.get("success"):
+            return payload, check_payload
+        last_payload = payload
+
+    return (
+        last_payload or {"success": False, "error": {"code": -1, "message": "Save export failed"}},
+        check_payload,
+    )
+
+
+def wait_for_export(sid: str, dlid: int, timeout_sec: int = 600, poll_interval_sec: int = 2):
+    """
+    Polls export progress and returns the final file extension.
+    """
+    url = f"http://{NAS_IP}:5000/webapi/entry.cgi"
+    deadline = time.time() + timeout_sec
+
+    while time.time() < deadline:
+        params = {
+            "api": "SYNO.SurveillanceStation.Recording",
+            "method": "GetRangeExportProgress",
+            "version": "6",
+            "_sid": sid,
+            "dlid": dlid,
+        }
+        r = requests.get(url, params=params, timeout=60)
+        r.raise_for_status()
+        result = r.json()
+        if not result.get("success"):
+            return None, result
+
+        data = result.get("data", {})
+        progress = data.get("progress", 0)
+        print(f"Export progress (dlid={dlid}): {progress}%")
+
+        if progress == 100:
+            return data.get("fileExt") or "mp4", result
+        if progress == -1:
+            return None, {
+                "success": False,
+                "error": {"code": -1, "message": "Export task failed on NAS"},
+            }
+
+        time.sleep(poll_interval_sec)
+
+    return None, {
+        "success": False,
+        "error": {"code": -2, "message": "Timed out waiting for export"},
+    }
+
+
+def download_export(sid: str, dlid: int, event_name: str, file_ext: str, output_dir: str):
+    """
+    Downloads completed export data and saves to output_dir.
+    """
+    url = f"http://{NAS_IP}:5000/webapi/entry.cgi"
+    params = {
+        "api": "SYNO.SurveillanceStation.Recording",
+        "method": "OnRangeExportDone",
+        "version": "6",
+        "_sid": sid,
+        "dlid": dlid,
+        "fileName": event_name,
+    }
+    r = requests.get(url, params=params, stream=True, timeout=120)
+    r.raise_for_status()
+
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, f"{event_name}.{file_ext}")
+    with open(output_path, "wb") as f:
+        for chunk in r.iter_content(chunk_size=1024 * 1024):
+            if chunk:
+                f.write(chunk)
+    return output_path
+
+
+def upload_to_filestation(local_path: str, nas_folder: str) -> str:
+    """
+    Uploads a local file to a NAS folder via SYNO.FileStation.Upload.
+    Uses a FileStation-scoped session (separate from SurveillanceStation).
+    Returns the NAS destination path: nas_folder/filename.
+    """
+    fs_sid, fs_token = get_sid(session="FileStation", request_token=True)
+    try:
+        url = f"http://{NAS_IP}:5000/webapi/entry.cgi"
+        filename = os.path.basename(local_path)
+        headers = {}
+        if fs_token:
+            headers["X-SYNO-TOKEN"] = fs_token
+        with open(local_path, "rb") as f:
+            r = requests.post(
+                url,
+                params={
+                    "api": "SYNO.FileStation.Upload",
+                    "version": "2",
+                    "method": "upload",
+                    "_sid": fs_sid,
+                },
+                data={
+                    "path": nas_folder,
+                    "create_parents": "true",
+                    "overwrite": "true",
+                },
+                files={"file": (filename, f, "application/octet-stream")},
+                headers=headers,
+                timeout=300,
+            )
+        r.raise_for_status()
+        payload = r.json()
+        if not payload.get("success"):
+            raise RuntimeError(f"FileStation upload failed: {payload}")
+        return f"{nas_folder.rstrip('/')}/{filename}"
+    finally:
+        logout_sid(fs_sid)
+
+
+def build_public_clip_ref(local_path: str, output_dir: str) -> str:
+    """
+    Convert local clip path to a shareable link if CLIP_PUBLIC_BASE is configured.
+    Example:
+      output_dir=/volume1/SharedFolder/EventClips
+      CLIP_PUBLIC_BASE=smb://nas.local/SharedFolder/EventClips
+      -> smb://nas.local/SharedFolder/EventClips/<file>
+    """
+    if not CLIP_PUBLIC_BASE:
+        return local_path
+    try:
+        rel = os.path.relpath(local_path, output_dir)
+    except ValueError:
+        # Different drives or invalid relpath on some platforms -> fallback
+        rel = os.path.basename(local_path)
+    rel = rel.replace("\\", "/")
+    return f"{CLIP_PUBLIC_BASE.rstrip('/')}/{rel.lstrip('/')}"
+
+
+def list_cameras(sid: str):
+    url = f"http://{NAS_IP}:5000/webapi/entry.cgi"
+    params = {
+        "api": "SYNO.SurveillanceStation.Camera",
+        "method": "List",
+        "version": "9",
+        "_sid": sid,
+        "limit": -1,
+    }
+    r = requests.get(url, params=params, timeout=30)
+    r.raise_for_status()
+    return r.json()
+
+
+def parse_time_to_unix(value: str) -> int:
+    """Accepts 'YYYY-MM-DD HH:MM:SS' or ISO 8601 and returns Unix timestamp."""
+    for fmt in ("%Y-%m-%d %H:%M:%S", "%Y-%m-%dT%H:%M:%S"):
+        try:
+            return int(datetime.datetime.strptime(value, fmt).timestamp())
+        except ValueError:
+            pass
+    try:
+        iso_dt = datetime.datetime.fromisoformat(value.replace("Z", "+00:00"))
+        return int(iso_dt.timestamp())
+    except ValueError as exc:
+        raise ValueError(f"Invalid datetime format: {value}") from exc
+
+
+def run_single_export(
+    start_str: str,
+    end_str: str,
+    camera_id: int,
+    event_name: str,
+    output_dir: str,
+    nas_save: bool = False,
+    nas_upload: bool = False,
+    nas_dstdir: str = NAS_EXPORT_DSTDIR,
+):
+    start_unix = parse_time_to_unix(start_str)
+    end_unix = parse_time_to_unix(end_str)
+    if end_unix <= start_unix:
+        raise ValueError("End time must be later than start time.")
+
+    session_id = None
+    try:
+        session_id, _ = get_sid()
+        print("Logged in.")
+        if nas_save:
+            print(f"Creating NAS-side clip export in dstdir='{nas_dstdir}'...")
+            result, check_payload = start_nas_save_export(
+                session_id,
+                camera_id=camera_id,
+                start_time=start_unix,
+                end_time=end_unix,
+                event_name=event_name,
+                nas_dstdir=nas_dstdir,
+            )
+            if not result.get("success"):
+                check_result = (check_payload or {}).get("data", {}).get("result")
+                if check_result == 2:
+                    raise RuntimeError(
+                        "Failed to start NAS save export: no Surveillance Station event in selected time range "
+                        "(CheckAvailableExport result=2). "
+                        "Save-mode exports events only on this NAS build. "
+                        "Use export mode without --nas-save for arbitrary time ranges."
+                    )
+                raise RuntimeError(f"Failed to start NAS save export: {result}")
+            nas_ref = f"{nas_dstdir.rstrip('/')}/{event_name}"
+            print(f"NAS export task started successfully: {result}")
+            if CLIP_PUBLIC_BASE:
+                print(f"Share link prefix: {CLIP_PUBLIC_BASE.rstrip('/')}/{event_name}")
+            return nas_ref
+        else:
+            print("Creating clip...")
+            result = start_range_export(
+                session_id,
+                camera_id=camera_id,
+                start_time=start_unix,
+                end_time=end_unix,
+                event_name=event_name,
+            )
+
+            if not result.get("success"):
+                raise RuntimeError(f"Failed to start export: {result}")
+
+            dlid = result.get("data", {}).get("dlid")
+            if dlid is None:
+                raise RuntimeError(f"Export started but no dlid returned: {result}")
+
+            file_ext, progress_result = wait_for_export(session_id, dlid)
+            if not file_ext:
+                raise RuntimeError(f"Error while waiting for export: {progress_result}")
+
+            if nas_upload:
+                os.makedirs(CLIP_TMP_DIR, exist_ok=True)
+                tmp_path = os.path.join(CLIP_TMP_DIR, f"{event_name}.{file_ext}")
+                try:
+                    download_export(session_id, dlid, event_name, file_ext, CLIP_TMP_DIR)
+                    # Log out SS session before FileStation login to avoid duplicate-session conflicts
+                    logout_sid(session_id)
+                    session_id = None
+                    print(f"Downloaded to temp: {tmp_path}, uploading to NAS:{nas_dstdir} ...")
+                    nas_ref = upload_to_filestation(tmp_path, nas_dstdir)
+                    print(f"Success! Clip uploaded to NAS: {nas_ref}")
+                    if CLIP_PUBLIC_BASE:
+                        print(f"Share link: {CLIP_PUBLIC_BASE.rstrip('/')}/{os.path.basename(nas_ref)}")
+                    return nas_ref
+                finally:
+                    try:
+                        os.remove(tmp_path)
+                    except Exception:
+                        pass
+            else:
+                output_path = download_export(
+                    session_id,
+                    dlid,
+                    event_name,
+                    file_ext,
+                    output_dir,
+                )
+                public_ref = build_public_clip_ref(output_path, output_dir)
+                print(f"Success! Clip saved to {output_path}")
+                if public_ref != output_path:
+                    print(f"Share link: {public_ref}")
+                return output_path
+    finally:
+        if session_id:
+            logout_sid(session_id)
+
+
+def update_remote_status(job_id: int, status: str, output_path: str | None = None, error_message: str | None = None):
+    payload: dict[str, Any] = {
+        "job_id": job_id,
+        "status": status,
+    }
+    if output_path is not None:
+        payload["output_path"] = output_path
+    if error_message is not None:
+        payload["error_message"] = error_message
+
+    headers = {
+        "Content-Type": "application/json",
+        "X-Shared-Password": AAREDB_SHARED_PASSWORD or "",
+    }
+    r = requests.post(
+        DISPATCHER_STATUS_URL,
+        data=json.dumps(payload),
+        headers=headers,
+        verify=DISPATCHER_VERIFY_SSL,
+        cert=(DISPATCHER_CLIENT_CERT, DISPATCHER_CLIENT_KEY) if DISPATCHER_CLIENT_CERT else None,
+        timeout=60,
+    )
+    r.raise_for_status()
+    return r.json()
+
+
+def build_event_name(job: dict[str, Any]) -> str:
+    sample_id = job.get("sample_id", "s")
+    camera_id = job.get("camera_id", "c")
+    job_id = job.get("job_id", "j")
+    start_time = str(job.get("start_time", "")).replace(":", "-").replace(" ", "_")
+    return f"clip_sample{sample_id}_cam{camera_id}_job{job_id}_{start_time}"
+
+
+def process_job(job: dict[str, Any], output_dir: str, nas_upload: bool = False, nas_dstdir: str = NAS_EXPORT_DSTDIR):
+    job_id = int(job["job_id"])
+    camera_id = int(job["camera_id"])
+    start_time = str(job["start_time"])
+    end_time = str(job["end_time"])
+    event_name = build_event_name(job)
+
+    print(f"[JOB {job_id}] Claiming job (camera={camera_id}, start={start_time}, end={end_time})")
+    update_remote_status(job_id, "submitted")
+
+    try:
+        output_path = run_single_export(
+            start_str=start_time,
+            end_str=end_time,
+            camera_id=camera_id,
+            event_name=event_name,
+            output_dir=output_dir,
+            nas_upload=nas_upload,
+            nas_dstdir=nas_dstdir,
+        )
+        public_ref = build_public_clip_ref(output_path, output_dir) if not nas_upload else output_path
+        print(f"[JOB {job_id}] Done -> {output_path}")
+        update_remote_status(job_id, "done", output_path=public_ref, error_message=None)
+    except Exception as exc:
+        err = str(exc)
+        print(f"[JOB {job_id}] Failed: {err}")
+        update_remote_status(job_id, "failed", error_message=err)
+
+
+def worker_loop(output_dir: str, nas_upload: bool = False, nas_dstdir: str = NAS_EXPORT_DSTDIR):
+    print(f"Connecting to clip jobs websocket: {DISPATCHER_WS_URL}")
+    active_jobs: set[int] = set()
+
+    while True:
+        ws = None
+        try:
+            ws = create_connection(
+                DISPATCHER_WS_URL,
+                header=[f"X-Shared-Password: {AAREDB_SHARED_PASSWORD or ''}"],
+                timeout=30,
+                sslopt=(
+                    {
+                        "ca_certs": DISPATCHER_CA_CERT,
+                        "certfile": DISPATCHER_CLIENT_CERT,
+                        "keyfile": DISPATCHER_CLIENT_KEY,
+                        "cert_reqs": 2,  # ssl.CERT_REQUIRED
+                    }
+                    if DISPATCHER_CA_CERT
+                    else (None if DISPATCHER_VERIFY_SSL else {"cert_reqs": 0})
+                ),
+            )
+            print("Websocket connected. Waiting for clip jobs...")
+            while True:
+                raw = ws.recv()
+                if not raw:
+                    continue
+                try:
+                    payload = json.loads(raw)
+                except json.JSONDecodeError:
+                    print(f"Skipping non-JSON websocket payload: {raw!r}")
+                    continue
+
+                if not isinstance(payload, list):
+                    continue
+
+                to_do_jobs = [
+                    j
+                    for j in payload
+                    if isinstance(j, dict)
+                    and str(j.get("status", "")).lower() == "to_do"
+                    and j.get("job_id") is not None
+                ]
+
+                for job in sorted(to_do_jobs, key=lambda x: int(x.get("job_id", 0))):
+                    job_id = int(job["job_id"])
+                    if job_id in active_jobs:
+                        continue
+                    active_jobs.add(job_id)
+                    try:
+                        process_job(job, output_dir=output_dir, nas_upload=nas_upload, nas_dstdir=nas_dstdir)
+                    finally:
+                        active_jobs.discard(job_id)
+        except Exception as exc:
+            print(f"Worker websocket error: {exc}. Reconnecting in 5 seconds...")
+            time.sleep(5)
+        finally:
+            try:
+                if ws:
+                    ws.close()
+            except Exception:
+                pass
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Surveillance Station clip tool: export once or run as websocket worker."
+    )
+    sub = parser.add_subparsers(dest="mode")
+
+    export_parser = sub.add_parser("export", help="Export one clip from explicit start/end")
+    export_parser.add_argument("start", help='Start time "YYYY-MM-DD HH:MM:SS" or ISO8601')
+    export_parser.add_argument("end", help='End time "YYYY-MM-DD HH:MM:SS" or ISO8601')
+    export_parser.add_argument("--camera-id", type=int, default=1)
+    export_parser.add_argument("--event-name", default="Event_101")
+    export_parser.add_argument("--output-dir", default=DEFAULT_OUTPUT_DIR)
+    export_parser.add_argument("--list-cameras", action="store_true")
+    export_parser.add_argument(
+        "--nas-save",
+        action="store_true",
+        help="Ask NAS to save clip locally on NAS instead of downloading to this machine",
+    )
+    export_parser.add_argument(
+        "--nas-dstdir",
+        default=NAS_EXPORT_DSTDIR,
+        help="Destination directory for NAS-side save export (default from NAS_EXPORT_DSTDIR/EventClips)",
+    )
+
+    worker_parser = sub.add_parser("worker", help="Connect websocket and process clip jobs")
+    worker_parser.add_argument("--output-dir", default=DEFAULT_OUTPUT_DIR)
+    worker_parser.add_argument(
+        "--nas-upload",
+        action="store_true",
+        help="Download clip to temp then upload to NAS via FileStation (works for any time range)",
+    )
+    worker_parser.add_argument(
+        "--nas-dstdir",
+        default=NAS_EXPORT_DSTDIR,
+        help="Destination folder on NAS for --nas-upload",
+    )
+
+    # Backward compatibility with previous invocation style:
+    # python bl-clip-manager.py "<start>" "<end>"
+    parser.add_argument("legacy_start", nargs="?", help=argparse.SUPPRESS)
+    parser.add_argument("legacy_end", nargs="?", help=argparse.SUPPRESS)
+    parser.add_argument("--camera-id", type=int, default=1, help=argparse.SUPPRESS)
+    parser.add_argument("--event-name", default="Event_101", help=argparse.SUPPRESS)
+    parser.add_argument("--output-dir", default=DEFAULT_OUTPUT_DIR, help=argparse.SUPPRESS)
+    parser.add_argument("--list-cameras", action="store_true", help=argparse.SUPPRESS)
+    parser.add_argument("--nas-save", action="store_true", help=argparse.SUPPRESS)
+    parser.add_argument("--nas-dstdir", default=NAS_EXPORT_DSTDIR, help=argparse.SUPPRESS)
+
+    return parser.parse_args()
+
+
+def main():
+    ensure_env_ready()
+    args = parse_args()
+
+    # Legacy mode support
+    if args.mode is None and args.legacy_start and args.legacy_end:
+        if args.list_cameras:
+            sid, _ = get_sid()
+            try:
+                camera_result = list_cameras(sid)
+                if camera_result.get("success"):
+                    cams = camera_result.get("data", {}).get("cameras", [])
+                    print("Available cameras:")
+                    for cam in cams:
+                        print(f"- id={cam.get('id')} name={cam.get('name')}")
+                else:
+                    print(f"Could not list cameras: {camera_result}")
+            finally:
+                logout_sid(sid)
+            return
+        run_single_export(
+            start_str=args.legacy_start,
+            end_str=args.legacy_end,
+            camera_id=args.camera_id,
+            event_name=args.event_name,
+            output_dir=args.output_dir,
+            nas_save=args.nas_save,
+            nas_dstdir=args.nas_dstdir,
+        )
+        return
+
+    if args.mode == "export":
+        if args.list_cameras:
+            sid, _ = get_sid()
+            try:
+                camera_result = list_cameras(sid)
+                if camera_result.get("success"):
+                    cams = camera_result.get("data", {}).get("cameras", [])
+                    print("Available cameras:")
+                    for cam in cams:
+                        print(f"- id={cam.get('id')} name={cam.get('name')}")
+                else:
+                    print(f"Could not list cameras: {camera_result}")
+            finally:
+                logout_sid(sid)
+            return
+
+        run_single_export(
+            start_str=args.start,
+            end_str=args.end,
+            camera_id=args.camera_id,
+            event_name=args.event_name,
+            output_dir=args.output_dir,
+            nas_save=args.nas_save,
+            nas_dstdir=args.nas_dstdir,
+        )
+        return
+
+    if args.mode == "worker":
+        worker_loop(output_dir=args.output_dir, nas_upload=args.nas_upload, nas_dstdir=args.nas_dstdir)
+        return
+
+    raise SystemExit(
+        "Usage:\n"
+        "  python bl-clip-manager.py worker\n"
+        "  python bl-clip-manager.py export \"YYYY-MM-DD HH:MM:SS\" \"YYYY-MM-DD HH:MM:SS\"\n"
+        "  python bl-clip-manager.py \"YYYY-MM-DD HH:MM:SS\" \"YYYY-MM-DD HH:MM:SS\"  (legacy)"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/core/__init__.py b/src/core/__init__.py
new file mode 100644
index 0000000..237c993
--- /dev/null
+++ b/src/core/__init__.py
@@ -0,0 +1,12 @@
+"""Core functionality for AareLC ML Studio."""
+from .zmq_client import ZMQStreamClient
+from .inference_client import InferenceClient
+from .db_client import DatabaseClient
+from .image_processor import ImageProcessor
+
+__all__ = [
+    'ZMQStreamClient',
+    'InferenceClient',
+    'DatabaseClient',
+    'ImageProcessor'
+]
diff --git a/src/core/cv_logic.py b/src/core/cv_logic.py
new file mode 100644
index 0000000..4792c15
--- /dev/null
+++ b/src/core/cv_logic.py
@@ -0,0 +1,71 @@
+"""Computer vision logic utilities for edge detection and polygon refinement.
+
+This module is maintained for backward compatibility.
+For new code, please use src.core.image_processor.ImageProcessor instead.
+"""
+import cv2
+import numpy as np
+
+
+def get_refined_polygon(self, roi, p):
+    """
+    Robust contour detection optimized for homogeneous backgrounds.
+    Uses Adaptive Thresholding + Hole Filling.
+    """
+    # 1. Preprocessing
+    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+    
+    # Median blur is superior for preserving edges on homogeneous backgrounds
+    blurred = cv2.medianBlur(gray, 5)
+    
+    # 2. Adaptive Thresholding
+    # block_size must be odd and > 1. We use morph_kernel slider to control this.
+    block_size = max(3, p["morph"] if p["morph"] % 2 != 0 else p["morph"] + 1)
+    
+    # Gaussian adaptive thresholding is very resilient to homogeneous background noise
+    thresh = cv2.adaptiveThreshold(
+        blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
+        cv2.THRESH_BINARY_INV, block_size, 2
+    )
+
+    # 3. Clean up noise
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+    opened = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
+        
+    # 4. Fill Holes (Crucial for loops)
+    # We find all contours and draw them filled on a mask
+    cnts, _ = cv2.findContours(opened, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+    mask = np.zeros_like(opened)
+    for c in cnts:
+        cv2.drawContours(mask, [c], -1, 255, -1) # -1 thickness = fill
+
+    # 5. Final Contour Extraction
+    # Now we take the largest filled blob
+    final_cnts, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+    if not final_cnts:
+        return np.array([]), opened
+
+    # Select largest blob by area
+    cnt = max(final_cnts, key=cv2.contourArea)
+        
+    # 6. Smooth the polygon based on the user's precision slider
+    epsilon = p["eps"] * cv2.arcLength(cnt, True)
+    approx = cv2.approxPolyDP(cnt, epsilon, True)
+        
+    return approx, mask
+
+
+def measure_focus(raw_frame):
+    """
+    Measure focus quality using Laplacian variance.
+
+    DEPRECATED: Use ImageProcessor.measure_focus() instead.
+
+    Args:
+        raw_frame: Grayscale image
+
+    Returns:
+        Focus score (higher = better focus)
+    """
+    return cv2.Laplacian(raw_frame, cv2.CV_64F).var()
diff --git a/src/core/db_client.py b/src/core/db_client.py
new file mode 100644
index 0000000..c01ff39
--- /dev/null
+++ b/src/core/db_client.py
@@ -0,0 +1,296 @@
+"""Client for database server communication."""
+import requests
+import numpy as np
+import cv2
+from pathlib import Path
+
+
+class DatabaseClient:
+    """Handles communication with database server for image retrieval."""
+
+    def __init__(self, server_url, shared_password="", download_base=None):
+        """
+        Initialize database client.
+
+        Args:
+            server_url: Base URL of database server (e.g., "http://localhost:8000")
+            shared_password: Password for shared auth
+            download_base: Base URL for image downloads (e.g., "http://localhost:8002/backend")
+        """
+        self.server_url = server_url
+        self.shared_password = shared_password
+        self.download_base = download_base or "http://localhost:8002/backend"
+
+    def _get_headers(self):
+        return {"x-shared-password": self.shared_password}
+
+    def fetch_next_raw_image(self, timeout=5):
+        # 1. Get metadata from Dispatcher (via Tunnel 8001 or Direct 443)
+        # Assuming you've set self.server_url to the dispatcher base
+        meta_url = f"{self.server_url.rstrip('/')}/dispatcher/protected_router/annotations/raw_next"
+        r = requests.get(meta_url, headers=self._get_headers(), verify=False, timeout=timeout)
+        
+        if r.status_code != 200:
+            raise ValueError(f"No more images available (status {r.status_code})")
+
+        data = r.json()
+        image_obj = data.get('image', {})
+        
+        # 2. Construct the correct download URL
+        filepath = image_obj.get('filepath')
+        if not filepath:
+            raise ValueError("Metadata received but no filepath found.")
+
+        # Use the configurable download_base
+        download_url = f"{self.download_base.rstrip('/')}/{filepath.lstrip('/')}"
+        
+        print(f"DEBUG: Fetching pixels from: {download_url}")
+        img_res = requests.get(download_url, timeout=timeout, verify=False)
+
+        if img_res.status_code != 200:
+            # Fallback debug
+            raise ValueError(f"Could not retrieve image from {download_url} (status {img_res.status_code})")
+
+        # 3. Process the content
+        content = img_res.content
+
+        # Use OpenCV to decode
+        nparr = np.frombuffer(content, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+        if img is None:
+            # If standard decode fails, check if it's a RAW Bayer file (which your system uses)
+            # A 4MP Bayer file is exactly 4194304 bytes or 4177936 bytes
+            if len(content) in [4194304, 4177936]:
+                try:
+                    # Attempt raw Bayer recovery (2048x2048 or 2048x2040)
+                    h = 2048
+                    w = len(content) // h
+                    raw = np.frombuffer(content, np.uint8).reshape((h, w))
+                    img = cv2.cvtColor(raw, cv2.COLOR_BAYER_RG2BGR)
+                    print(f"DEBUG: Successfully decoded as raw Bayer {w}x{h}")
+                except:
+                    pass
+
+        if img is None:
+            raise ValueError(f"Failed to decode image data. size={len(content)} bytes. Header: {content[:4].hex()}")
+
+        # Return both image and metadata
+        return img, data
+
+    def set_server_url(self, url):
+        """Update server URL."""
+        self.server_url = url
+
+    def get_server_url(self):
+        """Get current server URL."""
+        return self.server_url
+
+    def set_shared_password(self, password):
+        """Update the shared password."""
+        self.shared_password = password
+
+    def set_download_base(self, url):
+        """Update the image download base URL."""
+        self.download_base = url
+
+    def save_annotation(self, image_id, segments, username=None, timeout=10):
+        """
+        Save YOLO segmentation annotations to database.
+
+        Args:
+            image_id: ID of the image in the database
+            segments: List of dicts with 'class_id' and 'points' (normalized YOLO format)
+            username: Optional username to attribute the annotation to
+            timeout: Request timeout in seconds
+
+        Returns:
+            Response dict with status and annotation_id
+        """
+        save_url = f"{self.server_url.rstrip('/')}/dispatcher/protected_router/annotations/{image_id}"
+
+        payload = {
+            "segments": segments,
+            "username": username
+        }
+
+        print(f"DEBUG: Saving annotation to: {save_url}")
+        print(f"DEBUG: Payload: {len(segments)} segments")
+
+        response = requests.post(
+            save_url,
+            json=payload,
+            headers=self._get_headers(),
+            verify=False,
+            timeout=timeout
+        )
+
+        if response.status_code != 200:
+            raise ValueError(f"Failed to save annotation: {response.status_code} - {response.text}")
+
+        return response.json()
+
+    def skip_image(self, image_id, timeout=5):
+        """
+        Mark an image as skipped in the database.
+
+        Args:
+            image_id: ID of the image to skip
+            timeout: Request timeout in seconds
+
+        Returns:
+            Response dict with status
+        """
+        skip_url = f"{self.server_url.rstrip('/')}/dispatcher/protected_router/annotations/{image_id}/skip"
+
+        print(f"DEBUG: Skipping image: {skip_url}")
+
+        response = requests.post(
+            skip_url,
+            headers=self._get_headers(),
+            verify=False,
+            timeout=timeout
+        )
+
+        if response.status_code != 200:
+            raise ValueError(f"Failed to skip image: {response.status_code} - {response.text}")
+
+        return response.json()
+
+
+    def sync_local_annotations(
+        self,
+        since=None,
+        until=None,
+        include_unannotated=False,
+        dry_run=False,
+        limit=5000,
+        timeout=120,
+    ):
+        """Retrieve DB sync items and write missing files locally on this machine."""
+        sync_url = f"{self.server_url.rstrip('/')}/dispatcher/protected_router/annotations/sync/local"
+
+        params = {
+            "include_unannotated": str(bool(include_unannotated)).lower(),
+            "limit": int(limit),
+        }
+        if since:
+            params["since"] = since
+        if until:
+            params["until"] = until
+
+        print(f"DEBUG: Syncing local files from: {sync_url}")
+        print(f"DEBUG: Sync params: {params}")
+
+        response = requests.post(
+            sync_url,
+            params=params,
+            headers=self._get_headers(),
+            verify=False,
+            timeout=timeout,
+        )
+
+        if response.status_code != 200:
+            raise ValueError(
+                f"Failed to sync local files: {response.status_code} - {response.text}"
+            )
+        data = response.json()
+        items = data.get("items", [])
+
+        images_dir = Path("data") / "img_sets" / "seg_training" / "images"
+        labels_dir = Path("data") / "img_sets" / "seg_training" / "labels"
+        if not dry_run:
+            images_dir.mkdir(parents=True, exist_ok=True)
+            labels_dir.mkdir(parents=True, exist_ok=True)
+
+        stats = {
+            "scanned": len(items),
+            "image_copied": 0,
+            "image_existing": 0,
+            "image_failed": 0,
+            "image_skipped_no_label": 0,
+            "label_copied": 0,
+            "label_existing": 0,
+            "label_failed": 0,
+            "dry_run": bool(dry_run),
+            "since": since,
+            "until": until,
+        }
+        failures = []
+
+        for item in items:
+            image_id = item.get("image_id")
+            image_url = item.get("image_url")
+            segments = item.get("segments") or []
+
+            if image_id is None:
+                stats["image_failed"] += 1
+                failures.append("missing image_id in sync item")
+                continue
+
+            image_path = images_dir / f"img_{int(image_id)}.jpg"
+            label_path = labels_dir / f"img_{int(image_id)}.txt"
+
+            lines = []
+            for seg in segments:
+                points = seg.get("points")
+                if (
+                    not isinstance(points, list)
+                    or len(points) < 6
+                    or len(points) % 2 != 0
+                ):
+                    continue
+                class_id = int(seg.get("class_id", 0))
+                lines.append(f"{class_id} " + " ".join(f"{float(p):.6f}" for p in points))
+
+            label_ready = False
+            if label_path.exists():
+                stats["label_existing"] += 1
+                label_ready = True
+            elif not lines:
+                stats["label_failed"] += 1
+                stats["image_skipped_no_label"] += 1
+                failures.append(
+                    f"image_id={image_id}: no valid segmentation points for label"
+                )
+                continue
+            else:
+                try:
+                    if not dry_run:
+                        label_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+                    stats["label_copied"] += 1
+                    label_ready = True
+                except Exception as exc:
+                    stats["label_failed"] += 1
+                    failures.append(
+                        f"image_id={image_id}: failed writing {label_path} ({exc})"
+                    )
+                    continue
+
+            if not label_ready:
+                stats["image_skipped_no_label"] += 1
+                continue
+
+            if image_path.exists():
+                stats["image_existing"] += 1
+            else:
+                try:
+                    img_res = requests.get(
+                        image_url,
+                        timeout=min(timeout, 30),
+                        verify=False,
+                    )
+                    if img_res.status_code != 200 or not img_res.content:
+                        raise ValueError(
+                            f"image download failed status={img_res.status_code}"
+                        )
+                    if not dry_run:
+                        image_path.write_bytes(img_res.content)
+                    stats["image_copied"] += 1
+                except Exception as exc:
+                    stats["image_failed"] += 1
+                    failures.append(
+                        f"image_id={image_id}: failed downloading {image_url} ({exc})"
+                    )
+
+        return {"status": "ok", "stats": stats, "failures": failures[:100]}
diff --git a/src/core/image_processor.py b/src/core/image_processor.py
new file mode 100644
index 0000000..2a36e2b
--- /dev/null
+++ b/src/core/image_processor.py
@@ -0,0 +1,586 @@
+import cv2
+import numpy as np
+
+
+class ImageProcessor:
+    def __init__(self):
+        self.edge_map = None
+        self.edge_map_shape = None
+        self.roi_offset = (0, 0) # Store (x1, y1) of the box
+
+    def compute_edge_map(self, image, box=None):
+        """
+        Compute edge map, optionally restricted to a specific box.
+        """
+        if image is None:
+            return None
+
+        # 1. Handle ROI crop if box is provided
+        if box:
+            x1, y1, x2, y2 = int(box['x1']), int(box['y1']), int(box['x2']), int(box['y2'])
+            # Add small padding to capture edges right at the boundary
+            h, w = image.shape[:2]
+            p = 10
+            x1, y1 = max(0, x1-p), max(0, y1-p)
+            x2, y2 = min(w, x2+p), min(h, y2+p)
+            
+            working_img = image[y1:y2, x1:x2]
+            self.roi_offset = (x1, y1)
+        else:
+            working_img = image
+            self.roi_offset = (0, 0)
+
+        # 2. Convert and process local ROI
+        gray = cv2.cvtColor(working_img, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+
+        adaptive = cv2.adaptiveThreshold(
+            blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
+            cv2.THRESH_BINARY_INV, 11, 2
+        )
+        canny = cv2.Canny(blurred, 30, 100)
+        edge_map = cv2.bitwise_or(adaptive, canny)
+        
+        kernel = np.ones((3,3), np.uint8)
+        self.edge_map = cv2.dilate(edge_map, kernel, iterations=1)
+        self.edge_map_shape = working_img.shape[:2]
+        return self.edge_map
+
+    def find_nearest_edge(self, x, y, radius=20):
+        """
+        Find nearest edge within the stored ROI.
+        """
+        if self.edge_map is None:
+            return x, y
+
+        # Translate global mouse coords to ROI local coords
+        lx, ly = int(x - self.roi_offset[0]), int(y - self.roi_offset[1])
+        h, w = self.edge_map.shape
+
+        # Bounds check
+        x0, x1 = max(0, lx - radius), min(w - 1, lx + radius)
+        y0, y1 = max(0, ly - radius), min(h - 1, ly + radius)
+
+        region = self.edge_map[y0:y1+1, x0:x1+1]
+        edge_points = np.argwhere(region > 128)
+
+        if len(edge_points) == 0:
+            return x, y
+
+        # Find closest point in local ROI space
+        edge_points_local = edge_points + [y0, x0]
+        distances = np.linalg.norm(edge_points_local - [ly, lx], axis=1)
+        closest_idx = np.argmin(distances)
+        closest_y, closest_x = edge_points_local[closest_idx]
+
+        # Translate back to global image coords
+        return int(closest_x + self.roi_offset[0]), int(closest_y + self.roi_offset[1])
+
+    @staticmethod
+    def get_refined_polygon_watershed(roi, p, manual_mask=None):
+        """
+        Watershed-based segmentation using manual strokes as markers.
+        More intuitive than GrabCut: background strokes act as barriers.
+        """
+        h, w = roi.shape[:2]
+        print(f"  Watershed: roi_shape={roi.shape}")
+        if h < 10 or w < 10:
+            print(f"  Watershed: ROI too small ({h}x{w})")
+            return np.array([]), np.zeros((h, w), dtype=np.uint8)
+
+        # Convert to grayscale for processing
+        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+
+        # Compute gradient magnitude (edges)
+        sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
+        sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
+        gradient = np.sqrt(sobelx**2 + sobely**2)
+        gradient = cv2.normalize(gradient, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
+
+        # Create markers for watershed
+        markers = np.zeros((h, w), dtype=np.int32)
+
+        if manual_mask is not None:
+            # Match sizes
+            if manual_mask.shape[:2] != (h, w):
+                print(f"  Watershed: Resizing manual_mask from {manual_mask.shape[:2]} to ({h}, {w})")
+                manual_mask = cv2.resize(manual_mask, (w, h), interpolation=cv2.INTER_NEAREST)
+
+            has_bg = np.any(manual_mask == 0)
+            has_fg = np.any(manual_mask == 1)
+            print(f"  Watershed: has_bg={has_bg}, has_fg={has_fg}")
+
+            if has_bg or has_fg:
+                # Background strokes = marker 1 (will be excluded)
+                if has_bg:
+                    markers[manual_mask == 0] = 1
+
+                # Foreground strokes = marker 2 (will be included)
+                if has_fg:
+                    fg_pixels = np.sum(manual_mask == 1)
+                    print(f"  Watershed: Using {fg_pixels} foreground pixels")
+                    markers[manual_mask == 1] = 2
+
+                    # If only foreground painted, automatically add background border
+                    if not has_bg:
+                        print(f"  Watershed: No background painted - using border as background hint")
+                        border_width = max(3, min(w, h) // 30)
+                        markers[:border_width, :] = 1
+                        markers[-border_width:, :] = 1
+                        markers[:, :border_width] = 1
+                        markers[:, -border_width:] = 1
+                else:
+                    # No foreground marked - use center region
+                    center_x, center_y = w // 2, h // 2
+                    radius = min(w, h) // 6
+                    y_indices, x_indices = np.ogrid[:h, :w]
+                    center_mask = (x_indices - center_x)**2 + (y_indices - center_y)**2 <= radius**2
+                    markers[center_mask] = 2
+                    print(f"  Watershed: Using center circle as foreground hint")
+
+                # Apply watershed
+                print(f"  Watershed: Running watershed algorithm...")
+                markers = cv2.watershed(roi, markers)
+                print(f"  Watershed: Watershed complete, unique markers: {np.unique(markers)}")
+
+                # Extract foreground (marker == 2)
+                binary_mask = np.where(markers == 2, 255, 0).astype(np.uint8)
+                fg_area = np.sum(binary_mask > 0)
+                print(f"  Watershed: Binary mask has {fg_area} foreground pixels")
+            else:
+                # No manual strokes - use automatic thresholding
+                binary_mask = ImageProcessor._get_auto_mask(roi, p)
+        else:
+            # No manual mask - use automatic thresholding
+            binary_mask = ImageProcessor._get_auto_mask(roi, p)
+
+        # Morphological cleanup
+        k = max(1, p.get("morph", 7))
+        k = k if k % 2 != 0 else k + 1
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+        binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_CLOSE, kernel)
+        binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_OPEN, kernel)
+
+        # Find contours
+        contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        print(f"  Watershed: Found {len(contours)} contours")
+        if not contours:
+            print(f"  Watershed: No contours found!")
+            return np.array([]), binary_mask
+
+        # Get largest contour
+        cnt = max(contours, key=cv2.contourArea)
+        area = cv2.contourArea(cnt)
+        print(f"  Watershed: Largest contour area: {area}")
+
+        # Smooth polygon
+        epsilon = p.get("eps", 0.01) * cv2.arcLength(cnt, True)
+        approx = cv2.approxPolyDP(cnt, epsilon, True)
+        print(f"  Watershed: Polygon has {len(approx)} points")
+
+        return approx, binary_mask
+
+    @staticmethod
+    def _get_auto_mask(roi, p):
+        """Automatic mask generation without manual strokes."""
+        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+
+        # Adaptive thresholding
+        adaptive = cv2.adaptiveThreshold(
+            blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+            cv2.THRESH_BINARY_INV, 11, 2
+        )
+
+        # Canny edges
+        canny = cv2.Canny(blurred, 30, p.get("high", 150))
+
+        # Combine
+        binary_mask = cv2.bitwise_or(adaptive, canny)
+
+        return binary_mask
+
+    @staticmethod
+    def get_refined_polygon(roi, p, manual_mask=None):
+        """
+        Refinement using Interactive GrabCut.
+        Optimized for homogeneous backgrounds by using the ROI border as a background seed.
+        """
+        h, w = roi.shape[:2]
+        if h < 10 or w < 10: return np.array([]), np.zeros((h, w), dtype=np.uint8)
+
+        pad_w, pad_h = int(w * 0.05), int(h * 0.05)
+        rect = (pad_w, pad_h, w - 2 * pad_w, h - 2 * pad_h)
+
+        # Initialize mask with proper GrabCut values
+        if manual_mask is not None:
+            # Match sizes to prevent IndexErrors
+            if manual_mask.shape[:2] != roi.shape[:2]:
+                manual_mask = cv2.resize(manual_mask, (w, h), interpolation=cv2.INTER_NEAREST)
+
+            # Check what strokes we have
+            has_bg = np.any(manual_mask == 0)
+            has_fg = np.any(manual_mask == 1)
+
+            # GrabCut needs BOTH foreground and background samples
+            if has_bg and not has_fg:
+                # User only painted background - use rectangle center as foreground hint
+                print("  GrabCut mode: Background only - adding center as foreground hint")
+                mask = np.full((h, w), cv2.GC_PR_BGD, dtype=np.uint8)  # Start with probable background
+                mask[manual_mask == 0] = cv2.GC_BGD  # Hard background from user
+                # Set center region as probable foreground
+                center_x, center_y = w // 2, h // 2
+                radius = min(w, h) // 4
+                y_indices, x_indices = np.ogrid[:h, :w]
+                center_mask = (x_indices - center_x)**2 + (y_indices - center_y)**2 <= radius**2
+                mask[center_mask] = cv2.GC_PR_FGD
+                print(f"    BG pixels: {np.sum(mask == cv2.GC_BGD)}, FG hint pixels: {np.sum(mask == cv2.GC_PR_FGD)}")
+            elif has_fg and not has_bg:
+                # User only painted foreground - use border as background hint
+                mask = np.full((h, w), cv2.GC_PR_FGD, dtype=np.uint8)  # Start with probable foreground
+                mask[manual_mask == 1] = cv2.GC_FGD  # Hard foreground from user
+                # Set border as probable background
+                border_width = max(2, min(w, h) // 20)
+                mask[:border_width, :] = cv2.GC_PR_BGD
+                mask[-border_width:, :] = cv2.GC_PR_BGD
+                mask[:, :border_width] = cv2.GC_PR_BGD
+                mask[:, -border_width:] = cv2.GC_PR_BGD
+            elif has_bg and has_fg:
+                # User painted both - use their constraints directly
+                mask = np.full((h, w), cv2.GC_PR_FGD, dtype=np.uint8)
+                mask[manual_mask == 0] = cv2.GC_BGD
+                mask[manual_mask == 1] = cv2.GC_FGD
+            else:
+                # No strokes at all - fall back to rectangle mode
+                mask = np.zeros((h, w), np.uint8)
+                mode = cv2.GC_INIT_WITH_RECT
+                iters = max(1, min(2, int(p.get("high", 150) / 75)))
+                bgdModel = np.zeros((1, 65), np.float64)
+                fgdModel = np.zeros((1, 65), np.float64)
+                try:
+                    cv2.grabCut(roi, mask, rect, bgdModel, fgdModel, iters, mode)
+                except Exception as e:
+                    print(f"GrabCut failed: {e}")
+                    return np.array([]), np.zeros((h, w), dtype=np.uint8)
+                binary_mask = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8') * 255
+                k = max(1, p.get("morph", 7))
+                k = k if k % 2 != 0 else k + 1
+                kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+                binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_CLOSE, kernel)
+                contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                if not contours:
+                    return np.array([]), binary_mask
+                cnt = max(contours, key=cv2.contourArea)
+                epsilon = p.get("eps", 0.01) * cv2.arcLength(cnt, True)
+                approx = cv2.approxPolyDP(cnt, epsilon, True)
+                return approx, binary_mask
+
+            mode = cv2.GC_INIT_WITH_MASK
+            # More iterations when we have manual guidance
+            iters = 5
+        else:
+            mask = np.zeros((h, w), np.uint8)
+            mode = cv2.GC_INIT_WITH_RECT
+            iters = max(1, min(2, int(p.get("high", 150) / 75)))
+
+        bgdModel = np.zeros((1, 65), np.float64)
+        fgdModel = np.zeros((1, 65), np.float64)
+
+        try:
+            print(f"  Running GrabCut: mode={mode}, iters={iters}, roi_shape={roi.shape}")
+            cv2.grabCut(roi, mask, rect, bgdModel, fgdModel, iters, mode)
+            print(f"  GrabCut succeeded")
+        except Exception as e:
+            print(f"  GrabCut failed: {e}")
+            return np.array([]), np.zeros((h, w), dtype=np.uint8)
+
+        # Convert to binary mask
+        binary_mask = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8') * 255
+
+        # 4. Clean up the mask using the morphology slider
+        k = max(1, p.get("morph", 7))
+        k = k if k % 2 != 0 else k + 1
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+        binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_CLOSE, kernel)
+
+        # 5. Find the largest contour
+        contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            return np.array([]), binary_mask
+
+        cnt = max(contours, key=cv2.contourArea)
+
+        # 6. Smooth the polygon based on the epsilon slider
+        epsilon = p.get("eps", 0.01) * cv2.arcLength(cnt, True)
+        approx = cv2.approxPolyDP(cnt, epsilon, True)
+
+        return approx, binary_mask
+
+    def draw_detections(self, image, detections, class_params, class_colors, active_class=None,
+                        show_debug_edges=False, refine_mode=False, selected_idx=-1, selected_island_idx=0,
+                        selected_island_kind="outer", hidden_classes=None,
+                        segmentation_method='watershed', annotation_line_width=2,
+                        show_boxes=True, show_segments=True):
+        """
+        Draw detection boxes and polygons.
+        Supports manual editing by checking for existing 'poly' in detections.
+        """
+        draw_img = image.copy()
+        hidden_classes = hidden_classes or set()
+
+        # Show global edge map overlay if requested
+        if show_debug_edges and self.edge_map is not None:
+            # Create colored edge overlay
+            edge_colored = cv2.cvtColor(self.edge_map, cv2.COLOR_GRAY2BGR)
+            edge_colored[:, :, 0] = 0  # Remove blue channel
+            edge_colored[:, :, 1] = 0  # Remove green channel
+            # Keep only red channel for edges
+            draw_img = cv2.addWeighted(draw_img, 0.7, edge_colored, 0.3, 0)
+
+        for i, det in enumerate(detections):
+            is_selected = (i == selected_idx)
+            try:
+                box = det.get('box', det)
+                x1, y1, x2, y2 = int(box.get('x1', 0)), int(box.get('y1', 0)), int(box.get('x2', 0)), int(
+                    box.get('y2', 0))
+                label = det.get('name', det.get('label', 'unknown'))
+            except:
+                continue
+
+            # Skip hidden classes
+            if label in hidden_classes:
+                continue
+
+            color = class_colors.get(label, (255, 0, 255))
+            params = class_params.get(label) or next(iter(class_params.values()), {})
+            base_thickness = max(1, int(annotation_line_width))
+            thickness = base_thickness + 1 if is_selected else base_thickness
+
+            # 1. Draw Bounding Box
+            if show_boxes:
+                cv2.rectangle(draw_img, (x1, y1), (x2, y2), color, thickness)
+                cv2.putText(draw_img, f"{label} {'(EDITING)' if is_selected else ''}",
+                            (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
+
+            if is_selected and show_boxes:
+                # DRAW RESIZE HANDLES (Small white squares at all 4 corners)
+                handle_size = max(3, base_thickness + 2)
+                corners = [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]
+                for (cx, cy) in corners:
+                    cv2.rectangle(draw_img, (cx - handle_size, cy - handle_size),
+                                  (cx + handle_size, cy + handle_size), (255, 255, 255), -1)
+                    cv2.rectangle(draw_img, (cx - handle_size, cy - handle_size),
+                                  (cx + handle_size, cy + handle_size), color, max(1, base_thickness - 1))
+
+            # 2. Handle Polygon (Manual or Refined)
+            polygons = []
+            if isinstance(det.get('polygons'), list):
+                for poly_item in det.get('polygons'):
+                    arr = np.array(poly_item, dtype=np.float32).reshape(-1, 1, 2)
+                    if len(arr) >= 3:
+                        polygons.append(arr)
+            poly = det.get('poly')
+            if not polygons and poly is not None and len(poly) >= 3:
+                polygons = [np.array(poly, dtype=np.float32).reshape(-1, 1, 2)]
+            holes = []
+            if isinstance(det.get('holes'), list):
+                for hole_item in det.get('holes'):
+                    arr = np.array(hole_item, dtype=np.float32).reshape(-1, 1, 2)
+                    if len(arr) >= 3:
+                        holes.append(arr)
+            manual_mask = det.get('manual_mask') # Retrieve user brush strokes stored in the detection
+
+            # Regenerate polygon if: missing, empty, or needs refinement
+            suppress_auto_polygon = bool(det.get('suppress_auto_polygon', False))
+            should_regenerate = show_segments and refine_mode and y2 > y1 and x2 > x1 and (
+                len(polygons) == 0
+            ) and not suppress_auto_polygon
+
+            if should_regenerate:
+                print(f"  Should regenerate: method={segmentation_method}, manual_mask={'present' if manual_mask is not None else 'absent'}")
+                roi = image[y1:y2, x1:x2]
+                if roi.size > 0:
+                    # Use selected segmentation method
+                    if segmentation_method == 'watershed':
+                        generated_poly, debug_mask = self.get_refined_polygon_watershed(roi, params, manual_mask)
+                    elif segmentation_method == 'grabcut':
+                        generated_poly, debug_mask = self.get_refined_polygon(roi, params, manual_mask)
+                    elif segmentation_method == 'smart':
+                        print(f"  Calling smart ridge-snapping segmentation...")
+                        generated_poly, debug_mask = self.get_refined_polygon_livewire(roi, params, manual_mask)
+                    else:
+                        generated_poly, debug_mask = self.get_refined_polygon_watershed(roi, params, manual_mask)
+
+                    if len(generated_poly) > 0:
+                        # CRITICAL FIX: Ensure the polygon is stored back in the detection
+                        generated_poly = generated_poly.astype(np.float32)
+                        det['poly'] = generated_poly
+                        det['polygons'] = [generated_poly]
+                        polygons = [generated_poly]
+                        print(f"  Stored smart polygon with {len(generated_poly)} points in detection")
+                        
+                        b, g, r = color
+                        colored_mask = cv2.merge([
+                            np.where(debug_mask > 0, b, 0).astype(np.uint8),
+                            np.where(debug_mask > 0, g, 0).astype(np.uint8),
+                            np.where(debug_mask > 0, r, 0).astype(np.uint8)
+                        ])
+                        draw_img[y1:y2, x1:x2] = cv2.addWeighted(draw_img[y1:y2, x1:x2], 0.6, colored_mask, 0.4, 0)
+
+            # 3. Draw the Polygon and interactive points
+            if show_segments and polygons:
+                for poly_idx, poly_item in enumerate(polygons):
+                    pts = (poly_item.reshape((-1, 1, 2)) + [x1, y1]).astype(np.int32)
+                    cv2.polylines(draw_img, [pts], isClosed=True, color=color, thickness=thickness)
+
+                    if is_selected:
+                        vertex_fill_radius = max(2, base_thickness + 1)
+                        vertex_border_radius = vertex_fill_radius + 1
+                        active_idx = max(0, min(int(selected_island_idx), len(polygons) - 1))
+                        if selected_island_kind == "outer" and poly_idx == active_idx:
+                            for p_idx, p in enumerate(pts):
+                                cv2.circle(draw_img, tuple(p[0]), vertex_fill_radius, (255, 255, 255), -1)
+                                cv2.circle(draw_img, tuple(p[0]), vertex_border_radius, color, max(1, base_thickness - 1))
+                            cv2.putText(
+                                draw_img,
+                                f"outer {active_idx + 1}/{len(polygons)}",
+                                (x1, y2 + 16),
+                                cv2.FONT_HERSHEY_SIMPLEX,
+                                0.45,
+                                color,
+                                1
+                            )
+
+            if show_segments and holes:
+                hole_color = (0, 165, 255)
+                for hole_idx, hole_item in enumerate(holes):
+                    pts = (hole_item.reshape((-1, 1, 2)) + [x1, y1]).astype(np.int32)
+                    cv2.polylines(draw_img, [pts], isClosed=True, color=hole_color, thickness=max(1, thickness - 1))
+                    if is_selected:
+                        vertex_fill_radius = max(2, base_thickness + 1)
+                        vertex_border_radius = vertex_fill_radius + 1
+                        active_idx = max(0, min(int(selected_island_idx), len(holes) - 1))
+                        if selected_island_kind == "hole" and hole_idx == active_idx:
+                            for p in pts:
+                                cv2.circle(draw_img, tuple(p[0]), vertex_fill_radius, (255, 255, 255), -1)
+                                cv2.circle(draw_img, tuple(p[0]), vertex_border_radius, hole_color, max(1, base_thickness - 1))
+                            cv2.putText(
+                                draw_img,
+                                f"hole {active_idx + 1}/{len(holes)}",
+                                (x1, y2 + 32),
+                                cv2.FONT_HERSHEY_SIMPLEX,
+                                0.45,
+                                hole_color,
+                                1
+                            )
+
+            # 4. Visualize Brush Strokes with colored overlay
+            if show_segments and is_selected and manual_mask is not None:
+                # Get the actual ROI region from the image
+                roi_region = draw_img[y1:y2, x1:x2]
+                roi_h, roi_w = roi_region.shape[:2]
+
+                # Ensure manual_mask matches ROI size
+                if manual_mask.shape[:2] != (roi_h, roi_w):
+                    manual_mask_resized = cv2.resize(manual_mask, (roi_w, roi_h), interpolation=cv2.INTER_NEAREST)
+                else:
+                    manual_mask_resized = manual_mask
+
+                # Create colored overlay for brush strokes
+                overlay = np.zeros((roi_h, roi_w, 3), dtype=np.uint8)
+
+                # Background strokes in RED (semi-transparent)
+                bg_mask = manual_mask_resized == 0
+                overlay[bg_mask] = [0, 0, 255]  # Red in BGR
+
+                # Foreground strokes in GREEN (semi-transparent)
+                fg_mask = manual_mask_resized == 1
+                overlay[fg_mask] = [0, 255, 0]  # Green in BGR
+
+                # Only blend where we have brush strokes (not the "probable" areas)
+                mask_combined = (bg_mask | fg_mask).astype(np.uint8)
+                if mask_combined.sum() > 0:
+                    # Create alpha channel effect
+                    alpha = 0.4  # 40% opacity for the overlay
+                    blended = cv2.addWeighted(roi_region, 1 - alpha, overlay, alpha, 0)
+                    # Only apply blending where strokes exist
+                    roi_region[mask_combined > 0] = blended[mask_combined > 0]
+                    draw_img[y1:y2, x1:x2] = roi_region
+
+        return draw_img
+
+    @staticmethod
+    def measure_focus(raw_frame):
+        return cv2.Laplacian(raw_frame, cv2.CV_64F).var()
+
+    @staticmethod
+    def draw_fps_overlay(image, fps_in, fps_out, fps_pred=0.0):
+        """Draw input/output stream FPS overlay on an annotated frame."""
+        if image is None:
+            return image
+        text = f"FPS in (pserv): {fps_in:.1f} | out (unified): {fps_out:.1f} | pred: {fps_pred:.1f}"
+        cv2.putText(
+            image,
+            text,
+            (10, 88),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.6,
+            (0, 255, 255),
+            2
+        )
+        return image
+
+    @staticmethod
+    def get_refined_polygon_livewire(roi, p, manual_mask=None):
+        """
+        Smart selection using Gradient ridge-snapping.
+        """
+        h, w = roi.shape[:2]
+        if h < 10 or w < 10: return np.array([]), np.zeros((h, w), dtype=np.uint8)
+
+        # 1. Edge Detection
+        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+        
+        # High sensitivity Canny
+        low_t = max(10, p.get("high", 150) // 3)
+        edges = cv2.Canny(blurred, low_t, p.get("high", 150))
+        
+        # 2. Use Manual Mask to block areas
+        if manual_mask is not None:
+            if manual_mask.shape[:2] != (h, w):
+                manual_mask = cv2.resize(manual_mask, (w, h), interpolation=cv2.INTER_NEAREST)
+            # Remove edges in background-painted areas
+            edges[manual_mask == 0] = 0
+
+        # 3. Create a 'snapping' surface using distance transform
+        # This creates a 'valley' at every edge
+        inv_edges = cv2.bitwise_not(edges)
+        dist_trans = cv2.distanceTransform(inv_edges, cv2.DIST_L2, 5)
+        
+        # 4. Generate a base mask (Watershed is actually a great seed for this)
+        _, seed_mask = ImageProcessor.get_refined_polygon_watershed(roi, p, manual_mask)
+        
+        # 5. Shrink/Expand seed to snap to the distance transform ridge
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+        snapped_mask = cv2.morphologyEx(seed_mask, cv2.MORPH_CLOSE, kernel)
+
+        # 6. Find final contours on the snapped mask
+        # Use CHAIN_APPROX_SIMPLE for reliable point extraction
+        contours, _ = cv2.findContours(snapped_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            print("  Smart: No contours found in snapped_mask")
+            return np.array([]), snapped_mask
+
+        cnt = max(contours, key=cv2.contourArea)
+        
+        # 7. Apply smoothing and ensure float32 format for storage
+        eps_factor = max(0.0001, p.get("eps", 0.005))
+        epsilon = eps_factor * cv2.arcLength(cnt, True)
+        approx = cv2.approxPolyDP(cnt, epsilon, True)
+
+        # Ensure the polygon is a float32 array in (N, 1, 2) shape
+        poly_out = approx.astype(np.float32)
+        print(f"  Smart: Generated polygon with {len(poly_out)} points")
+
+        return poly_out, snapped_mask
diff --git a/src/core/inference_client.py b/src/core/inference_client.py
new file mode 100644
index 0000000..5ef14d8
--- /dev/null
+++ b/src/core/inference_client.py
@@ -0,0 +1,162 @@
+"""Client for ML inference server communication."""
+import requests
+import cv2
+import json
+import numpy as np
+
+
+class InferenceClient:
+    """Handles communication with ML inference server."""
+
+    def __init__(self, server_url):
+        """
+        Initialize inference client.
+
+        Args:
+            server_url: Base URL of inference server (e.g., "http://mx-ml.psi.ch:8002")
+        """
+        self.server_url = server_url
+
+    def predict(self, image, timeout=10):
+        """
+        Send image to server for prediction.
+
+        Args:
+            image: OpenCV image (BGR format)
+            timeout: Request timeout in seconds
+
+        Returns:
+            List of detection results, or None on error
+
+        Raises:
+            requests.RequestException: On connection errors
+            ValueError: On invalid response
+        """
+        _, buf = cv2.imencode('.jpg', image)
+        url = f"{self.server_url}/predict/"
+        files = {'file': ('image.jpg', buf.tobytes(), 'image/jpeg')}
+
+        response = requests.post(url, files=files, timeout=timeout)
+
+        if response.status_code == 200:
+            data = response.json()
+            return data.get('results', [])
+        else:
+            raise ValueError(f"Server returned status {response.status_code}")
+
+    def segment_anything(self, image, x, y, timeout=15):
+        """
+        Request a high-precision mask from the SAM backend.
+        """
+        _, buf = cv2.imencode('.jpg', image)
+        # Use trailing slash to match the server's /sam/ route exactly
+        url = f"{self.server_url.rstrip('/')}/sam/"
+        params = {"point_x": int(x), "point_y": int(y)}
+        files = {'file': ('image.jpg', buf.tobytes(), 'image/jpeg')}
+
+        response = requests.post(url, files=files, params=params, timeout=timeout)
+        
+        if response.status_code == 200:
+            return response.json()
+        else:
+            raise ValueError(f"SAM error: {response.status_code} - {response.text}")
+
+    def segment_anything_box(self, image, x1, y1, x2, y2, timeout=15):
+        """
+        Request a high-precision mask from the SAM backend using a bounding box prompt.
+        """
+        _, buf = cv2.imencode('.jpg', image)
+        url = f"{self.server_url.rstrip('/')}/sam_box/"
+        params = {
+            "x1": int(x1),
+            "y1": int(y1),
+            "x2": int(x2),
+            "y2": int(y2)
+        }
+        files = {'file': ('image.jpg', buf.tobytes(), 'image/jpeg')}
+
+        response = requests.post(url, files=files, params=params, timeout=timeout)
+
+        if response.status_code == 200:
+            return response.json()
+        else:
+            raise ValueError(f"SAM Box error: {response.status_code} - {response.text}")
+
+    def segment_multi_box(self, image, boxes, timeout=30):
+        """
+        Request masks for multiple boxes in one call.
+        boxes: List of [x1, y1, x2, y2]
+        """
+        _, buf = cv2.imencode('.jpg', image)
+        url = f"{self.server_url.rstrip('/')}/sam_multi_box/"
+        params = {"boxes_json": json.dumps(boxes)}
+        files = {'file': ('image.jpg', buf.tobytes(), 'image/jpeg')}
+
+        response = requests.post(url, files=files, params=params, timeout=timeout)
+        if response.status_code == 200:
+            return response.json().get('results', [])
+        else:
+            raise ValueError(f"SAM Multi Box error: {response.status_code}")
+
+    def predict_depth(self, image, timeout=20, colorize=True, depth_model=None, depth_max_side=0):
+        """
+        Request depth map for an image.
+
+        Returns:
+            OpenCV image array decoded from PNG response.
+        """
+        _, buf = cv2.imencode('.jpg', image)
+        url = f"{self.server_url.rstrip('/')}/depth/"
+        params = {"colorize": "true" if colorize else "false"}
+        if depth_model:
+            params["depth_model"] = str(depth_model)
+        if isinstance(depth_max_side, int) and depth_max_side > 0:
+            params["depth_max_side"] = str(depth_max_side)
+        files = {'file': ('image.jpg', buf.tobytes(), 'image/jpeg')}
+
+        response = requests.post(url, files=files, params=params, timeout=timeout)
+        if response.status_code != 200:
+            raise ValueError(f"Depth error: {response.status_code} - {response.text}")
+        if "image/png" not in response.headers.get("content-type", ""):
+            try:
+                err = response.json().get("error", response.text)
+            except Exception:
+                err = response.text
+            raise ValueError(f"Depth error: {err}")
+
+        depth_png = cv2.imdecode(np.frombuffer(response.content, np.uint8), cv2.IMREAD_UNCHANGED)
+        if depth_png is None:
+            raise ValueError("Depth response decode failed.")
+        return depth_png
+
+    def send_training_data(self, image, metadata, timeout=10):
+        """
+        Send annotated image to training pipeline.
+
+        Args:
+            image: OpenCV image (BGR format)
+            metadata: Dict containing params and detections
+            timeout: Request timeout in seconds
+
+        Returns:
+            Response object
+
+        Raises:
+            requests.RequestException: On connection errors
+        """
+        _, buf = cv2.imencode('.jpg', image)
+        url = f"{self.server_url}/train/"
+        files = {'file': ('img.jpg', buf.tobytes(), 'image/jpeg')}
+        data = {"metadata": json.dumps(metadata)}
+
+        response = requests.post(url, files=files, data=data, timeout=timeout)
+        response.raise_for_status()
+        return response
+
+    def set_server_url(self, url):
+        """Update server URL."""
+        self.server_url = url
+
+    def get_server_url(self):
+        """Get current server URL."""
+        return self.server_url
diff --git a/src/core/zmq_client.py b/src/core/zmq_client.py
new file mode 100644
index 0000000..5b3859f
--- /dev/null
+++ b/src/core/zmq_client.py
@@ -0,0 +1,201 @@
+"""ZMQ client for receiving live image streams with detection results."""
+import zmq
+import json
+import numpy as np
+import cv2
+import threading
+
+
+class ZMQStreamClient:
+    """Handles ZMQ subscription for live detector stream."""
+
+    def __init__(self, zmq_url, on_frame_callback, on_focus_callback=None, use_cuda=True):
+        """
+        Initialize ZMQ stream client.
+
+        Args:
+            zmq_url: ZMQ server URL (e.g., "tcp://localhost:9091")
+            on_frame_callback: Callback function(frame, detections, metadata) called on new frame
+            on_focus_callback: Optional callback function(focus_score) for focus updates
+            use_cuda: Use OpenCV CUDA path when available on this client
+        """
+        self.zmq_url = zmq_url
+        self.on_frame_callback = on_frame_callback
+        self.on_focus_callback = on_focus_callback
+        self.focus_enabled = on_focus_callback is not None
+        self.use_cuda = bool(use_cuda)
+        self.cuda_enabled = False
+        self._cuda_lap_filter = None
+        self.streaming = False
+        self.worker_thread = None
+
+        if self.use_cuda:
+            try:
+                if hasattr(cv2, 'cuda') and cv2.cuda.getCudaEnabledDeviceCount() > 0:
+                    # Create reusable Laplacian filter for focus score on GPU.
+                    self._cuda_lap_filter = cv2.cuda.createLaplacianFilter(cv2.CV_8UC1, cv2.CV_32F, 3)
+                    self.cuda_enabled = True
+            except Exception:
+                self.cuda_enabled = False
+                self._cuda_lap_filter = None
+        self._cuda_fallback_logged = False
+
+    def start(self):
+        """Start streaming in background thread."""
+        if self.streaming:
+            return
+        self.streaming = True
+        self.worker_thread = threading.Thread(target=self._worker, daemon=True)
+        self.worker_thread.start()
+
+    def stop(self):
+        """Stop streaming."""
+        self.streaming = False
+        if self.worker_thread:
+            self.worker_thread.join(timeout=1.0)
+
+    def is_streaming(self):
+        """Check if currently streaming."""
+        return self.streaming
+
+    def enable_focus(self):
+        """Enable focus-score computation and callbacks."""
+        self.focus_enabled = True
+
+    def disable_focus(self):
+        """Disable focus-score computation and callbacks."""
+        self.focus_enabled = False
+
+    def _worker(self):
+        """Background worker that listens to ZMQ stream."""
+        print(f"DEBUG: Starting Unified ZMQ worker ({self.zmq_url}) cuda={self.cuda_enabled}", flush=True)
+        if self.use_cuda:
+            if self.cuda_enabled:
+                print("INFO: OpenCV CUDA path enabled for focus scoring", flush=True)
+            else:
+                print("INFO: OpenCV CUDA requested but unavailable, using CPU path", flush=True)
+        ctx = zmq.Context()
+        sub = ctx.socket(zmq.SUB)
+        sub.connect(self.zmq_url)
+        sub.setsockopt_string(zmq.SUBSCRIBE, "")
+        sub.setsockopt(zmq.RCVBUF, 128 * 1024 * 1024)
+        poller = zmq.Poller()
+        poller.register(sub, zmq.POLLIN)
+
+        while self.streaming:
+            try:
+                socks = dict(poller.poll(10))
+                if sub in socks:
+                    # Drain queue to get latest message
+                    latest_msg = None
+                    while True:
+                        try:
+                            latest_msg = sub.recv_multipart(zmq.NOBLOCK)
+                        except zmq.Again:
+                            break
+
+                    if latest_msg:
+                        self._process_message(latest_msg)
+            except Exception as e:
+                print(f"ZMQ Error: {e}")
+                break
+
+        sub.close()
+        ctx.term()
+
+    def _process_message(self, msg_parts):
+        """Process received ZMQ multipart message."""
+        try:
+            # Last part is image data, rest might contain header
+            img_data = msg_parts[-1]
+            header = None
+
+            # Try to find JSON header in message parts
+            for part in msg_parts[:-1]:
+                try:
+                    if not part:
+                        continue
+                    header = json.loads(part.decode('utf-8'))
+                    if isinstance(header, dict) and 'shape' in header:
+                        break
+                    header = None
+                except:
+                    continue
+
+            if not header:
+                return
+
+            # Extract detections
+            detections = header.get('boxes', [])
+            if isinstance(detections, list):
+                for det in detections:
+                    if not isinstance(det, dict):
+                        continue
+                    poly = det.get('poly')
+                    if poly is None:
+                        continue
+                    try:
+                        poly_arr = np.asarray(poly, dtype=np.float32)
+                        if poly_arr.ndim == 2 and poly_arr.shape[1] == 2 and poly_arr.shape[0] >= 3:
+                            det['poly'] = poly_arr.reshape(-1, 1, 2)
+                        else:
+                            det.pop('poly', None)
+                    except Exception:
+                        det.pop('poly', None)
+
+            # Decode image payload (raw BGR or Bayer from unified stream server).
+            pixel_format = str(header.get('pixel_format', 'BGR')).upper()
+            shape = header.get('shape', [])
+            if pixel_format == 'BAYER':
+                if len(shape) != 2:
+                    return
+                h, w = int(shape[0]), int(shape[1])
+                raw = np.frombuffer(img_data, np.uint8).reshape((h, w))
+                bayer_pattern = str(header.get('bayer_pattern', 'RGGB')).upper()
+                demosaic_map = {
+                    'RGGB': cv2.COLOR_BAYER_RG2BGR,
+                    'GBRG': cv2.COLOR_BAYER_GB2BGR,
+                    'GRBG': cv2.COLOR_BAYER_GR2BGR,
+                    'BGGR': cv2.COLOR_BAYER_BG2BGR,
+                }
+                demosaic_code = demosaic_map.get(bayer_pattern)
+                if demosaic_code is None:
+                    return
+                img = cv2.cvtColor(raw, demosaic_code)
+            else:
+                if len(shape) != 3:
+                    return
+                h, w, c = int(shape[0]), int(shape[1]), int(shape[2])
+                if c != 3:
+                    return
+                img = np.frombuffer(img_data, np.uint8).reshape((h, w, c))
+
+            # Calculate focus score
+            if self.focus_enabled and self.on_focus_callback:
+                if self.cuda_enabled:
+                    try:
+                        gpu_img = cv2.cuda_GpuMat()
+                        gpu_img.upload(img)
+                        gpu_gray = cv2.cuda.cvtColor(gpu_img, cv2.COLOR_BGR2GRAY)
+                        gpu_lap = self._cuda_lap_filter.apply(gpu_gray)
+                        lap = gpu_lap.download()
+                        focus_score = float(lap.var())
+                    except Exception:
+                        if not self._cuda_fallback_logged:
+                            print("WARN: CUDA focus path failed at runtime, falling back to CPU", flush=True)
+                            self._cuda_fallback_logged = True
+                        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+                        focus_score = cv2.Laplacian(gray, cv2.CV_64F).var()
+                else:
+                    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+                    focus_score = cv2.Laplacian(gray, cv2.CV_64F).var()
+                self.on_focus_callback(focus_score)
+
+            # Call frame callback. Keep backward compatibility with 2-arg callbacks.
+            try:
+                self.on_frame_callback(img, detections, header)
+            except TypeError:
+                self.on_frame_callback(img, detections)
+
+        except Exception as e:
+            print(f"Error processing ZMQ message: {e}")
diff --git a/src/gui/__init__.py b/src/gui/__init__.py
new file mode 100644
index 0000000..3f18f40
--- /dev/null
+++ b/src/gui/__init__.py
@@ -0,0 +1,5 @@
+"""GUI components for AareLC ML Studio."""
+from .canvas_panel import MainCanvas
+from .control_panel import ControlPanel
+
+__all__ = ['MainCanvas', 'ControlPanel']
diff --git a/src/gui/canvas_interaction_mixin.py b/src/gui/canvas_interaction_mixin.py
new file mode 100644
index 0000000..fe70fc3
--- /dev/null
+++ b/src/gui/canvas_interaction_mixin.py
@@ -0,0 +1,697 @@
+"""Canvas drawing and interaction mixin for the GUI."""
+
+import math
+
+import cv2
+import numpy as np
+
+
+class CanvasInteractionMixin:
+    """Canvas rendering and user interaction logic."""
+
+    @staticmethod
+    def _clamp_point(pt, width, height):
+        if pt is None:
+            return None
+        x = max(0, min(width - 1, int(pt[0])))
+        y = max(0, min(height - 1, int(pt[1])))
+        return x, y
+
+    def _extract_target_from_metadata(self, metadata):
+        if not isinstance(metadata, dict):
+            return None, None
+        point = metadata.get('target_point')
+        if not isinstance(point, dict):
+            return None, None
+        x = point.get('x')
+        y = point.get('y')
+        if not isinstance(x, (int, float)) or not isinstance(y, (int, float)):
+            return None, None
+        label = str(point.get('source', 'target_point'))
+        return (int(round(x)), int(round(y))), label
+
+    @staticmethod
+    def _draw_target_cross(image, point, label=None):
+        if image is None or point is None:
+            return image
+        h, w = image.shape[:2]
+        cx = max(0, min(w - 1, int(point[0])))
+        cy = max(0, min(h - 1, int(point[1])))
+        arm = 14
+        color = (255, 0, 255)  # magenta
+        cv2.line(image, (cx - arm, cy), (cx + arm, cy), color, 2)
+        cv2.line(image, (cx, cy - arm), (cx, cy + arm), color, 2)
+        cv2.circle(image, (cx, cy), 3, color, -1)
+        if label:
+            cv2.putText(
+                image,
+                f"Target: {label} ({cx},{cy})",
+                (max(8, cx + 10), max(20, cy - 10)),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                color,
+                1
+            )
+        return image
+
+    def _toggle_analysis_grid(self, event=None):
+        self.analysis_state['show_grid'] = not self.analysis_state['show_grid']
+        self.redraw_current_frame()
+        self.status_var.set(f"Measure grid: {'ON' if self.analysis_state['show_grid'] else 'OFF'}")
+
+    def _toggle_analysis_crosshair(self, event=None):
+        self.analysis_state['show_crosshair'] = not self.analysis_state['show_crosshair']
+        self.redraw_current_frame()
+        self.status_var.set(f"Measure crosshair: {'ON' if self.analysis_state['show_crosshair'] else 'OFF'}")
+
+    def _clear_measurements(self, event=None):
+        self.analysis_state['line_start'] = None
+        self.analysis_state['line_end'] = None
+        self.analysis_state['line_dragging'] = False
+        self.analysis_state['roi_start'] = None
+        self.analysis_state['roi_end'] = None
+        self.analysis_state['roi_dragging'] = False
+        self.redraw_current_frame()
+        self.status_var.set("Measurement overlays cleared")
+
+    def _draw_analysis_overlay(self, annotated, source_frame):
+        h, w = annotated.shape[:2]
+        state = self.analysis_state
+
+        if state['show_grid']:
+            step = max(40, min(w, h) // 12)
+            for gx in range(step, w, step):
+                cv2.line(annotated, (gx, 0), (gx, h - 1), (70, 70, 70), 1)
+            for gy in range(step, h, step):
+                cv2.line(annotated, (0, gy), (w - 1, gy), (70, 70, 70), 1)
+
+        cursor = self._clamp_point(state['cursor'], w, h)
+        if cursor is not None and state['show_crosshair']:
+            cx, cy = cursor
+            cv2.line(annotated, (cx, 0), (cx, h - 1), (0, 180, 255), 1)
+            cv2.line(annotated, (0, cy), (w - 1, cy), (0, 180, 255), 1)
+
+        if cursor is not None:
+            cx, cy = cursor
+            b, g, r = source_frame[cy, cx]
+            cv2.putText(
+                annotated,
+                f"Cursor ({cx},{cy}) BGR=({int(b)},{int(g)},{int(r)})",
+                (10, h - 14),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                (50, 220, 255),
+                1
+            )
+
+        line_start = self._clamp_point(state['line_start'], w, h)
+        line_end = self._clamp_point(state['line_end'], w, h)
+        if line_start is not None and line_end is not None:
+            cv2.line(annotated, line_start, line_end, (0, 255, 255), 2)
+            cv2.circle(annotated, line_start, 4, (0, 255, 255), -1)
+            cv2.circle(annotated, line_end, 4, (0, 255, 255), -1)
+            dx = line_end[0] - line_start[0]
+            dy = line_end[1] - line_start[1]
+            dist = math.hypot(dx, dy)
+            angle_deg = math.degrees(math.atan2(dy, dx))
+            cv2.putText(
+                annotated,
+                f"Line: {dist:.1f}px  dx={dx} dy={dy}  angle={angle_deg:.1f}deg",
+                (10, 44),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.55,
+                (0, 255, 255),
+                2
+            )
+
+        roi_start = self._clamp_point(state['roi_start'], w, h)
+        roi_end = self._clamp_point(state['roi_end'], w, h)
+        if roi_start is not None and roi_end is not None:
+            x1, x2 = sorted((roi_start[0], roi_end[0]))
+            y1, y2 = sorted((roi_start[1], roi_end[1]))
+            cv2.rectangle(annotated, (x1, y1), (x2, y2), (255, 255, 0), 2)
+            roi_w = x2 - x1
+            roi_h = y2 - y1
+            roi_area = roi_w * roi_h
+
+            roi_label = f"ROI: {roi_w}x{roi_h}px area={roi_area}px2"
+            if roi_w > 1 and roi_h > 1:
+                roi = source_frame[y1:y2, x1:x2]
+                if roi.size > 0:
+                    mean_bgr = roi.reshape(-1, 3).mean(axis=0)
+                    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+                    roi_label += (
+                        f" gray={float(gray.mean()):.1f}+/-{float(gray.std()):.1f}"
+                        f" meanBGR=({mean_bgr[0]:.1f},{mean_bgr[1]:.1f},{mean_bgr[2]:.1f})"
+                    )
+
+            cv2.putText(
+                annotated,
+                roi_label,
+                (10, 66),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                (255, 255, 0),
+                1
+            )
+
+        if self.tk_vars['active_tool'].get() == "measure":
+            cv2.putText(
+                annotated,
+                "Measure tool: L-drag line | R-drag ROI | g grid | c crosshair | x clear",
+                (10, h - 34),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                (220, 220, 220),
+                1
+            )
+        return annotated
+
+    def redraw_current_frame(self):
+        """Redraw current frame with annotations."""
+        if self.current_raw_frame is not None:
+            self.update_canvas(self.current_raw_frame)
+
+    def update_canvas(self, frame, metadata=None):
+        """Update canvas with annotated frame."""
+        if frame is None:
+            return
+
+        is_streaming = self.zmq_client is not None and self.zmq_client.is_streaming()
+
+        # Pass the selected index to the processor for highlighting
+        selected_idx = getattr(self.canvas, 'selected_obj_idx', -1)
+
+        # Get hidden classes from control panel
+        hidden_classes = {
+            cls_name for cls_name, var in self.ctrl_panel.visibility_vars.items()
+            if not var.get()
+        }
+
+        # Compute edge map if not already done or image changed
+        if self.image_processor.edge_map is None or self.image_processor.edge_map_shape != frame.shape[:2]:
+            self.image_processor.compute_edge_map(frame)
+
+        annotated = self.image_processor.draw_detections(
+            frame,
+            self.last_results,
+            self.class_params,
+            self.class_colors,
+            active_class=self.tk_vars['active_class'].get(),
+            show_debug_edges=self.tk_vars['show_debug_edges'].get(),
+            refine_mode=not is_streaming,
+            selected_idx=selected_idx,
+            selected_island_idx=self.selected_island_idx,
+            selected_island_kind=self.selected_island_kind,
+            hidden_classes=hidden_classes,
+            segmentation_method=self.tk_vars['segmentation_method'].get(),
+            annotation_line_width=self.tk_vars['annotation_line_width'].get(),
+            show_boxes=self.tk_vars['show_boxes'].get(),
+            show_segments=self.tk_vars['show_segments'].get(),
+        )
+        if self.tk_vars['active_tool'].get() == "measure":
+            annotated = self._draw_analysis_overlay(annotated, frame)
+        if is_streaming:
+            annotated = self.image_processor.draw_fps_overlay(
+                annotated,
+                self.stream_fps_in,
+                self.stream_fps_out,
+                self.stream_fps_pred
+            )
+        point, label = self._extract_target_from_metadata(metadata if metadata is not None else self._last_stream_metadata)
+        annotated = self._draw_target_cross(annotated, point, label)
+        self.canvas.display_image(annotated)
+
+    def _get_hidden_classes(self):
+        """Get set of currently hidden classes."""
+        return {
+            cls_name for cls_name, var in self.ctrl_panel.visibility_vars.items()
+            if not var.get()
+        }
+
+    def _on_canvas_interaction(self, click_event=None, drag_event=None, right_click_event=None,
+                               double_click_event=None, move_event=None, right_drag_event=None,
+                               right_release_event=None):
+        if self.current_raw_frame is None:
+            return
+        img_h, img_w = self.current_raw_frame.shape[:2]
+        active_tool = self.tk_vars['active_tool'].get()
+        self.canvas.suspend_pan = (active_tool == "measure")
+
+        if active_tool != "measure" and (move_event is not None or right_drag_event is not None or right_release_event is not None):
+            return
+
+        if active_tool == "measure":
+            state = self.analysis_state
+
+            # End left-drag measurement when canvas signals release (no event payload)
+            if not any([click_event, drag_event, right_click_event, double_click_event, move_event, right_drag_event, right_release_event]):
+                state['line_dragging'] = False
+                return
+
+            active_event = move_event or click_event or right_click_event or double_click_event or right_drag_event
+            if drag_event:
+                active_event = drag_event[0]
+            if right_release_event:
+                active_event = right_release_event
+
+            if active_event is not None:
+                ix, iy = self.canvas.get_image_coords(active_event.x, active_event.y, img_w, img_h)
+                state['cursor'] = (ix, iy)
+
+            if click_event is not None:
+                ix, iy = self.canvas.get_image_coords(click_event.x, click_event.y, img_w, img_h)
+                state['line_start'] = (ix, iy)
+                state['line_end'] = (ix, iy)
+                state['line_dragging'] = True
+
+            if drag_event is not None and state['line_dragging']:
+                event, _, _ = drag_event
+                ix, iy = self.canvas.get_image_coords(event.x, event.y, img_w, img_h)
+                state['line_end'] = (ix, iy)
+
+            if right_click_event is not None:
+                ix, iy = self.canvas.get_image_coords(right_click_event.x, right_click_event.y, img_w, img_h)
+                state['roi_start'] = (ix, iy)
+                state['roi_end'] = (ix, iy)
+                state['roi_dragging'] = True
+
+            if right_drag_event is not None and state['roi_dragging']:
+                ix, iy = self.canvas.get_image_coords(right_drag_event.x, right_drag_event.y, img_w, img_h)
+                state['roi_end'] = (ix, iy)
+
+            if right_release_event is not None:
+                ix, iy = self.canvas.get_image_coords(right_release_event.x, right_release_event.y, img_w, img_h)
+                state['roi_end'] = (ix, iy)
+                state['roi_dragging'] = False
+
+            self.redraw_current_frame()
+            return
+
+        # --- SAM CLICK LOGIC ---
+        if click_event and active_tool == "sam":
+            ix, iy = self.canvas.get_image_coords(click_event.x, click_event.y, img_w, img_h)
+            self._run_sam_inference(ix, iy)
+            return
+
+        # Initialize variables
+        hidden_classes = self._get_hidden_classes()
+        brush_mode = self.tk_vars['brush_mode'].get()
+        event_obj = click_event or right_click_event or double_click_event or (drag_event[0] if drag_event else None)
+
+        is_opt = False
+        if event_obj:
+            is_opt = (event_obj.state & 0x0008) or (event_obj.state & 0x0010)
+
+        # --- BRUSH LOGIC (Handles both Click and Drag) ---
+        # Check BOTH old active_tool and new brush_mode checkbox
+        is_brush_active = (active_tool == "brush" or brush_mode or (right_click_event and is_opt))
+
+        if event_obj and is_brush_active:
+            ix, iy = self.canvas.get_image_coords(event_obj.x, event_obj.y, img_w, img_h)
+
+            if self.canvas.selected_obj_idx != -1:
+                det = self.last_results[self.canvas.selected_obj_idx]
+                box = det.get('box', det)
+                x1, y1 = int(box['x1']), int(box['y1'])
+                x2, y2 = int(box['x2']), int(box['y2'])
+
+                # Save history on first brush stroke
+                if not getattr(self, '_brush_started', False):
+                    self._save_history()
+                    self._brush_started = True
+
+                # Get brush parameters
+                brush_size = self.tk_vars['brush_size'].get()
+                brush_type = self.tk_vars['brush_type'].get()
+
+                # Convert to local coordinates
+                lx, ly = int(ix - x1), int(iy - y1)
+
+                if 0 <= lx < (x2 - x1) and 0 <= ly < (y2 - y1):
+                    # Create or get the manual mask
+                    if 'manual_mask' not in det:
+                        h, w = y2 - y1, x2 - x1
+                        det['manual_mask'] = np.full((h, w), 2, dtype=np.uint8)  # 2 = probable foreground
+
+                    # Paint with circular brush
+                    mask_value = 0 if brush_type == 'background' else 1  # 0=BGD, 1=FGD
+
+                    # Draw circular brush stroke on the mask
+                    cv2.circle(det['manual_mask'], (lx, ly), brush_size, mask_value, -1)
+
+                    # Don't auto-delete polygon - wait for manual regeneration
+                    # This prevents slowdown from constant regeneration during painting
+
+                    self.redraw_current_frame()
+                    self.status_var.set(f"Brush: {'Background' if brush_type == 'background' else 'Foreground'} region marked - Click 'Regenerate Edge' to update")
+                    return  # Stop other interactions while brushing
+
+        # --- REGULAR INTERACTION (Select Tool) ---
+        active_event = click_event or right_click_event or double_click_event
+        if active_event:
+            ix, iy = self.canvas.get_image_coords(active_event.x, active_event.y, img_w, img_h)
+
+            # --- DOUBLE CLICK: Add/Insert Point ---
+            if double_click_event and self.canvas.selected_obj_idx != -1:
+                self._save_history()
+                det = self.last_results[self.canvas.selected_obj_idx]
+                box = det.get('box', det)
+                poly = self._get_active_polygon(det)
+                lx, ly = ix - box.get('x1', 0), iy - box.get('y1', 0)
+
+                if poly is None or len(poly) < 2:
+                    new_poly = np.array([[[lx, ly]], [[lx + 50, ly]], [[lx + 50, ly + 50]]], dtype=np.float32)
+                    if self.selected_island_kind == "hole":
+                        holes = self._det_get_holes_relative(det)
+                        if holes:
+                            holes[self.selected_island_idx] = new_poly
+                            self._set_det_holes(det, holes)
+                        else:
+                            self._set_det_holes(det, [new_poly])
+                    else:
+                        polygons = self._det_get_polygons_relative(det)
+                        if polygons:
+                            polygons[self.selected_island_idx] = new_poly
+                            self._set_det_polygons(det, polygons)
+                        else:
+                            self._set_det_single_polygon(det, new_poly)
+                else:
+                    best_dist, insert_idx = float('inf'), len(poly)
+                    for i in range(len(poly)):
+                        p1, p2 = poly[i].reshape(-1), poly[(i + 1) % len(poly)].reshape(-1)
+                        d = np.linalg.norm(p1 - [lx, ly]) + np.linalg.norm(p2 - [lx, ly])
+                        if d < best_dist:
+                            best_dist, insert_idx = d, i + 1
+                    updated_poly = np.insert(poly, insert_idx, [[[lx, ly]]], axis=0)
+                    if self.selected_island_kind == "hole":
+                        holes = self._det_get_holes_relative(det)
+                        if holes:
+                            holes[self.selected_island_idx] = updated_poly
+                            self._set_det_holes(det, holes)
+                        else:
+                            self._set_det_holes(det, [updated_poly])
+                    else:
+                        polygons = self._det_get_polygons_relative(det)
+                        if polygons:
+                            polygons[self.selected_island_idx] = updated_poly
+                            self._set_det_polygons(det, polygons)
+                        else:
+                            self._set_det_single_polygon(det, updated_poly)
+                return self.redraw_current_frame()
+
+            # --- HIT TESTING: Select or Delete ---
+            hit_radius = 15 / self.canvas.zoom_level
+
+            # Hit Test: Box corners FIRST so resize handles win over nearby polygon points
+            for i, det in enumerate(self.last_results):
+                if det.get('name', det.get('label', 'unknown')) in hidden_classes:
+                    continue
+                box = det.get('box', det)
+                x1, y1 = box.get('x1', 0), box.get('y1', 0)
+                x2, y2 = box.get('x2', 0), box.get('y2', 0)
+                corners = {
+                    'box_resize_tl': (x1, y1),
+                    'box_resize_tr': (x2, y1),
+                    'box_resize_bl': (x1, y2),
+                    'box_resize_br': (x2, y2),
+                }
+                for mode, (cx, cy) in corners.items():
+                    if np.linalg.norm(np.array([cx, cy]) - [ix, iy]) < hit_radius:
+                        self.canvas.selected_obj_idx = i
+                        self.selected_island_idx = 0
+                        self.selected_island_kind = "outer"
+                        self.canvas.drag_mode = mode
+                        self.canvas.selected_point_idx = -1
+                        return self.redraw_current_frame()
+
+            # Hit Test: Points (after corners)
+            for i, det in enumerate(self.last_results):
+                if det.get('name', det.get('label', 'unknown')) in hidden_classes:
+                    continue
+                box = det.get('box', det)
+                rings = [("outer", self._det_get_polygons_relative(det)), ("hole", self._det_get_holes_relative(det))]
+                for ring_kind, ring_list in rings:
+                    for island_idx, poly in enumerate(ring_list):
+                        global_pts = poly.reshape(-1, 2) + [box.get('x1', 0), box.get('y1', 0)]
+                        for p_idx, pt in enumerate(global_pts):
+                            if np.linalg.norm(pt - [ix, iy]) < hit_radius:
+                                self.selected_island_kind = ring_kind
+                                self.selected_island_idx = island_idx
+                                if right_click_event:
+                                    self._save_history()
+                                    updated = np.delete(poly, p_idx, axis=0)
+                                    ring_list[island_idx] = updated
+                                    if ring_kind == "outer":
+                                        self._set_det_polygons(det, ring_list)
+                                    else:
+                                        self._set_det_holes(det, ring_list)
+                                    return self.redraw_current_frame()
+                                self.canvas.selected_obj_idx, self.canvas.selected_point_idx = i, p_idx
+                                self.canvas.drag_mode = 'point_move'
+                                return self.redraw_current_frame()
+
+            # Hit Test: polygon interior to select active island
+            for i, det in enumerate(self.last_results):
+                if det.get('name', det.get('label', 'unknown')) in hidden_classes:
+                    continue
+                box = det.get('box', det)
+                rings = [("hole", self._det_get_holes_relative(det)), ("outer", self._det_get_polygons_relative(det))]
+                for ring_kind, ring_list in rings:
+                    for island_idx, poly in enumerate(ring_list):
+                        global_pts = (poly.reshape(-1, 2) + [box.get('x1', 0), box.get('y1', 0)]).astype(np.float32)
+                        inside = cv2.pointPolygonTest(global_pts.reshape(-1, 1, 2), (float(ix), float(iy)), False)
+                        if inside >= 0:
+                            self.canvas.selected_obj_idx = i
+                            self.selected_island_kind = ring_kind
+                            self.selected_island_idx = island_idx
+                            self.canvas.selected_point_idx = -1
+                            self.canvas.drag_mode = 'box_move'
+                            return self.redraw_current_frame()
+
+            # Hit Test: Box body with overlap disambiguation (top-most first + click-to-cycle)
+            hit_indices = []
+            for i in range(len(self.last_results) - 1, -1, -1):
+                det = self.last_results[i]
+                if det.get('name', det.get('label', 'unknown')) in hidden_classes:
+                    continue
+                box = det.get('box', det)
+                x1, y1, x2, y2 = box.get('x1', 0), box.get('y1', 0), box.get('x2', 0), box.get('y2', 0)
+                if x1 < ix < x2 and y1 < iy < y2:
+                    hit_indices.append(i)
+
+            if hit_indices:
+                if right_click_event and not is_opt:
+                    target_idx = hit_indices[0]
+                    self._save_history()
+                    self.last_results.pop(target_idx)
+                    self.canvas.selected_obj_idx = -1
+                    self.selected_island_idx = 0
+                    self.selected_island_kind = "outer"
+                    return self.redraw_current_frame()
+
+                selected_idx = hit_indices[0]
+                if click_event and len(hit_indices) > 1:
+                    cycle_key = tuple(hit_indices)
+                    pick = self._last_overlap_pick
+                    same_spot = False
+                    if pick is not None:
+                        px, py = pick.get("point", (None, None))
+                        same_spot = px is not None and py is not None and abs(px - ix) <= 6 and abs(py - iy) <= 6
+                    if pick and pick.get("key") == cycle_key and same_spot:
+                        selected_idx = hit_indices[(pick.get("pos", 0) + 1) % len(hit_indices)]
+                    pos = hit_indices.index(selected_idx)
+                    self._last_overlap_pick = {"key": cycle_key, "pos": pos, "point": (ix, iy)}
+                else:
+                    self._last_overlap_pick = {"key": tuple(hit_indices), "pos": 0, "point": (ix, iy)}
+
+                self.canvas.selected_obj_idx, self.canvas.drag_mode, self.canvas.selected_point_idx = selected_idx, 'box_move', -1
+
+                det = self.last_results[selected_idx]
+                self.selected_island_idx = 0
+                self.selected_island_kind = "outer"
+                det_class = det.get('name', det.get('label', self._default_class_name()))
+                if det_class in self.class_params:
+                    self.tk_vars['active_class'].set(det_class)
+
+                return self.redraw_current_frame()
+
+            if click_event:
+                self.canvas.selected_obj_idx = -1
+                self.selected_island_idx = 0
+                self.selected_island_kind = "outer"
+                self.redraw_current_frame()
+
+        # 3. DRAG HANDLING
+        if drag_event:
+            event, dx, dy = drag_event
+            ix, iy = self.canvas.get_image_coords(event.x, event.y, img_w, img_h)
+
+            # Continuous Brush Painting
+            if (active_tool == "brush" or brush_mode or (event.state & 0x0400 and is_opt)) and self.canvas.selected_obj_idx != -1:
+                det = self.last_results[self.canvas.selected_obj_idx]
+                box = det.get('box', det)
+                x1, y1 = int(box['x1']), int(box['y1'])
+                x2, y2 = int(box['x2']), int(box['y2'])
+
+                # Initialize manual mask if needed
+                if 'manual_mask' not in det:
+                    h, w = y2 - y1, x2 - x1
+                    det['manual_mask'] = np.full((h, w), 2, dtype=np.uint8)
+
+                lx, ly = int(ix - x1), int(iy - y1)
+                if 0 <= lx < det['manual_mask'].shape[1] and 0 <= ly < det['manual_mask'].shape[0]:
+                    # Get brush parameters
+                    brush_size = self.tk_vars['brush_size'].get()
+                    brush_type = self.tk_vars['brush_type'].get()
+                    mask_value = 0 if brush_type == 'background' else 1
+
+                    cv2.circle(det['manual_mask'], (lx, ly), brush_size, mask_value, -1)
+                    self.redraw_current_frame()
+                return
+
+            if not getattr(self, '_is_dragging', False):
+                self._save_history()
+                self._is_dragging = True
+
+            idx = self.canvas.selected_obj_idx
+            if idx == -1:
+                return
+            det = self.last_results[idx]
+            box = det.get('box', det)
+
+            if self.canvas.drag_mode == 'point_move':
+                p_idx = self.canvas.selected_point_idx
+                if self.selected_island_kind == "hole":
+                    rings = self._det_get_holes_relative(det)
+                else:
+                    rings = self._det_get_polygons_relative(det)
+                if not rings:
+                    return
+                island_idx = int(max(0, min(self.selected_island_idx, len(rings) - 1)))
+                poly = rings[island_idx].astype(np.float32)
+                poly_view = poly[p_idx]
+                px, py = (poly_view[0][0], poly_view[0][1]) if poly_view.ndim > 1 else (poly_view[0], poly_view[1])
+                nx, ny = px + dx, py + dy
+                if event.state & 0x0001:  # Shift Snap
+                    nx, ny = self.image_processor.find_nearest_edge(nx + box['x1'], ny + box['y1'])
+                    nx -= box['x1']
+                    ny -= box['y1']
+                if poly_view.ndim > 1:
+                    poly_view[0][0], poly_view[0][1] = nx, ny
+                else:
+                    poly_view[0], poly_view[1] = nx, ny
+                rings[island_idx] = poly
+                if self.selected_island_kind == "hole":
+                    self._set_det_holes(det, rings)
+                else:
+                    self._set_det_polygons(det, rings)
+                self.redraw_current_frame()
+            elif 'box_resize' in self.canvas.drag_mode or self.canvas.drag_mode == 'box_move':
+                old_x1, old_y1 = float(box['x1']), float(box['y1'])
+                old_x2, old_y2 = float(box['x2']), float(box['y2'])
+                old_w = max(1.0, old_x2 - old_x1)
+                old_h = max(1.0, old_y2 - old_y1)
+
+                if self.canvas.drag_mode == 'box_resize_br':
+                    box['x2'] += dx
+                    box['y2'] += dy
+                elif self.canvas.drag_mode == 'box_resize_tl':
+                    box['x1'] += dx
+                    box['y1'] += dy
+                elif self.canvas.drag_mode == 'box_resize_tr':
+                    box['x2'] += dx
+                    box['y1'] += dy
+                elif self.canvas.drag_mode == 'box_resize_bl':
+                    box['x1'] += dx
+                    box['y2'] += dy
+                elif self.canvas.drag_mode == 'box_move':
+                    box['x1'] += dx
+                    box['x2'] += dx
+                    box['y1'] += dy
+                    box['y2'] += dy
+                    self.image_processor.compute_edge_map(self.current_raw_frame, box)
+
+                # Keep corners ordered and inside image bounds.
+                min_size = 2.0
+                x1_new, x2_new = sorted((float(box['x1']), float(box['x2'])))
+                y1_new, y2_new = sorted((float(box['y1']), float(box['y2'])))
+                x1_new = max(0.0, min(x1_new, img_w - min_size))
+                y1_new = max(0.0, min(y1_new, img_h - min_size))
+                x2_new = max(x1_new + min_size, min(x2_new, float(img_w)))
+                y2_new = max(y1_new + min_size, min(y2_new, float(img_h)))
+                box['x1'], box['y1'], box['x2'], box['y2'] = x1_new, y1_new, x2_new, y2_new
+
+                # Resize existing polygon together with the box.
+                if self.canvas.drag_mode.startswith('box_resize'):
+                    new_w = max(1.0, float(box['x2']) - float(box['x1']))
+                    new_h = max(1.0, float(box['y2']) - float(box['y1']))
+                    sx = new_w / old_w
+                    sy = new_h / old_h
+                    polygons_rel = self._det_get_polygons_relative(det)
+                    if polygons_rel:
+                        scaled = []
+                        for poly in polygons_rel:
+                            arr = poly.astype(np.float32)
+                            arr[:, 0, 0] *= sx
+                            arr[:, 0, 1] *= sy
+                            scaled.append(arr)
+                        self._set_det_polygons(det, scaled)
+                    holes_rel = self._det_get_holes_relative(det)
+                    if holes_rel:
+                        scaled_holes = []
+                        for hole in holes_rel:
+                            arr = hole.astype(np.float32)
+                            arr[:, 0, 0] *= sx
+                            arr[:, 0, 1] *= sy
+                            scaled_holes.append(arr)
+                        self._set_det_holes(det, scaled_holes)
+
+                self.redraw_current_frame()
+
+            # Reset flags when drag ends
+        if not drag_event:
+            self._is_dragging = False
+            self._brush_started = False
+
+    def add_new_box(self):
+        """Adds a box at the center of the current screen view."""
+        self._save_history()
+        if self.current_raw_frame is None:
+            return
+
+        img_h, img_w = self.current_raw_frame.shape[:2]
+        cls = self.tk_vars['active_class'].get()
+
+        # Get image coordinates for the center of the canvas
+        cw = self.canvas.winfo_width() / 2
+        ch = self.canvas.winfo_height() / 2
+        ix, iy = self.canvas.get_image_coords(cw, ch, img_w, img_h)
+
+        # Create a 150x150 box centered on the view
+        size = 75
+        x1, y1 = max(0, ix - size), max(0, iy - size)
+        x2, y2 = min(img_w, ix + size), min(img_h, iy + size)
+
+        new_det = {
+            "box": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
+            "name": cls,
+            # Keep brand-new boxes as rectangles until user explicitly refines/regenerates.
+            "suppress_auto_polygon": True,
+        }
+
+        self.last_results.append(new_det)
+        self.canvas.selected_obj_idx = len(self.last_results) - 1
+        self.selected_island_idx = 0
+        self.selected_island_kind = "outer"
+        self.redraw_current_frame()
+        self.status_var.set("Added new box")
+
+    def delete_selected(self, event=None):
+        if self.canvas.selected_obj_idx != -1:
+            self._save_history()
+            self.last_results.pop(self.canvas.selected_obj_idx)
+            self.canvas.selected_obj_idx = -1
+            self.selected_island_idx = 0
+            self.selected_island_kind = "outer"
+            self.redraw_current_frame()
diff --git a/src/gui/canvas_panel.py b/src/gui/canvas_panel.py
new file mode 100644
index 0000000..a548090
--- /dev/null
+++ b/src/gui/canvas_panel.py
@@ -0,0 +1,144 @@
+"""Main canvas panel for image display with zoom and pan support."""
+import tkinter as tk
+from PIL import Image, ImageTk
+import cv2
+
+class MainCanvas(tk.Canvas):
+    """Canvas widget for displaying images with zoom and pan capabilities."""
+
+    def __init__(self, parent):
+        # 1. Initialize state variables BEFORE super().__init__
+        self.zoom_level = 1.0
+        self.pan_x = 0
+        self.pan_y = 0
+        self.selected_obj_idx = -1
+        self.selected_point_idx = -1
+        self.drag_mode = None
+        self.last_mouse_x = 0
+        self.last_mouse_y = 0
+        self.display_img = None
+        self.on_redraw_callback = None
+        self.suspend_pan = False
+
+        super().__init__(parent, bg="#111", highlightthickness=0)
+
+        # Bindings
+        self.bind("<ButtonPress-1>", self._on_press)
+        self.bind("<Double-Button-1>", self._on_double_click)
+        self.bind("<B1-Motion>", self._on_drag)
+        self.bind("<ButtonRelease-1>", self._on_release)
+        self.bind("<Motion>", self._on_motion)
+
+        # Map both common right-click buttons
+        self.bind("<Button-2>", self._on_right_click)  # macOS
+        self.bind("<Button-3>", self._on_right_click)  # Windows/Linux
+        self.bind("<B2-Motion>", self._on_right_drag)  # macOS
+        self.bind("<B3-Motion>", self._on_right_drag)  # Windows/Linux
+        self.bind("<ButtonRelease-2>", self._on_right_release)  # macOS
+        self.bind("<ButtonRelease-3>", self._on_right_release)  # Windows/Linux
+
+        self.bind("<MouseWheel>", self._handle_zoom)
+        self.bind("<Button-4>", self._handle_zoom)
+        self.bind("<Button-5>", self._handle_zoom)
+
+    def _on_double_click(self, event):
+        """Handle double-click to add a new point to the selected polygon."""
+        if self.on_redraw_callback:
+            self.on_redraw_callback(double_click_event=event)
+
+    def _on_right_click(self, event):
+        """Handle right-click event for point deletion."""
+        if self.on_redraw_callback:
+            self.on_redraw_callback(right_click_event=event)
+
+    def set_redraw_callback(self, callback):
+        """Set callback function to trigger interaction logic."""
+        self.on_redraw_callback = callback
+
+    def get_image_coords(self, canvas_x, canvas_y, img_w, img_h):
+        """Converts canvas mouse coordinates to original image pixel coordinates."""
+        # Calculate center of canvas
+        cx = self.winfo_width() // 2 + self.pan_x
+        cy = self.winfo_height() // 2 + self.pan_y
+
+        # Calculate coordinates relative to the image center, accounting for zoom
+        rel_x = (canvas_x - cx) / self.zoom_level
+        rel_y = (canvas_y - cy) / self.zoom_level
+
+        # Translate from center-origin to top-left origin
+        img_x = int(rel_x + img_w / 2)
+        img_y = int(rel_y + img_h / 2)
+        return img_x, img_y
+
+    def _on_press(self, event):
+        self.last_mouse_x, self.last_mouse_y = event.x, event.y
+        if self.on_redraw_callback:
+            self.on_redraw_callback(click_event=event)
+
+    def _on_drag(self, event):
+        # Calculate movement delta in image pixels
+        dx = (event.x - self.last_mouse_x) / self.zoom_level
+        dy = (event.y - self.last_mouse_y) / self.zoom_level
+
+        # If no object is being edited, perform standard pan
+        if self.selected_obj_idx == -1 and not self.suspend_pan:
+            self.pan_x += event.x - self.last_mouse_x
+            self.pan_y += event.y - self.last_mouse_y
+
+        self.last_mouse_x, self.last_mouse_y = event.x, event.y
+
+        if self.on_redraw_callback:
+            # Main app handles moving points or boxes
+            self.on_redraw_callback(drag_event=(event, dx, dy))
+
+    def _on_release(self, event):
+        if self.on_redraw_callback:
+            self.on_redraw_callback()
+
+    def _on_motion(self, event):
+        if self.on_redraw_callback:
+            self.on_redraw_callback(move_event=event)
+
+    def _on_right_drag(self, event):
+        if self.on_redraw_callback:
+            self.on_redraw_callback(right_drag_event=event)
+
+    def _on_right_release(self, event):
+        if self.on_redraw_callback:
+            self.on_redraw_callback(right_release_event=event)
+
+    def display_image(self, cv_image):
+        """Display OpenCV image on canvas with current zoom and pan."""
+        if cv_image is None: return
+
+        # Pre-process for Tkinter
+        rgb_frame = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
+        pil_img = Image.fromarray(rgb_frame)
+
+        # Apply zoom resizing
+        new_w = int(pil_img.width * self.zoom_level)
+        new_h = int(pil_img.height * self.zoom_level)
+
+        if new_w > 0 and new_h > 0:
+            # Use NEAREST for maximum display throughput in live mode.
+            pil_img = pil_img.resize((new_w, new_h), Image.Resampling.NEAREST)
+
+        self.display_img = ImageTk.PhotoImage(pil_img)
+        self.delete("all")
+
+        # Center of canvas + current pan offset
+        cx = self.winfo_width() // 2 + self.pan_x
+        cy = self.winfo_height() // 2 + self.pan_y
+        self.create_image(cx, cy, image=self.display_img, anchor=tk.CENTER)
+
+    def _handle_zoom(self, event):
+        """Handle mouse wheel zoom centered on canvas."""
+        factor = 1.1 if (event.num == 4 or getattr(event, 'delta', 0) > 0) else 0.9
+        self.zoom_level = max(0.1, min(self.zoom_level * factor, 10.0))
+
+        # NEW: If the GUI has a zoom variable, keep it in sync
+        if hasattr(self.master.master, 'zoom_var'):
+            self.master.master.zoom_var.set(round(self.zoom_level, 2))
+
+        if self.on_redraw_callback:
+            self.on_redraw_callback()
diff --git a/src/gui/class_bank_mixin.py b/src/gui/class_bank_mixin.py
new file mode 100644
index 0000000..13dd90a
--- /dev/null
+++ b/src/gui/class_bank_mixin.py
@@ -0,0 +1,555 @@
+"""Class-set and reusable element-bank GUI mixin."""
+
+from pathlib import Path
+from datetime import datetime
+import json
+
+import numpy as np
+from tkinter import filedialog, messagebox, simpledialog
+
+
+class ClassBankMixin:
+    """Methods for class-set management and reusable element templates."""
+
+    def _bind_class_visibility_traces(self):
+        """Bind redraw callbacks for class visibility toggles once per class."""
+        for cls_name, var in self.ctrl_panel.visibility_vars.items():
+            if cls_name in self._visibility_trace_bound_classes:
+                continue
+            var.trace_add("write", lambda *args: self.redraw_current_frame())
+            self._visibility_trace_bound_classes.add(cls_name)
+
+        self._visibility_trace_bound_classes.intersection_update(
+            set(self.ctrl_panel.visibility_vars.keys())
+        )
+
+    def _refresh_class_ui(self):
+        """Refresh all class-dependent UI controls."""
+        class_names = list(self.class_params.keys())
+        self.ctrl_panel.update_class_selector(class_names)
+        self.ctrl_panel.update_class_visibility(class_names, self.class_colors)
+        self._bind_class_visibility_traces()
+
+        active = self.tk_vars['active_class'].get()
+        if active not in self.class_params:
+            self.tk_vars['active_class'].set(self._default_class_name())
+        self.redraw_current_frame()
+
+    def _apply_class_set(self, class_names, class_params=None, class_colors=None, annotation_mode=None):
+        """Apply a new class set and rebuild mappings/colors/params."""
+        cleaned = []
+        seen = set()
+        for raw in class_names:
+            name = str(raw).strip()
+            if not name or name in seen:
+                continue
+            seen.add(name)
+            cleaned.append(name)
+        if not cleaned:
+            raise ValueError("Class set is empty.")
+
+        old_params = dict(self.class_params)
+        old_colors = dict(self.class_colors)
+        class_params = class_params or {}
+        class_colors = class_colors or {}
+
+        self.class_params = {
+            name: dict(class_params.get(name, old_params.get(name, self._default_class_params())))
+            for name in cleaned
+        }
+        self.class_colors = {
+            name: class_colors.get(
+                name,
+                old_colors.get(name, self._class_color_palette[idx % len(self._class_color_palette)])
+            )
+            for idx, name in enumerate(cleaned)
+        }
+        self.class_to_id = {name: idx for idx, name in enumerate(cleaned)}
+        self.id_to_class = {idx: name for name, idx in self.class_to_id.items()}
+        if annotation_mode in self.annotation_mode_labels:
+            self.annotation_mode = annotation_mode
+        self.ctrl_panel.class_params = self.class_params
+        self._refresh_class_ui()
+
+    def choose_local_output_dir(self):
+        """Pick local folder used for manual image annotation saves."""
+        selected = filedialog.askdirectory(
+            title="Select local annotation output root",
+            initialdir=str(self.local_output_dir),
+        )
+        if not selected:
+            return
+        self.local_output_dir = Path(selected)
+        self.status_var.set(f"Local save folder: {self.local_output_dir}")
+
+    def create_new_class_set(self):
+        """Create a class set from a comma-separated list."""
+        mode = self._prompt_annotation_mode()
+        if mode is None:
+            return
+
+        current = ", ".join(self.class_to_id.keys())
+        entered = simpledialog.askstring(
+            "Create Class Set",
+            (
+                "Enter class names separated by commas.\n\n"
+                "Example: background, cell, nucleus\n\n"
+                f"Current classes: {current}"
+            ),
+            parent=self.root,
+        )
+        if entered is None:
+            return
+        try:
+            classes = [part.strip() for part in entered.split(",")]
+            self._apply_class_set(classes, annotation_mode=mode)
+            self.class_set_source_path = None
+            self.status_var.set(
+                f"Created class set with {len(self.class_to_id)} classes ({self._get_annotation_mode_label()})"
+            )
+        except Exception as e:
+            messagebox.showerror("Class Set Error", f"Failed to create class set:\n{e}")
+
+    def add_single_class(self):
+        """Add one class to current class set without replacing existing classes."""
+        entered = simpledialog.askstring(
+            "Add Class",
+            "Enter new class name:",
+            parent=self.root,
+        )
+        if entered is None:
+            return
+
+        new_name = entered.strip()
+        if not new_name:
+            messagebox.showwarning("Invalid Class", "Class name cannot be empty.")
+            return
+        if new_name in self.class_to_id:
+            messagebox.showwarning("Duplicate Class", f"Class '{new_name}' already exists.")
+            self.tk_vars['active_class'].set(new_name)
+            return
+
+        class_names = [name for _, name in sorted((idx, name) for name, idx in self.class_to_id.items())]
+        class_names.append(new_name)
+        self._apply_class_set(class_names, annotation_mode=self.annotation_mode)
+        self.tk_vars['active_class'].set(new_name)
+        self.status_var.set(f"Added class: {new_name} ({self._get_annotation_mode_label()})")
+
+    def load_class_set_from_disk(self):
+        """Load class set from JSON or TXT file."""
+        path = filedialog.askopenfilename(
+            title="Load class set",
+            filetypes=[
+                ("Class files", "*.json *.txt"),
+                ("JSON", "*.json"),
+                ("Text", "*.txt"),
+                ("All files", "*.*"),
+            ],
+        )
+        if not path:
+            return
+        try:
+            class_names, loaded_params, loaded_colors, loaded_mode = self._read_class_set(Path(path))
+            if loaded_mode is None:
+                loaded_mode = self._prompt_annotation_mode()
+                if loaded_mode is None:
+                    return
+            self._apply_class_set(class_names, loaded_params, loaded_colors, annotation_mode=loaded_mode)
+            self.class_set_source_path = Path(path)
+            self.status_var.set(
+                f"Loaded class set: {self.class_set_source_path.name} ({self._get_annotation_mode_label()})"
+            )
+        except Exception as e:
+            messagebox.showerror("Class Set Error", f"Failed loading class set:\n{e}")
+
+    def save_class_set_as(self):
+        """Persist current class set to JSON or TXT file."""
+        path = filedialog.asksaveasfilename(
+            title="Save class set as",
+            defaultextension=".json",
+            initialfile="class_set.json",
+            filetypes=[
+                ("JSON", "*.json"),
+                ("Text", "*.txt"),
+                ("All files", "*.*"),
+            ],
+        )
+        if not path:
+            return
+
+        out_path = Path(path)
+        class_names = [name for _, name in sorted((idx, name) for name, idx in self.class_to_id.items())]
+        try:
+            if out_path.suffix.lower() == ".txt":
+                out_path.write_text("\n".join(class_names) + ("\n" if class_names else ""), encoding="utf-8")
+            else:
+                payload = {
+                    "classes": class_names,
+                    "class_params": {name: self.class_params.get(name, self._default_class_params()) for name in class_names},
+                    "class_colors": {name: list(self.class_colors.get(name, (255, 0, 255))) for name in class_names},
+                    "annotation_mode": self.annotation_mode,
+                }
+                out_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+            self.class_set_source_path = out_path
+            self.status_var.set(f"Saved class set: {out_path.name}")
+        except Exception as e:
+            messagebox.showerror("Class Set Error", f"Failed saving class set:\n{e}")
+
+    def _read_class_set(self, path: Path):
+        """Read class names and optional style data from supported class-set files."""
+        suffix = path.suffix.lower()
+        if suffix == ".txt":
+            lines = path.read_text(encoding="utf-8").splitlines()
+            names = [line.strip() for line in lines if line.strip()]
+            return names, {}, {}, None
+
+        payload = json.loads(path.read_text(encoding="utf-8"))
+        if isinstance(payload, list):
+            names = [str(item).strip() for item in payload if str(item).strip()]
+            return names, {}, {}, None
+        if isinstance(payload, dict):
+            classes = payload.get("classes")
+            if isinstance(classes, list):
+                names = []
+                for item in classes:
+                    if isinstance(item, str):
+                        name = item.strip()
+                    elif isinstance(item, dict):
+                        name = str(item.get("name", "")).strip()
+                    else:
+                        name = str(item).strip()
+                    if name:
+                        names.append(name)
+                loaded_params = self._sanitize_loaded_class_params(payload.get("class_params", {}), names)
+                loaded_colors = self._sanitize_loaded_class_colors(payload.get("class_colors", {}), names)
+                loaded_mode = payload.get("annotation_mode", "yolo_single_polygon")
+                if loaded_mode not in self.annotation_mode_labels:
+                    loaded_mode = "yolo_single_polygon"
+                return names, loaded_params, loaded_colors, loaded_mode
+        raise ValueError("Expected TXT lines or JSON with a classes array.")
+
+    def _sanitize_loaded_class_params(self, maybe_params, class_names):
+        """Validate loaded class params map."""
+        if not isinstance(maybe_params, dict):
+            return {}
+        out = {}
+        for name in class_names:
+            raw = maybe_params.get(name)
+            if not isinstance(raw, dict):
+                continue
+            default = self._default_class_params()
+            out[name] = {
+                "low": int(raw.get("low", default["low"])),
+                "high": int(raw.get("high", default["high"])),
+                "clahe": float(raw.get("clahe", default["clahe"])),
+                "morph": int(raw.get("morph", default["morph"])),
+                "eps": float(raw.get("eps", default["eps"])),
+            }
+        return out
+
+    def _sanitize_loaded_class_colors(self, maybe_colors, class_names):
+        """Validate loaded class color map as BGR tuples."""
+        if not isinstance(maybe_colors, dict):
+            return {}
+        out = {}
+        for name in class_names:
+            raw = maybe_colors.get(name)
+            if not isinstance(raw, (list, tuple)) or len(raw) != 3:
+                continue
+            try:
+                bgr = tuple(max(0, min(255, int(v))) for v in raw)
+            except Exception:
+                continue
+            out[name] = bgr
+        return out
+
+    def _default_template_name(self, cls_name):
+        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+        return f"{cls_name}_{ts}"
+
+    @staticmethod
+    def _fit_box_inside_image(cx, cy, w, h, img_w, img_h):
+        w = max(2.0, min(float(w), float(img_w)))
+        h = max(2.0, min(float(h), float(img_h)))
+        x1 = float(cx) - w / 2.0
+        y1 = float(cy) - h / 2.0
+        x2 = x1 + w
+        y2 = y1 + h
+        if x1 < 0:
+            x2 -= x1
+            x1 = 0.0
+        if y1 < 0:
+            y2 -= y1
+            y1 = 0.0
+        if x2 > img_w:
+            shift = x2 - float(img_w)
+            x1 -= shift
+            x2 -= shift
+        if y2 > img_h:
+            shift = y2 - float(img_h)
+            y1 -= shift
+            y2 -= shift
+        x1 = max(0.0, x1)
+        y1 = max(0.0, y1)
+        x2 = min(float(img_w), x2)
+        y2 = min(float(img_h), y2)
+        return {"x1": x1, "y1": y1, "x2": x2, "y2": y2}
+
+    @staticmethod
+    def _normalize_poly(poly_arr, width, height):
+        arr = np.array(poly_arr, dtype=np.float32).reshape(-1, 2)
+        if len(arr) < 3:
+            return []
+        w = max(1.0, float(width))
+        h = max(1.0, float(height))
+        out = []
+        for x, y in arr:
+            nx = float(np.clip(x / w, 0.0, 1.0))
+            ny = float(np.clip(y / h, 0.0, 1.0))
+            out.append([round(nx, 6), round(ny, 6)])
+        return out
+
+    @staticmethod
+    def _denormalize_poly(poly_points, width, height):
+        pts = []
+        w = max(1.0, float(width))
+        h = max(1.0, float(height))
+        for pt in poly_points:
+            if not isinstance(pt, (list, tuple)) or len(pt) < 2:
+                continue
+            try:
+                nx = float(pt[0])
+                ny = float(pt[1])
+            except Exception:
+                continue
+            pts.append([np.clip(nx, 0.0, 1.0) * w, np.clip(ny, 0.0, 1.0) * h])
+        if len(pts) < 3:
+            return None
+        return np.array(pts, dtype=np.float32).reshape(-1, 1, 2)
+
+    def _element_bank_payload(self):
+        return {
+            "version": 1,
+            "updated_at": datetime.now().isoformat(),
+            "elements": self.element_bank,
+        }
+
+    def _load_default_element_bank_if_exists(self):
+        path = self.element_bank_path
+        if path.exists():
+            try:
+                self._load_element_bank_from_path(path, set_as_default=False)
+            except Exception as e:
+                self.status_var.set(f"Element bank load skipped: {e}")
+
+    def _load_element_bank_from_path(self, path: Path, set_as_default=True):
+        payload = json.loads(path.read_text(encoding="utf-8"))
+        elements = payload.get("elements", payload if isinstance(payload, list) else [])
+        if not isinstance(elements, list):
+            raise ValueError("Expected JSON object with elements[] or a top-level list.")
+        cleaned = []
+        for item in elements:
+            if not isinstance(item, dict):
+                continue
+            name = str(item.get("name", "")).strip()
+            class_name = str(item.get("class_name", self._default_class_name())).strip() or self._default_class_name()
+            try:
+                width = max(2.0, float(item.get("width", 64.0)))
+                height = max(2.0, float(item.get("height", 64.0)))
+            except Exception:
+                continue
+            polygons_norm = item.get("polygons_norm", [])
+            holes_norm = item.get("holes_norm", [])
+            if not isinstance(polygons_norm, list):
+                polygons_norm = []
+            if not isinstance(holes_norm, list):
+                holes_norm = []
+            if not name:
+                name = self._default_template_name(class_name)
+            cleaned.append(
+                {
+                    "name": name,
+                    "class_name": class_name,
+                    "width": width,
+                    "height": height,
+                    "polygons_norm": polygons_norm,
+                    "holes_norm": holes_norm,
+                    "created_at": str(item.get("created_at", datetime.now().isoformat())),
+                }
+            )
+        self.element_bank = cleaned
+        if set_as_default:
+            self.element_bank_path = path
+        self.status_var.set(f"Loaded element bank: {path.name} ({len(self.element_bank)} elements)")
+
+    def load_element_bank_from_disk(self):
+        path = filedialog.askopenfilename(
+            title="Load element bank",
+            filetypes=[("JSON", "*.json"), ("All files", "*.*")],
+        )
+        if not path:
+            return
+        try:
+            self._load_element_bank_from_path(Path(path), set_as_default=True)
+        except Exception as e:
+            messagebox.showerror("Element Bank Error", f"Failed loading element bank:\n{e}")
+
+    def _save_element_bank_to_path(self, path: Path, set_as_default=True):
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(self._element_bank_payload(), indent=2), encoding="utf-8")
+        if set_as_default:
+            self.element_bank_path = path
+        self.status_var.set(f"Saved element bank: {path.name}")
+
+    def save_element_bank_as(self):
+        path = filedialog.asksaveasfilename(
+            title="Save element bank as",
+            defaultextension=".json",
+            initialfile="element_bank.json",
+            filetypes=[("JSON", "*.json"), ("All files", "*.*")],
+        )
+        if not path:
+            return
+        try:
+            self._save_element_bank_to_path(Path(path), set_as_default=True)
+        except Exception as e:
+            messagebox.showerror("Element Bank Error", f"Failed saving element bank:\n{e}")
+
+    def _pick_element_from_bank(self):
+        if not self.element_bank:
+            messagebox.showinfo(
+                "Element Bank",
+                "Element bank is empty.\nSelect an annotation and use Elements -> Add Selected to Bank.",
+            )
+            return None
+        options = [
+            f"{idx + 1}. {el.get('name', 'unnamed')} [{el.get('class_name', '?')}]"
+            for idx, el in enumerate(self.element_bank)
+        ]
+        prompt = "Choose element number:\n\n" + "\n".join(options[:30])
+        if len(options) > 30:
+            prompt += f"\n... ({len(options) - 30} more not shown)"
+        picked = simpledialog.askinteger("Place Element", prompt, parent=self.root, minvalue=1, maxvalue=len(options))
+        if not picked:
+            return None
+        return self.element_bank[picked - 1]
+
+    def add_selected_to_element_bank(self):
+        idx = self.canvas.selected_obj_idx
+        if idx == -1 or idx >= len(self.last_results):
+            messagebox.showwarning("Element Bank", "Select an annotation first.")
+            return
+        det = self.last_results[idx]
+        box = det.get("box", det)
+        w = max(2.0, float(box.get("x2", 0.0)) - float(box.get("x1", 0.0)))
+        h = max(2.0, float(box.get("y2", 0.0)) - float(box.get("y1", 0.0)))
+        class_name = str(det.get("name", det.get("label", self._default_class_name())))
+        polygons_rel = self._det_get_polygons_relative(det)
+        holes_rel = self._det_get_holes_relative(det)
+        if not polygons_rel:
+            polygons_rel = [np.array([[[0.0, 0.0]], [[w, 0.0]], [[w, h]], [[0.0, h]]], dtype=np.float32)]
+        polygons_norm = [self._normalize_poly(poly, w, h) for poly in polygons_rel]
+        holes_norm = [self._normalize_poly(poly, w, h) for poly in holes_rel]
+        name = simpledialog.askstring(
+            "Element Name",
+            "Name for this reusable element:",
+            initialvalue=self._default_template_name(class_name),
+            parent=self.root,
+        )
+        if name is None:
+            return
+        name = name.strip()
+        if not name:
+            messagebox.showwarning("Element Bank", "Element name cannot be empty.")
+            return
+        self.element_bank.append(
+            {
+                "name": name,
+                "class_name": class_name,
+                "width": w,
+                "height": h,
+                "polygons_norm": polygons_norm,
+                "holes_norm": holes_norm,
+                "created_at": datetime.now().isoformat(),
+            }
+        )
+        try:
+            self._save_element_bank_to_path(self.element_bank_path, set_as_default=True)
+        except Exception as e:
+            messagebox.showwarning("Element Bank", f"Added to memory, but auto-save failed:\n{e}")
+        self.status_var.set(f"Added element: {name}")
+
+    def place_element_from_bank(self):
+        if self.current_raw_frame is None:
+            messagebox.showwarning("Warning", "No image loaded.")
+            return
+        element = self._pick_element_from_bank()
+        if element is None:
+            return
+
+        scale = simpledialog.askfloat(
+            "Element Scale",
+            "Scale factor (1.0 = original size):",
+            initialvalue=1.0,
+            minvalue=0.1,
+            maxvalue=10.0,
+            parent=self.root,
+        )
+        if scale is None:
+            return
+
+        img_h, img_w = self.current_raw_frame.shape[:2]
+        cw = self.canvas.winfo_width() / 2
+        ch = self.canvas.winfo_height() / 2
+        cx, cy = self.canvas.get_image_coords(cw, ch, img_w, img_h)
+        width = max(2.0, float(element.get("width", 64.0)) * float(scale))
+        height = max(2.0, float(element.get("height", 64.0)) * float(scale))
+        box = self._fit_box_inside_image(cx, cy, width, height, img_w, img_h)
+        box_w = max(1.0, box["x2"] - box["x1"])
+        box_h = max(1.0, box["y2"] - box["y1"])
+
+        polygons = []
+        for poly in element.get("polygons_norm", []):
+            arr = self._denormalize_poly(poly, box_w, box_h)
+            if arr is not None:
+                polygons.append(arr)
+        if not polygons:
+            polygons = [np.array([[[0.0, 0.0]], [[box_w, 0.0]], [[box_w, box_h]], [[0.0, box_h]]], dtype=np.float32)]
+
+        holes = []
+        for poly in element.get("holes_norm", []):
+            arr = self._denormalize_poly(poly, box_w, box_h)
+            if arr is not None:
+                holes.append(arr)
+
+        class_name = str(element.get("class_name", self._default_class_name()))
+        if class_name not in self.class_to_id:
+            if messagebox.askyesno(
+                "Unknown Class",
+                f"Element class '{class_name}' is not in current class set.\nAdd it now?",
+                parent=self.root,
+            ):
+                class_names = [name for _, name in sorted((idx, name) for name, idx in self.class_to_id.items())]
+                class_names.append(class_name)
+                self._apply_class_set(class_names, annotation_mode=self.annotation_mode)
+            else:
+                class_name = self.tk_vars['active_class'].get()
+
+        self._save_history()
+        det = {
+            "box": box,
+            "name": class_name,
+            "poly": polygons[0],
+            "polygons": polygons,
+            "conf": 1.0,
+        }
+        if holes:
+            det["holes"] = holes
+
+        self.last_results.append(det)
+        self.canvas.selected_obj_idx = len(self.last_results) - 1
+        self.canvas.selected_point_idx = -1
+        self.selected_island_idx = 0
+        self.selected_island_kind = "outer"
+        self.redraw_current_frame()
+        self.status_var.set(f"Placed element: {element.get('name', 'unnamed')}")
diff --git a/src/gui/control_panel.py b/src/gui/control_panel.py
new file mode 100644
index 0000000..870ce9a
--- /dev/null
+++ b/src/gui/control_panel.py
@@ -0,0 +1,337 @@
+"""Control panel GUI component with parameter controls and focus plot."""
+import tkinter as tk
+from tkinter import ttk
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+
+
+class ControlPanel(tk.Frame):
+    """Right-side control panel with tuning parameters and focus plot."""
+
+    def __init__(self, parent, class_params, tk_vars, callbacks):
+        """
+        Initialize control panel.
+
+        Args:
+            parent: Parent tkinter widget
+            class_params: Dict of class-specific parameters
+            tk_vars: Dict with tkinter variables:
+                - active_class: StringVar for selected class
+                - canny_high: IntVar for Canny high threshold
+                - morph_kernel: IntVar for morphological kernel size
+                - poly_epsilon: DoubleVar for polygon epsilon
+                - show_debug_edges: BooleanVar for edge visualization
+                - plot_window_size: IntVar for focus plot window
+                - annotation_line_width: IntVar for annotation line width
+            callbacks: Dict with callback functions:
+                - on_train_click: Function to save for training
+        """
+        super().__init__(parent, width=300, relief=tk.SUNKEN, bd=1)
+        self.class_params = class_params
+        self.tk_vars = tk_vars
+        self.callbacks = callbacks
+
+        # Create canvas and scrollbar for scrollable content
+        self.canvas = tk.Canvas(self, width=280, highlightthickness=0)
+        self.scrollbar = tk.Scrollbar(self, orient="vertical", command=self.canvas.yview)
+        self.scrollable_frame = tk.Frame(self.canvas)
+
+        self.scrollable_frame.bind(
+            "<Configure>",
+            lambda e: self.canvas.configure(scrollregion=self.canvas.bbox("all"))
+        )
+
+        self.canvas.create_window((0, 0), window=self.scrollable_frame, anchor="nw")
+        self.canvas.configure(yscrollcommand=self.scrollbar.set)
+
+        self.canvas.pack(side="left", fill="both", expand=True, padx=5, pady=5)
+        self.scrollbar.pack(side="right", fill="y")
+
+        # FIX: Only bind to this specific canvas, not bind_all
+        self.canvas.bind("<MouseWheel>", self._on_mousewheel)
+        self.canvas.bind("<Button-4>", self._on_mousewheel)
+        self.canvas.bind("<Button-5>", self._on_mousewheel)
+
+        self._setup_ui()
+
+    def _on_mousewheel(self, event):
+        """Handle mousewheel scrolling."""
+        if event.num == 4 or event.delta > 0:
+            self.canvas.yview_scroll(-1, "units")
+        elif event.num == 5 or event.delta < 0:
+            self.canvas.yview_scroll(1, "units")
+
+    def _setup_ui(self):
+        """Setup control panel UI elements."""
+        frame = self.scrollable_frame  # Use scrollable frame for all widgets
+
+        # Title
+        tk.Label(frame, text="Tuning Controls", font=('bold', 12)).pack(anchor=tk.W, pady=5)
+
+        # Class selector
+        tk.Label(frame, text="Active Class:").pack(anchor=tk.W)
+        self.class_combobox = ttk.Combobox(
+            frame,
+            textvariable=self.tk_vars['active_class'],
+            values=list(self.class_params.keys()),
+            state="readonly"
+        )
+        self.class_combobox.pack(fill=tk.X, pady=5)
+
+        # Canny High threshold
+        tk.Label(frame, text="Canny High:").pack(anchor=tk.W)
+        tk.Scale(
+            frame,
+            from_=0,
+            to=255,
+            orient=tk.HORIZONTAL,
+            variable=self.tk_vars['canny_high']
+        ).pack(fill=tk.X)
+
+        # Morph kernel size
+        tk.Label(frame, text="Morph Kernel:").pack(anchor=tk.W)
+        tk.Scale(
+            frame,
+            from_=1,
+            to=100,
+            orient=tk.HORIZONTAL,
+            variable=self.tk_vars['morph_kernel']
+        ).pack(fill=tk.X)
+
+        # Polygon precision
+        tk.Label(frame, text="Poly Precision:").pack(anchor=tk.W)
+        tk.Scale(
+            frame,
+            from_=0.0001,
+            to=0.05,
+            resolution=0.0001,
+            orient=tk.HORIZONTAL,
+            variable=self.tk_vars['poly_epsilon']
+        ).pack(fill=tk.X)
+
+        # Debug edges checkbox
+        tk.Checkbutton(
+            frame,
+            text="View Raw Edge Map",
+            variable=self.tk_vars['show_debug_edges']
+        ).pack(anchor=tk.W, pady=5)
+
+        # Segmentation method selector
+        tk.Label(frame, text="Segmentation Method:", font=('bold', 10)).pack(anchor=tk.W, pady=(10, 5))
+
+        seg_frame = tk.Frame(frame)
+        seg_frame.pack(fill=tk.X, pady=5)
+
+        # Ensure we use the correct variable from tk_vars
+        seg_var = self.tk_vars.get('segmentation_method')
+
+        tk.Radiobutton(
+            seg_frame,
+            text="Watershed",
+            variable=seg_var,
+            value='watershed'
+        ).pack(side=tk.LEFT, padx=2)
+
+        tk.Radiobutton(
+            seg_frame,
+            text="GrabCut",
+            variable=seg_var,
+            value='grabcut'
+        ).pack(side=tk.LEFT, padx=2)
+
+        tk.Radiobutton(
+            seg_frame,
+            text="✨ Smart",
+            variable=seg_var,
+            value='smart'
+        ).pack(side=tk.LEFT, padx=2)
+
+        # Brush tool section
+        tk.Label(frame, text="Brush Tool:", font=('bold', 10)).pack(anchor=tk.W, pady=(10, 5))
+
+        # Brush usage hint
+        hint_label = tk.Label(
+            frame,
+            text="💡 Tip: Paint BACKGROUND (red)\naround object edges for best results",
+            font=('Arial', 9, 'italic'),
+            fg='#666',
+            justify=tk.LEFT
+        )
+        hint_label.pack(anchor=tk.W, pady=2)
+
+        # Brush mode toggle
+        brush_frame = tk.Frame(frame)
+        brush_frame.pack(fill=tk.X, pady=5)
+
+        tk.Checkbutton(
+            brush_frame,
+            text="🖌️ Enable Brush Mode",
+            variable=self.tk_vars['brush_mode']
+        ).pack(side=tk.LEFT)
+
+        # Brush type radio buttons
+        brush_type_frame = tk.Frame(frame)
+        brush_type_frame.pack(fill=tk.X, pady=2)
+
+        tk.Radiobutton(
+            brush_type_frame,
+            text="Background",
+            variable=self.tk_vars['brush_type'],
+            value='background'
+        ).pack(side=tk.LEFT, padx=5)
+
+        tk.Radiobutton(
+            brush_type_frame,
+            text="Foreground",
+            variable=self.tk_vars['brush_type'],
+            value='foreground'
+        ).pack(side=tk.LEFT, padx=5)
+
+        # Brush size slider
+        tk.Label(frame, text="Brush Size:").pack(anchor=tk.W)
+        tk.Scale(
+            frame,
+            from_=5,
+            to=50,
+            orient=tk.HORIZONTAL,
+            variable=self.tk_vars['brush_size']
+        ).pack(fill=tk.X)
+
+        # Regenerate edge button
+        tk.Button(
+            frame,
+            text="🔄 Regenerate Edge",
+            command=self.callbacks.get('on_regenerate_edge', lambda: None),
+            bg="#007bff",
+            fg="white"
+        ).pack(fill=tk.X, pady=5)
+
+        # Clear brush strokes button
+        tk.Button(
+            frame,
+            text="Clear Brush Strokes",
+            command=self.callbacks.get('on_clear_brush', lambda: None)
+        ).pack(fill=tk.X, pady=2)
+
+        # Class visibility section
+        tk.Label(frame, text="Class Visibility:", font=('bold', 10)).pack(anchor=tk.W, pady=(10, 5))
+        self.class_visibility_frame = tk.Frame(frame)
+        self.class_visibility_frame.pack(fill=tk.X, pady=5)
+
+        # Store visibility variables (will be populated by update_class_visibility)
+        self.visibility_vars = {}
+
+        # Annotation stroke width
+        tk.Label(frame, text="Annotation Line Width:").pack(anchor=tk.W, pady=(8, 0))
+        tk.Scale(
+            frame,
+            from_=1,
+            to=6,
+            orient=tk.HORIZONTAL,
+            variable=self.tk_vars['annotation_line_width']
+        ).pack(fill=tk.X)
+
+        # Training button
+        tk.Button(
+            frame,
+            text="SAVE FOR TRAINING",
+            bg="green",
+            fg="white",
+            font=('bold'),
+            command=self.callbacks['on_train_click']
+        ).pack(fill=tk.X, pady=20)
+
+        # Focus plot section
+        self._setup_focus_plot()
+
+    def _setup_focus_plot(self):
+        """Setup focus history plot."""
+        frame = self.scrollable_frame
+
+        tk.Label(frame, text="Focus History", font=('bold')).pack(anchor=tk.W, pady=(10, 0))
+
+        # Window size slider
+        tk.Scale(
+            frame,
+            from_=50,
+            to=500,
+            orient=tk.HORIZONTAL,
+            variable=self.tk_vars['plot_window_size']
+        ).pack(fill=tk.X)
+
+        # Create matplotlib figure
+        self.fig, self.ax = plt.subplots(figsize=(3, 2), dpi=80)
+        self.ax.set_facecolor('#f0f0f0')
+        self.line, = self.ax.plot([], [], color='blue', lw=1.5)
+        self.ax.get_xaxis().set_visible(False)
+
+        # Embed in tkinter
+        self.canvas_plot = FigureCanvasTkAgg(self.fig, master=frame)
+        self.canvas_plot.get_tk_widget().pack(fill=tk.X, pady=10)
+
+    def update_focus_plot(self, focus_data, window_size):
+        """
+        Update focus plot with new data.
+
+        Args:
+            focus_data: List of focus scores
+            window_size: Number of recent points to display
+        """
+        display_data = focus_data[-window_size:] if len(focus_data) > window_size else focus_data
+
+        if not display_data:
+            self.line.set_data([], [])
+        else:
+            self.line.set_data(range(len(display_data)), display_data)
+            ymin, ymax = min(display_data), max(display_data)
+            margin = (ymax - ymin) * 0.1 if ymax > ymin else 1.0
+            self.ax.set_ylim(ymin - margin, ymax + margin)
+            self.ax.set_xlim(0, window_size)
+
+        self.canvas_plot.draw_idle()
+
+    def update_class_visibility(self, class_names, class_colors):
+        """
+        Update class visibility checkboxes.
+
+        Args:
+            class_names: List of class names
+            class_colors: Dict mapping class names to BGR colors
+        """
+        class_set = set(class_names)
+
+        # Remove stale variables for classes no longer present
+        for existing in list(self.visibility_vars.keys()):
+            if existing not in class_set:
+                del self.visibility_vars[existing]
+
+        # Clear existing checkboxes
+        for widget in self.class_visibility_frame.winfo_children():
+            widget.destroy()
+
+        # Create checkbox for each class
+        for cls_name in class_names:
+            if cls_name not in self.visibility_vars:
+                self.visibility_vars[cls_name] = tk.BooleanVar(value=True)
+
+            frame = tk.Frame(self.class_visibility_frame)
+            frame.pack(fill=tk.X, pady=2)
+
+            # Color indicator
+            color_bgr = class_colors.get(cls_name, (255, 0, 255))
+            # Convert BGR to hex for tkinter
+            color_hex = f"#{color_bgr[2]:02x}{color_bgr[1]:02x}{color_bgr[0]:02x}"
+            color_label = tk.Label(frame, bg=color_hex, width=2, height=1)
+            color_label.pack(side=tk.LEFT, padx=(0, 5))
+
+            # Checkbox
+            cb = tk.Checkbutton(
+                frame,
+                text=cls_name,
+                variable=self.visibility_vars[cls_name]
+            )
+            cb.pack(side=tk.LEFT)
+
+    def update_class_selector(self, class_names):
+        """Refresh active-class dropdown with a new class set."""
+        self.class_combobox.configure(values=list(class_names))
diff --git a/src/gui/dataset_prep_mixin.py b/src/gui/dataset_prep_mixin.py
new file mode 100644
index 0000000..6db5c3b
--- /dev/null
+++ b/src/gui/dataset_prep_mixin.py
@@ -0,0 +1,522 @@
+"""YOLO dataset prep script generation mixin for the GUI."""
+
+from pathlib import Path
+from datetime import datetime
+import json
+import os
+import textwrap
+
+from tkinter import filedialog, messagebox, simpledialog
+
+
+class DatasetPrepMixin:
+    """Methods to generate standalone YOLO segmentation dataset prep scripts."""
+
+    def show_dataset_prep_script_dialog(self):
+        """Collect settings and generate a headless dataset preparation script."""
+        source_dir = filedialog.askdirectory(
+            title="Select source folder (contains images/labels or images+labels folders)"
+        )
+        if not source_dir:
+            return
+
+        default_dest = str(Path(source_dir).parent / f"{Path(source_dir).name}_yolo_seg")
+        destination_dir = simpledialog.askstring(
+            "Destination Dataset Path",
+            (
+                "Destination path to create on the training machine.\n"
+                "This can be an absolute path and does not need to exist yet."
+            ),
+            initialvalue=default_dest,
+            parent=self.root,
+        )
+        if destination_dir is None:
+            return
+        destination_dir = destination_dir.strip()
+        if not destination_dir:
+            messagebox.showwarning("Invalid Input", "Destination path cannot be empty.")
+            return
+
+        train_ratio = simpledialog.askfloat(
+            "Training Ratio",
+            "Training split ratio (e.g. 0.85 for 85/15 train/val):",
+            initialvalue=0.85,
+            minvalue=0.5,
+            maxvalue=0.99,
+            parent=self.root,
+        )
+        if train_ratio is None:
+            return
+
+        seed = simpledialog.askinteger(
+            "Random Seed",
+            "Random seed for reproducible split:",
+            initialvalue=42,
+            parent=self.root,
+        )
+        if seed is None:
+            return
+
+        model_spec = self._prompt_training_model_spec()
+        if model_spec is None:
+            return
+
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        default_script_name = f"prepare_yolo_seg_dataset_{timestamp}.py"
+        script_path = filedialog.asksaveasfilename(
+            title="Save generated preparation script",
+            defaultextension=".py",
+            initialfile=default_script_name,
+            filetypes=[("Python Script", "*.py"), ("All Files", "*.*")],
+        )
+        if not script_path:
+            return
+
+        class_names = [
+            name for _, name in sorted((class_id, name) for name, class_id in self.class_to_id.items())
+        ]
+
+        try:
+            project_root = Path(__file__).resolve().parents[2]
+            source_for_script = self._to_project_relative(source_dir, project_root)
+            destination_for_script = self._to_project_relative(destination_dir, project_root)
+            script_content = self._build_yolo_prep_script(
+                source_dir=source_for_script,
+                destination_dir=destination_for_script,
+                train_ratio=train_ratio,
+                seed=seed,
+                class_names=class_names,
+                model_spec=model_spec,
+            )
+            output_path = Path(script_path)
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+            output_path.write_text(script_content, encoding="utf-8")
+            os.chmod(output_path, 0o755)
+        except Exception as e:
+            messagebox.showerror("Generation Error", f"Failed to generate script:\n{e}")
+            self.status_var.set("Dataset prep script generation failed")
+            return
+
+        messagebox.showinfo(
+            "Script Generated",
+            (
+                f"Script saved:\n{output_path}\n\n"
+                "Run this on the training machine:\n"
+                f"python3 {output_path}\n\n"
+                "Optional dry-run:\n"
+                f"python3 {output_path} --dry-run"
+            ),
+        )
+        self.status_var.set(f"Generated dataset prep script: {output_path.name}")
+
+    def _prompt_training_model_spec(self):
+        """Prompt for initial training model: local file in models/ or Ultralytics model name."""
+        use_local = messagebox.askyesnocancel(
+            "Training Model",
+            (
+                "Use a local model file from this project (recommended if available)?\n\n"
+                "Yes: choose a local .pt/.yaml model file\n"
+                "No: enter an Ultralytics model name (e.g. yolo11n-seg.pt)\n"
+                "Cancel: abort"
+            ),
+            parent=self.root,
+        )
+
+        if use_local is None:
+            return None
+
+        if use_local:
+            project_root = Path(__file__).resolve().parents[2]
+            initial_dir = project_root / "models"
+            model_path = filedialog.askopenfilename(
+                title="Select input model",
+                initialdir=str(initial_dir if initial_dir.exists() else project_root),
+                filetypes=[
+                    ("YOLO models/config", "*.pt *.yaml *.yml"),
+                    ("All Files", "*.*"),
+                ],
+            )
+            if not model_path:
+                return None
+            return self._to_project_relative(model_path, project_root)
+
+        model_name = simpledialog.askstring(
+            "Ultralytics Model Name",
+            (
+                "Enter Ultralytics model name to auto-download at training time.\n"
+                "Examples: yolo11n-seg.pt, yolo11s-seg.pt"
+            ),
+            initialvalue="yolo11n-seg.pt",
+            parent=self.root,
+        )
+        if model_name is None:
+            return None
+        model_name = model_name.strip()
+        if not model_name:
+            messagebox.showwarning("Invalid Input", "Model name cannot be empty.")
+            return None
+        return model_name
+
+    @staticmethod
+    def _to_project_relative(path_value, project_root: Path):
+        """Convert absolute path under project root to relative POSIX path for portability."""
+        raw = Path(path_value).expanduser()
+        if not raw.is_absolute():
+            return raw.as_posix()
+        try:
+            return raw.resolve().relative_to(project_root.resolve()).as_posix()
+        except ValueError:
+            return str(raw.resolve())
+
+    def _build_yolo_prep_script(self, source_dir, destination_dir, train_ratio, seed, class_names, model_spec):
+        """Build a standalone Python script to prepare YOLO segmentation dataset."""
+        generated_at = datetime.now().isoformat(timespec="seconds")
+        script = f'''#!/usr/bin/env python3
+"""
+Auto-generated by AareLC ML Studio on {generated_at}.
+Prepare YOLO segmentation train/val folders from source images/labels.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import random
+import shutil
+from collections import Counter
+from datetime import datetime, timezone
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent
+DEFAULT_SOURCE = {source_dir!r}
+DEFAULT_DESTINATION = {destination_dir!r}
+DEFAULT_TRAIN_RATIO = {train_ratio!r}
+DEFAULT_SEED = {seed!r}
+DEFAULT_MODEL_SPEC = {model_spec!r}
+CLASS_NAMES = {json.dumps(class_names)}
+IMAGE_EXTENSIONS = {{".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff", ".webp"}}
+
+
+def resolve_user_path(path_value: str) -> Path:
+    p = Path(path_value).expanduser()
+    if not p.is_absolute():
+        p = PROJECT_ROOT / p
+    return p.resolve()
+
+
+def resolve_layout(source_root: Path) -> tuple[Path, Path]:
+    images_dir = source_root / "images"
+    labels_dir = source_root / "labels"
+    if images_dir.is_dir() and labels_dir.is_dir():
+        return images_dir, labels_dir
+    return source_root, source_root
+
+
+def collect_pairs(source_root: Path):
+    images_dir, labels_dir = resolve_layout(source_root)
+    image_files = []
+    for ext in IMAGE_EXTENSIONS:
+        image_files.extend(images_dir.rglob(f"*{{ext}}"))
+        image_files.extend(images_dir.rglob(f"*{{ext.upper()}}"))
+    image_files = sorted(set(image_files))
+
+    pairs = []
+    missing_labels = []
+    used_labels = set()
+
+    for img_path in image_files:
+        rel_image = img_path.relative_to(images_dir)
+        label_candidate = (labels_dir / rel_image).with_suffix(".txt")
+        if not label_candidate.exists():
+            flat_fallback = labels_dir / f"{{img_path.stem}}.txt"
+            if flat_fallback.exists():
+                label_candidate = flat_fallback
+
+        if label_candidate.exists():
+            pairs.append((img_path, label_candidate, rel_image))
+            used_labels.add(label_candidate.resolve())
+        else:
+            missing_labels.append(str(img_path))
+
+    all_labels = {{p.resolve() for p in labels_dir.rglob("*.txt")}}
+    orphan_labels = sorted(str(p) for p in (all_labels - used_labels))
+    return pairs, missing_labels, orphan_labels, images_dir, labels_dir
+
+
+def split_pairs(pairs, train_ratio: float, seed: int):
+    ordered = list(pairs)
+    rng = random.Random(seed)
+    rng.shuffle(ordered)
+    total = len(ordered)
+    if total == 0:
+        return [], []
+
+    train_count = int(total * train_ratio)
+    if total > 1:
+        train_count = max(1, min(total - 1, train_count))
+    else:
+        train_count = 1
+    return ordered[:train_count], ordered[train_count:]
+
+
+def parse_class_ids(label_path: Path):
+    class_ids = []
+    for line in label_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        head = line.split()[0]
+        try:
+            class_ids.append(int(float(head)))
+        except ValueError:
+            continue
+    return class_ids
+
+
+def write_dataset_yaml(destination_root: Path):
+    lines = [
+        f"path: {{destination_root.resolve()}}",
+        "train: train/images",
+        "val: val/images",
+        "",
+        "names:",
+    ]
+    for class_id, class_name in enumerate(CLASS_NAMES):
+        lines.append(f"  {{class_id}}: {{class_name}}")
+    dataset_yaml = destination_root / "dataset.yaml"
+    dataset_yaml.write_text("\\n".join(lines) + "\\n", encoding="utf-8")
+    return dataset_yaml
+
+
+def write_train_script(destination_root: Path, dataset_yaml: Path, model_spec: str):
+    train_script = destination_root / "train_yolo_seg.py"
+    script_text = f"""#!/usr/bin/env python3
+from pathlib import Path
+from ultralytics import YOLO
+import argparse
+
+
+def resolve_model(model_value: str) -> str:
+    p = Path(model_value).expanduser()
+    if p.is_absolute():
+        return str(p)
+    local_candidate = Path(__file__).resolve().parent.parent / p
+    if local_candidate.exists():
+        return str(local_candidate.resolve())
+    return model_value
+
+
+def main():
+    parser = argparse.ArgumentParser(description=\\"Train YOLO segmentation model\\")
+    parser.add_argument(\\"--model\\", default={model_spec!r}, help=\\"Local model path or Ultralytics model name\\")
+    parser.add_argument(\\"--data\\", default=None, help=\\"Dataset YAML path (default: ./dataset.yaml next to this script)\\")
+    parser.add_argument(\\"--epochs\\", type=int, default=100)
+    parser.add_argument(\\"--imgsz\\", type=int, default=640)
+    parser.add_argument(\\"--batch\\", type=int, default=16)
+    parser.add_argument(\\"--device\\", default=\\"0\\")
+    parser.add_argument(\\"--project\\", default=\\"runs/segment\\")
+    parser.add_argument(\\"--name\\", default=\\"aarelc_seg_train\\")
+    args = parser.parse_args()
+
+    model_value = resolve_model(args.model)
+    if args.data:
+        data_yaml = Path(args.data).expanduser().resolve()
+    else:
+        data_yaml = Path(__file__).resolve().parent / "dataset.yaml"
+    if not data_yaml.exists():
+        raise FileNotFoundError(
+            f"Dataset YAML not found: {{data_yaml}}. "
+            "Run prepare_yolo_seg_dataset first or pass --data /path/to/dataset.yaml."
+        )
+
+    model = YOLO(model_value)
+    model.train(
+        data=str(data_yaml),
+        epochs=args.epochs,
+        patience=50,
+        amp=False,
+        overlap_mask=False,
+        imgsz=args.imgsz,
+        batch=args.batch,
+        device=args.device,
+        project=args.project,
+        name=args.name,
+    )
+
+
+if __name__ == \\"__main__\\":
+    main()
+"""
+    train_script.write_text(script_text, encoding="utf-8")
+    train_script.chmod(0o755)
+    return train_script
+
+
+def copy_pairs(split_name: str, rows, destination_root: Path, dry_run: bool):
+    log_rows = []
+    for image_path, label_path, rel_image in rows:
+        dst_image = destination_root / split_name / "images" / rel_image
+        dst_label = destination_root / split_name / "labels" / rel_image.with_suffix(".txt")
+        log_rows.append(
+            {{
+                "source_image": str(image_path),
+                "source_label": str(label_path),
+                "dest_image": str(dst_image),
+                "dest_label": str(dst_label),
+            }}
+        )
+        if dry_run:
+            continue
+        dst_image.parent.mkdir(parents=True, exist_ok=True)
+        dst_label.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(image_path, dst_image)
+        shutil.copy2(label_path, dst_label)
+    return log_rows
+
+
+def build_text_log(metadata: dict) -> str:
+    lines = [
+        "YOLO Dataset Preparation Log",
+        "=" * 32,
+        f"Prepared at (UTC): {{metadata['prepared_at_utc']}}",
+        f"Source root: {{metadata['source_root']}}",
+        f"Destination root: {{metadata['destination_root']}}",
+        f"Model spec: {{metadata['model_spec']}}",
+        f"Training script: {{metadata['train_script']}}",
+        f"Dry run: {{metadata['dry_run']}}",
+        f"Train ratio: {{metadata['train_ratio']}}",
+        f"Seed: {{metadata['seed']}}",
+        "",
+        f"Total image+label pairs: {{metadata['totals']['pairs']}}",
+        f"Train pairs: {{metadata['totals']['train_pairs']}}",
+        f"Validation pairs: {{metadata['totals']['val_pairs']}}",
+        f"Missing labels: {{metadata['totals']['missing_labels']}}",
+        f"Orphan labels: {{metadata['totals']['orphan_labels']}}",
+        "",
+        "Class counts:",
+    ]
+    for class_id, count in sorted(metadata["class_counts"].items(), key=lambda x: int(x[0])):
+        class_name = metadata["class_names"].get(class_id, f"class_{{class_id}}")
+        lines.append(f"  class {{class_id}} ({{class_name}}): {{count}}")
+
+    lines.append("")
+    lines.append("Train files:")
+    for row in metadata["train_files"]:
+        lines.append(f"  IMG {{row['source_image']}} -> {{row['dest_image']}}")
+        lines.append(f"  LBL {{row['source_label']}} -> {{row['dest_label']}}")
+
+    lines.append("")
+    lines.append("Validation files:")
+    for row in metadata["val_files"]:
+        lines.append(f"  IMG {{row['source_image']}} -> {{row['dest_image']}}")
+        lines.append(f"  LBL {{row['source_label']}} -> {{row['dest_label']}}")
+
+    if metadata["missing_labels"]:
+        lines.append("")
+        lines.append("Images missing labels:")
+        for path in metadata["missing_labels"]:
+            lines.append(f"  {{path}}")
+
+    if metadata["orphan_labels"]:
+        lines.append("")
+        lines.append("Labels without matching images:")
+        for path in metadata["orphan_labels"]:
+            lines.append(f"  {{path}}")
+
+    return "\\n".join(lines) + "\\n"
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Prepare YOLO segmentation train/val dataset.")
+    parser.add_argument("--source", default=DEFAULT_SOURCE, help="Source folder with images/labels")
+    parser.add_argument("--destination", default=DEFAULT_DESTINATION, help="Destination dataset root")
+    parser.add_argument("--train-ratio", type=float, default=DEFAULT_TRAIN_RATIO, help="Train split ratio")
+    parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="Random seed")
+    parser.add_argument("--dry-run", action="store_true", help="Preview only, do not copy files")
+    args = parser.parse_args()
+
+    if not (0.0 < args.train_ratio < 1.0):
+        raise ValueError("train-ratio must be between 0 and 1")
+
+    source_root = resolve_user_path(args.source)
+    destination_root = resolve_user_path(args.destination)
+    if not source_root.exists():
+        raise FileNotFoundError(f"Source folder not found: {{source_root}}")
+
+    pairs, missing_labels, orphan_labels, images_dir, labels_dir = collect_pairs(source_root)
+    train_pairs, val_pairs = split_pairs(pairs, args.train_ratio, args.seed)
+
+    class_counts = Counter()
+    for _, label_path, _ in pairs:
+        class_counts.update(parse_class_ids(label_path))
+
+    if not args.dry_run:
+        for split in ("train", "val"):
+            (destination_root / split / "images").mkdir(parents=True, exist_ok=True)
+            (destination_root / split / "labels").mkdir(parents=True, exist_ok=True)
+
+    train_rows = copy_pairs("train", train_pairs, destination_root, args.dry_run)
+    val_rows = copy_pairs("val", val_pairs, destination_root, args.dry_run)
+
+    dataset_yaml = None
+    train_script_path = None
+    if not args.dry_run:
+        dataset_yaml = write_dataset_yaml(destination_root)
+        train_script_path = write_train_script(destination_root, dataset_yaml, DEFAULT_MODEL_SPEC)
+
+    now_utc = datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+    class_name_map = {{str(i): name for i, name in enumerate(CLASS_NAMES)}}
+    for class_id in class_counts:
+        class_name_map.setdefault(str(class_id), f"class_{{class_id}}")
+
+    metadata = {{
+        "prepared_at_utc": now_utc,
+        "source_root": str(source_root),
+        "destination_root": str(destination_root),
+        "images_dir_resolved": str(images_dir),
+        "labels_dir_resolved": str(labels_dir),
+        "dry_run": args.dry_run,
+        "train_ratio": args.train_ratio,
+        "seed": args.seed,
+        "dataset_yaml": str(dataset_yaml) if dataset_yaml else None,
+        "train_script": str(train_script_path) if train_script_path else None,
+        "model_spec": DEFAULT_MODEL_SPEC,
+        "class_names": class_name_map,
+        "class_counts": {{str(k): int(v) for k, v in sorted(class_counts.items())}},
+        "totals": {{
+            "pairs": len(pairs),
+            "train_pairs": len(train_pairs),
+            "val_pairs": len(val_pairs),
+            "missing_labels": len(missing_labels),
+            "orphan_labels": len(orphan_labels),
+        }},
+        "train_files": train_rows,
+        "val_files": val_rows,
+        "missing_labels": missing_labels,
+        "orphan_labels": orphan_labels,
+    }}
+
+    logs_root = destination_root / "logs"
+    if not args.dry_run:
+        logs_root.mkdir(parents=True, exist_ok=True)
+        stamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+        json_log = logs_root / f"dataset_preparation_{{stamp}}.json"
+        txt_log = logs_root / f"dataset_preparation_{{stamp}}.txt"
+        json_log.write_text(json.dumps(metadata, indent=2), encoding="utf-8")
+        txt_log.write_text(build_text_log(metadata), encoding="utf-8")
+        print(f"Wrote logs: {{json_log}} and {{txt_log}}")
+        print(f"Wrote training script: {{train_script_path}}")
+    else:
+        print("Dry run enabled: files were not copied and logs were not written.")
+        print("Summary:")
+        print(json.dumps(metadata["totals"], indent=2))
+
+    print(
+        f"Prepared dataset split from {{source_root}} -> {{destination_root}} "
+        f"(train={{len(train_pairs)}}, val={{len(val_pairs)}}, total={{len(pairs)}})."
+    )
+
+
+if __name__ == "__main__":
+    main()
+'''
+        return textwrap.dedent(script)
diff --git a/src/gui/ml_gui.py b/src/gui/ml_gui.py
new file mode 100644
index 0000000..b8dbb34
--- /dev/null
+++ b/src/gui/ml_gui.py
@@ -0,0 +1,2522 @@
+"""AareLC ML Studio - Main GUI Application."""
+import os
+import time
+import json
+import tkinter as tk
+import urllib.parse
+from tkinter import ttk
+from tkinter import filedialog, messagebox, simpledialog
+import cv2
+import requests
+from collections import deque
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+from PIL import Image, ImageTk
+
+# Import modular components
+from src.gui.canvas_panel import MainCanvas
+from src.gui.control_panel import ControlPanel
+from src.gui.class_bank_mixin import ClassBankMixin
+from src.gui.dataset_prep_mixin import DatasetPrepMixin
+from src.gui.canvas_interaction_mixin import CanvasInteractionMixin
+from src.gui.review_mixin import ReviewMixin
+from src.core.zmq_client import ZMQStreamClient
+from src.core.inference_client import InferenceClient
+from src.core.db_client import DatabaseClient
+from src.core.image_processor import ImageProcessor
+
+AAREDB_SHARED_PASSWORD = os.getenv('AAREDB_SHARED_PASSWORD')
+ZMQ_CONTROL_TOKEN = os.getenv('AARELC_ZMQ_CONTROL_TOKEN')
+
+
+class InferenceGUI(ClassBankMixin, DatasetPrepMixin, CanvasInteractionMixin, ReviewMixin):
+    """Main GUI application for ML inference and annotation."""
+
+    def __init__(self, root):
+        self.root = root
+        self.root.title("AareLC ML Studio")
+        self.root.geometry("1400x900")
+
+        # --- Clients ---
+        self.zmq_client = None
+        self.inference_client = None
+        self.db_client = None
+        self.image_processor = ImageProcessor()
+
+        # --- Settings ---
+        self.pref_inference_url = tk.StringVar(value="http://mx-ml.psi.ch:8002")
+        self.pref_db_url = tk.StringVar(value="https://mx-db-01.psi.ch")
+        self.pref_download_url = tk.StringVar(value="http://localhost:8002/backend")
+        self.pref_zmq_control_url = tk.StringVar(value="http://localhost:8090")
+        self.pref_zmq_control_token = tk.StringVar(value=ZMQ_CONTROL_TOKEN)
+        self.pref_shared_pw = tk.StringVar(value=AAREDB_SHARED_PASSWORD)
+        self.pref_username = tk.StringVar(value=os.getenv('USER', 'anonymous'))
+        self.zmq_det_url = tk.StringVar(value="tcp://localhost:9091")
+        self.focus_enabled_var = tk.BooleanVar(value=True)
+        self.pref_zmq_stream_options = [
+            "tcp://localhost:9091",
+            "tcp://x10sa-spark-01:9091",
+            "tcp://sls-gpu-003:9089",
+        ]
+        self.pref_zmq_control_options = [
+            "http://localhost:8090",
+            "http://x10sa-spark-01:8090",
+            "http://sls-gpu-003:8090",
+        ]
+        self.pref_download_url_options = [
+            "http://localhost:8002/backend",
+            "http://mx-db-01:8002/backend",
+        ]
+
+        # --- Sync filters ---
+        self.sync_since = tk.StringVar(value="")
+        self.sync_until = tk.StringVar(value="")
+        self.sync_limit = tk.IntVar(value=5000)
+        self.sync_include_unannotated = tk.BooleanVar(value=False)
+        self.sync_dry_run = tk.BooleanVar(value=True)
+        self.review_mode = False
+        self.review_items = []
+        self.review_index = -1
+        self.current_review_item = None
+        self.review_format = None
+        self.review_coco_path = None
+        self.review_coco_data = None
+
+        # --- State ---
+        self.current_raw_frame = None
+        self.current_image_id = None  # Track current image ID from database
+        self.current_annotation_id = None  # Track annotation ID for this image
+        self.last_results = []
+        self._last_overlap_pick = None
+        self.selected_island_idx = 0
+        self.selected_island_kind = "outer"
+        self.history = deque(maxlen=20)  # NEW: Store last 20 states
+        self.focus_history = deque(maxlen=500)
+        self.stream_out_timestamps = deque(maxlen=240)
+        self.stream_fps_in = 0.0
+        self.stream_fps_out = 0.0
+        self.stream_fps_pred = 0.0
+        self._last_server_frame_id = None
+        self._last_server_frame_ts = None
+        # UI render cap for live stream; 0 means uncapped while still dropping stale frames.
+        # Override with env var AARELC_STREAM_UI_MAX_FPS (e.g. 8, 15, 0).
+        try:
+            self.stream_ui_max_fps = float(os.getenv("AARELC_STREAM_UI_MAX_FPS", "0"))
+        except ValueError:
+            self.stream_ui_max_fps = 0.0
+        if self.stream_ui_max_fps > 0:
+            print(f"INFO: Stream UI FPS cap enabled at {self.stream_ui_max_fps:.1f} FPS", flush=True)
+        else:
+            print("INFO: Stream UI FPS cap disabled (uncapped)", flush=True)
+        self._last_ui_render_ts = 0.0
+        self._pending_stream_frame = None
+        self._pending_stream_detections = None
+        self._pending_stream_metadata = None
+        self._last_stream_metadata = {}
+        self._stream_render_scheduled = False
+
+        # Status Bar Variables
+        self.status_var = tk.StringVar(value="Ready")
+        # Setup status truncation early to prevent X11 errors
+        self._setup_status_truncation()
+        self.local_output_dir = Path("data/custom_annotations")
+        self.current_local_image_path = None
+        self.class_set_source_path = None
+        self.element_bank_path = self.local_output_dir / "element_bank.json"
+        self.element_bank = []
+        self.annotation_mode = "yolo_single_polygon"
+        self.annotation_mode_labels = {
+            "yolo_single_polygon": "YOLO single polygon",
+            "coco_multi_polygon": "COCO multi-polygon",
+        }
+
+        # YOLO segmentation classes
+        self.class_params = {
+            "loop_all": {"low": 50, "high": 150, "clahe": 3.0, "morph": 7, "eps": 0.01},
+            "pin": {"low": 100, "high": 200, "clahe": 2.0, "morph": 5, "eps": 0.002},
+            "crystal": {"low": 50, "high": 150, "clahe": 3.0, "morph": 11, "eps": 0.01},
+            "loop_face": {"low": 30, "high": 100, "clahe": 4.0, "morph": 3, "eps": 0.005},
+            "ice": {"low": 50, "high": 150, "clahe": 3.0, "morph": 7, "eps": 0.01},
+            "needle": {"low": 50, "high": 150, "clahe": 3.0, "morph": 7, "eps": 0.01}
+        }
+
+        # Color scheme mapping (BGR for OpenCV) for YOLO classes
+        self.class_colors = {
+            "loop_all": (0, 255, 0),  # Blue
+            "pin": (0, 0, 255),  # Green
+            "crystal": (255, 0, 0),  # Red
+            "loop_face": (0, 255, 255),  # Yellow
+            "ice": (255, 255, 255),  # White/Cyan
+            "needle": (128, 0, 255)
+        }
+
+        # Map class names to YOLO class IDs (0-indexed for YOLO format)
+        self.class_to_id = {
+            "loop_all": 0,
+            "pin": 1,
+            "crystal": 2,
+            "loop_face": 3,
+            "ice": 4,
+            "needle": 5,
+        }
+        self.id_to_class = {v: k for k, v in self.class_to_id.items()}
+        self._class_color_palette = [
+            (0, 255, 0),
+            (0, 0, 255),
+            (255, 0, 0),
+            (0, 255, 255),
+            (255, 255, 255),
+            (128, 0, 255),
+            (255, 165, 0),
+            (255, 0, 255),
+            (0, 128, 255),
+            (128, 255, 0),
+        ]
+        self._visibility_trace_bound_classes = set()
+
+        # Tkinter variables for control panel
+        self.tk_vars = {
+            'active_class': tk.StringVar(value=self._default_class_name()),
+            'active_tool': tk.StringVar(value="select"), # select, brush, sam, or measure
+            'canny_high': tk.IntVar(value=150),
+            'morph_kernel': tk.IntVar(value=7),
+            'poly_epsilon': tk.DoubleVar(value=0.01),
+            'show_debug_edges': tk.BooleanVar(value=False),
+            'plot_window_size': tk.IntVar(value=100),
+            'annotation_line_width': tk.IntVar(value=2),
+            'show_boxes': tk.BooleanVar(value=True),
+            'show_segments': tk.BooleanVar(value=True),
+            'brush_mode': tk.BooleanVar(value=False),
+            'brush_type': tk.StringVar(value='background'),
+            'brush_size': tk.IntVar(value=15),
+            'segmentation_method': tk.StringVar(value='smart')  # Default to smart!
+        }
+
+        # Brush strokes storage: detection_idx -> {'bg': [], 'fg': []}
+        self.brush_strokes = {}
+        self.analysis_state = {
+            'show_grid': False,
+            'show_crosshair': True,
+            'cursor': (0, 0),
+            'line_start': None,
+            'line_end': None,
+            'line_dragging': False,
+            'roi_start': None,
+            'roi_end': None,
+            'roi_dragging': False,
+        }
+        self.depth_backend_var = tk.StringVar(value=os.getenv("AARELC_DEPTH_BACKEND", "depthpro"))
+        self.depth_max_side_var = tk.IntVar(value=int(os.getenv("AARELC_DEPTH_MAX_SIDE", "0")))
+        self.depth_preview_window = None
+        self.depth_preview_label = None
+        self.depth_preview_photo = None
+
+        # Initialize clients
+        self._init_clients()
+
+        # Setup UI
+        self._setup_menus()
+        self._setup_ui()
+        self._load_default_element_bank_if_exists()
+
+        # Status bar
+        self.status_bar = tk.Label(
+            self.root,
+            textvariable=self.status_var,
+            bd=1,
+            relief=tk.SUNKEN,
+            anchor=tk.W,
+            font="fixed"
+        )
+        self.status_bar.pack(side=tk.BOTTOM, fill=tk.X)
+
+        # Setup parameter traces
+        self._setup_traces()
+        print(f"AAREDB_SHARED_PASSWORD={AAREDB_SHARED_PASSWORD}")
+        print(f"AARELC_ZMQ_CONTROL_TOKEN={ZMQ_CONTROL_TOKEN}")
+
+    def _init_clients(self):
+        """Initialize client objects."""
+        self.inference_client = InferenceClient(self.pref_inference_url.get())
+        self.db_client = DatabaseClient(
+            self.pref_db_url.get(), 
+            self.pref_shared_pw.get(),
+            download_base=self.pref_download_url.get()
+        )
+
+    def _setup_menus(self):
+        """Setup menu bar."""
+        menubar = tk.Menu(self.root)
+
+        # File menu
+        file_menu = tk.Menu(menubar, tearoff=0)
+        file_menu.add_command(label="Load Image from Disk...", command=self.load_local_image)
+        file_menu.add_command(label="Fetch Next from DB", command=self.fetch_db_image)
+        file_menu.add_separator()
+        file_menu.add_command(label="Set Local Save Folder...", command=self.choose_local_output_dir)
+        file_menu.add_separator()
+        file_menu.add_command(label="Add Class...", command=self.add_single_class)
+        file_menu.add_command(label="Create New Class Set...", command=self.create_new_class_set)
+        file_menu.add_command(label="Load Class Set from Disk...", command=self.load_class_set_from_disk)
+        file_menu.add_command(label="Save Class Set As...", command=self.save_class_set_as)
+        file_menu.add_separator()
+        file_menu.add_command(label="Exit", command=self.root.quit)
+        menubar.add_cascade(label="File", menu=file_menu)
+
+        # Preferences menu
+        pref_menu = tk.Menu(menubar, tearoff=0)
+        pref_menu.add_command(label="Server Settings...", command=self.show_preferences)
+        pref_menu.add_command(label="ZMQ Inference Control...", command=self.show_zmq_inference_control_dialog)
+        menubar.add_cascade(label="Preferences", menu=pref_menu)
+
+        # Sync menu
+        sync_menu = tk.Menu(menubar, tearoff=0)
+        sync_menu.add_command(
+            label="Retrieve Images + Labels Locally...",
+            command=self.show_sync_dialog,
+        )
+        sync_menu.add_command(
+            label="Generate YOLO Segmentation Prep Script...",
+            command=self.show_dataset_prep_script_dialog,
+        )
+        menubar.add_cascade(label="Sync", menu=sync_menu)
+
+        # Review menu
+        review_menu = tk.Menu(menubar, tearoff=0)
+        review_menu.add_command(
+            label="Open Review Folder...",
+            command=self.open_review_folder,
+        )
+        review_menu.add_command(label="Previous Review Image (P)", command=self.prev_review_image)
+        review_menu.add_command(label="Next Review Image (N)", command=self.next_review_image)
+        review_menu.add_separator()
+        review_menu.add_command(label="Save Current Review Annotation", command=self.save_review_annotation)
+        review_menu.add_command(label="Exit Review Mode", command=self.exit_review_mode)
+        menubar.add_cascade(label="Review", menu=review_menu)
+
+        # Elements menu
+        elements_menu = tk.Menu(menubar, tearoff=0)
+        elements_menu.add_command(label="Add Selected to Bank...", command=self.add_selected_to_element_bank)
+        elements_menu.add_command(label="Place Element from Bank...", command=self.place_element_from_bank)
+        elements_menu.add_separator()
+        elements_menu.add_command(label="Load Element Bank...", command=self.load_element_bank_from_disk)
+        elements_menu.add_command(label="Save Element Bank As...", command=self.save_element_bank_as)
+        menubar.add_cascade(label="Elements", menu=elements_menu)
+
+        self.root.config(menu=menubar)
+
+    def _setup_ui(self):
+        """Setup main UI layout."""
+        # 1. Top toolbars (two rows to avoid clipping on smaller screens)
+        self.top_bar = tk.Frame(self.root, bg="#eee", pady=5, relief=tk.RAISED, bd=1)
+        self.top_bar.pack(side=tk.TOP, fill=tk.X)
+        self.top_bar_aux = tk.Frame(self.root, bg="#f3f3f3", pady=4, relief=tk.RAISED, bd=1)
+        self.top_bar_aux.pack(side=tk.TOP, fill=tk.X)
+
+        # Removed ZMQ Entry from here
+
+        # NEW: Tool Palette Frame (Left side of toolbar)
+        self.tool_frame = tk.Frame(self.top_bar, bg="#ddd", padx=5)
+        self.tool_frame.pack(side=tk.LEFT, padx=5)
+
+        self.select_tool_btn = tk.Radiobutton(
+            self.tool_frame, text="Select", variable=self.tk_vars['active_tool'],
+            value="select", indicatoron=0, width=8, padx=5, pady=2
+        )
+        self.select_tool_btn.pack(side=tk.LEFT)
+
+        self.brush_tool_btn = tk.Radiobutton(
+            self.tool_frame, text="Brush", variable=self.tk_vars['active_tool'],
+            value="brush", indicatoron=0, width=8, padx=5, pady=2
+        )
+        self.brush_tool_btn.pack(side=tk.LEFT)
+
+        self.sam_tool_btn = tk.Radiobutton(
+            self.tool_frame, text="SAM", variable=self.tk_vars['active_tool'],
+            value="sam", indicatoron=0, width=8, padx=5, pady=2
+        )
+        self.sam_tool_btn.pack(side=tk.LEFT)
+
+        self.measure_tool_btn = tk.Radiobutton(
+            self.tool_frame, text="Measure", variable=self.tk_vars['active_tool'],
+            value="measure", indicatoron=0, width=10, padx=5, pady=2
+        )
+        self.measure_tool_btn.pack(side=tk.LEFT)
+        
+        tk.Label(self.top_bar, text="|", bg="#eee").pack(side=tk.LEFT, padx=5)
+        
+        # Connect button
+        self.stream_btn = tk.Button(self.top_bar, text="Connect Live", command=self.toggle_stream, width=15)
+        self.stream_btn.pack(side=tk.LEFT, padx=5)
+
+        self.focus_btn = tk.Button(
+            self.top_bar,
+            text="Focus ON",
+            command=self.toggle_focus_stream,
+            width=12,
+            bg="#17a2b8",
+            fg="white"
+        )
+        self.focus_btn.pack(side=tk.LEFT, padx=5)
+
+        # Database Fetch button (Next Random Image)
+        self.db_next_btn = tk.Button(self.top_bar, text="Next Image", command=self.fetch_db_image, bg="#28a745",
+                                     fg="white")
+        self.db_next_btn.pack(side=tk.LEFT, padx=5)
+
+        # Skip Image button
+        self.skip_btn = tk.Button(self.top_bar, text="Skip", command=self.skip_current_image, bg="#ffc107",
+                                  fg="black")
+        self.skip_btn.pack(side=tk.LEFT, padx=2)
+
+        # Predict button
+        self.predict_btn = tk.Button(self.top_bar, text="Predict (/predict)", command=self.request_inference,
+                                     bg="#007bff", fg="white")
+        self.predict_btn.pack(side=tk.LEFT, padx=20)
+        self.depth_btn = tk.Button(
+            self.top_bar,
+            text="Depth Map",
+            command=self.request_depth_map,
+            bg="#5a3d00",
+            fg="white"
+        )
+        self.depth_btn.pack(side=tk.LEFT, padx=5)
+        self.sam_boxes_btn = tk.Button(
+            self.top_bar_aux,
+            text="SAM from Boxes",
+            command=self.refine_boxes_with_sam,
+            bg="#17a2b8",
+            fg="white"
+        )
+        self.sam_boxes_btn.pack(side=tk.LEFT, padx=5)
+        tk.Label(self.top_bar_aux, text="Depth:", bg="#f3f3f3").pack(side=tk.LEFT, padx=(8, 2))
+        self.depth_backend_combo = ttk.Combobox(
+            self.top_bar_aux,
+            textvariable=self.depth_backend_var,
+            values=[
+                "depthpro",
+                "depth_anything_v1",
+                "depth_anything_v1_trt",
+                "depth_anything_v2",
+                "depth_anything_v2_tiny",
+                "depth_anything_v2_trt",
+                "midas_v21_small",
+            ],
+            width=20,
+            state="readonly"
+        )
+        self.depth_backend_combo.pack(side=tk.LEFT, padx=2)
+        tk.Label(self.top_bar_aux, text="Max side:", bg="#f3f3f3").pack(side=tk.LEFT, padx=(6, 2))
+        self.depth_max_side_combo = ttk.Combobox(
+            self.top_bar_aux,
+            textvariable=self.depth_max_side_var,
+            values=[0, 384, 512, 640, 768, 1024],
+            width=6,
+            state="readonly"
+        )
+        self.depth_max_side_combo.pack(side=tk.LEFT, padx=2)
+
+        # NEW: Zoom Slider in Toolbar
+        tk.Label(self.top_bar_aux, text="Zoom:", bg="#f3f3f3").pack(side=tk.LEFT, padx=(10, 2))
+        self.zoom_var = tk.DoubleVar(value=1.0)
+        self.zoom_slider = tk.Scale(
+            self.top_bar_aux, from_=0.1, to=5.0, resolution=0.1,
+            orient=tk.HORIZONTAL, variable=self.zoom_var,
+            showvalue=False, width=10, length=120, bg="#f3f3f3",
+            command=self._on_zoom_slider_change
+        )
+        self.zoom_slider.pack(side=tk.LEFT, padx=5)
+
+        # NEW: SAM refinement toggle
+        self.use_sam_refine = tk.BooleanVar(value=False)
+        self.sam_refine_check = tk.Checkbutton(
+            self.top_bar_aux, text="Use SAM Refinement", variable=self.use_sam_refine,
+            bg="#f3f3f3", font=('Arial', 9)
+        )
+        self.sam_refine_check.pack(side=tk.LEFT, padx=5)
+
+        self.show_boxes_check = tk.Checkbutton(
+            self.top_bar_aux,
+            text="Show Boxes",
+            variable=self.tk_vars['show_boxes'],
+            bg="#f3f3f3",
+            font=('Arial', 9)
+        )
+        self.show_boxes_check.pack(side=tk.LEFT, padx=4)
+
+        self.show_segments_check = tk.Checkbutton(
+            self.top_bar_aux,
+            text="Show Segments",
+            variable=self.tk_vars['show_segments'],
+            bg="#f3f3f3",
+            font=('Arial', 9)
+        )
+        self.show_segments_check.pack(side=tk.LEFT, padx=4)
+
+        # 2. Main content area
+        self.content_frame = tk.Frame(self.root)
+        self.content_frame.pack(fill=tk.BOTH, expand=True)
+
+        # 3. Control panel (right side)
+        self.ctrl_panel = ControlPanel(
+            self.content_frame,
+            self.class_params,
+            self.tk_vars,
+            callbacks={
+                'on_train_click': self.send_to_training,
+                'on_clear_brush': self.clear_brush_strokes,
+                'on_regenerate_edge': self.regenerate_selected_edge
+            }
+        )
+        self.ctrl_panel.pack(side=tk.RIGHT, fill=tk.Y)
+
+        # Initialize class visibility checkboxes
+        self.ctrl_panel.update_class_visibility(list(self.class_params.keys()), self.class_colors)
+        self.ctrl_panel.update_class_selector(list(self.class_params.keys()))
+        self._bind_class_visibility_traces()
+
+        # Add trace to redraw when brush mode changes
+        self.tk_vars['brush_mode'].trace_add("write", lambda *args: self.redraw_current_frame())
+
+        # 4. Canvas (center)
+        self.canvas = MainCanvas(self.content_frame)
+        # FIX: Use _on_canvas_interaction, which handles click/drag arguments
+        self.canvas.set_redraw_callback(self._on_canvas_interaction)
+        self.canvas.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
+
+        self.add_box_btn = tk.Button(
+            self.top_bar,
+            text="Add Box",
+            command=self.add_new_box,
+            bg="#6c757d",
+            fg="white"
+        )
+        self.add_box_btn.pack(side=tk.LEFT, padx=5)
+        self.add_island_btn = tk.Button(
+            self.top_bar_aux,
+            text="Island",
+            command=self.add_island_to_selected,
+            bg="#6c757d",
+            fg="white"
+        )
+        self.add_island_btn.pack(side=tk.LEFT, padx=2)
+        self.add_hole_btn = tk.Button(
+            self.top_bar_aux,
+            text="Add Hole",
+            command=self.add_hole_to_selected,
+            bg="#6c757d",
+            fg="white"
+        )
+        self.add_hole_btn.pack(side=tk.LEFT, padx=2)
+        self.next_island_btn = tk.Button(
+            self.top_bar_aux,
+            text="Cycle Island",
+            command=self.cycle_selected_island,
+            bg="#f8f9fa"
+        )
+        self.next_island_btn.pack(side=tk.LEFT, padx=2)
+        self.remove_island_btn = tk.Button(
+            self.top_bar_aux,
+            text="Remove Island",
+            command=self.remove_selected_island,
+            bg="#f8f9fa"
+        )
+        self.remove_island_btn.pack(side=tk.LEFT, padx=2)
+
+        # NEW: Undo Button
+        self.undo_btn = tk.Button(
+            self.top_bar,
+            text="Undo",
+            command=self.undo_action,
+            bg="#f8f9fa"
+        )
+        self.undo_btn.pack(side=tk.LEFT, padx=5)
+
+    def _on_zoom_slider_change(self, val):
+        """Sync slider value to canvas zoom level."""
+        self.canvas.zoom_level = float(val)
+        self.redraw_current_frame()
+
+    def _setup_status_truncation(self):
+        """Setup status message truncation to prevent X11 BadLength errors."""
+        def truncate_status(*args):
+            val = self.status_var.get()
+            if len(val) > 100:
+                truncated = val[:97] + "..."
+                if val != truncated:
+                    self.status_var.set(truncated)
+
+        self.status_var.trace_add("write", truncate_status)
+
+    def _setup_traces(self):
+        """Setup variable traces for parameter changes."""
+        self._after_id = None  # NEW: Track the debouncing timer
+        for var_name in ['canny_high', 'morph_kernel', 'poly_epsilon']:
+            self.tk_vars[var_name].trace_add("write", self.on_param_change)
+
+        self.tk_vars['active_class'].trace_add("write", self.on_class_switch)
+        self.tk_vars['active_tool'].trace_add("write", self._on_tool_switch)
+        self.tk_vars['show_debug_edges'].trace_add("write", lambda *args: self.redraw_current_frame())
+        self.tk_vars['plot_window_size'].trace_add("write", lambda *args: self.redraw_focus_plot())
+        self.tk_vars['annotation_line_width'].trace_add("write", lambda *args: self.redraw_current_frame())
+        self.tk_vars['show_boxes'].trace_add("write", lambda *args: self.redraw_current_frame())
+        self.tk_vars['show_segments'].trace_add("write", lambda *args: self.redraw_current_frame())
+        self.root.bind("<a>", lambda e: self.add_new_box())
+        self.root.bind("<Delete>", self.delete_selected)
+        self.root.bind("<Control-z>", lambda e: self.undo_action())  # NEW: Ctrl+Z shortcut
+        self.root.bind("<Command-z>", lambda e: self.undo_action())  # NEW: Cmd+Z for macOS
+        self.root.bind("<i>", lambda e: self.cycle_selected_island())
+        # Review navigation shortcuts
+        self.root.bind("<Right>", self._on_review_next_shortcut)
+        self.root.bind("<Left>", self._on_review_prev_shortcut)
+        self.root.bind("<n>", self._on_review_next_shortcut)
+        self.root.bind("<p>", self._on_review_prev_shortcut)
+        self.root.bind("<N>", self._on_review_next_shortcut)
+        self.root.bind("<P>", self._on_review_prev_shortcut)
+        self.root.bind("<g>", self._toggle_analysis_grid)
+        self.root.bind("<c>", self._toggle_analysis_crosshair)
+        self.root.bind("<x>", self._clear_measurements)
+
+        # Global safety: Truncate any status message to 100 chars to avoid X11 BadLength
+        # Moved to _setup_status_truncation for early setup
+
+    def _default_class_name(self):
+        """Return first available class name."""
+        return next(iter(self.class_to_id.keys()), "class_0")
+
+    @staticmethod
+    def _default_class_params():
+        """Default contour extraction params for newly created classes."""
+        return {"low": 50, "high": 150, "clahe": 3.0, "morph": 7, "eps": 0.01}
+
+    def _is_coco_multi_mode(self):
+        """Whether current class set uses COCO multi-polygon mode."""
+        return self.annotation_mode == "coco_multi_polygon"
+
+    def _get_annotation_mode_label(self):
+        return self.annotation_mode_labels.get(self.annotation_mode, self.annotation_mode)
+
+    def _prompt_annotation_mode(self):
+        """Ask user which annotation mode should be used for a new class set."""
+        choice = messagebox.askyesnocancel(
+            "Annotation Mode",
+            (
+                "Enable COCO multi-polygon mode for this class set?\n\n"
+                "Yes: COCO multi-polygon (supports multi-island instances, local COCO save)\n"
+                "No: YOLO single-polygon (current/default behavior)\n"
+                "Cancel: abort"
+            ),
+            parent=self.root,
+        )
+        if choice is None:
+            return None
+        return "coco_multi_polygon" if choice else "yolo_single_polygon"
+
+    def _det_get_polygons_relative(self, det):
+        """Return list of relative polygons in (N,1,2) float32 format."""
+        polys = det.get("polygons")
+        out = []
+        if isinstance(polys, list):
+            for poly in polys:
+                arr = np.array(poly, dtype=np.float32).reshape(-1, 1, 2)
+                if len(arr) >= 3:
+                    out.append(arr)
+        if out:
+            return out
+        poly = det.get("poly")
+        if poly is not None and len(poly) >= 3:
+            out.append(np.array(poly, dtype=np.float32).reshape(-1, 1, 2))
+        return out
+
+    def _det_get_holes_relative(self, det):
+        """Return list of relative hole polygons in (N,1,2) float32 format."""
+        holes = det.get("holes")
+        out = []
+        if isinstance(holes, list):
+            for poly in holes:
+                arr = np.array(poly, dtype=np.float32).reshape(-1, 1, 2)
+                if len(arr) >= 3:
+                    out.append(arr)
+        return out
+
+    def _det_has_polygon(self, det):
+        """True if detection has at least one explicit polygon island."""
+        return len(self._det_get_polygons_relative(det)) > 0
+
+    def _set_det_single_polygon(self, det, poly_arr):
+        """Set detection polygon in both legacy and multi-poly fields."""
+        arr = np.array(poly_arr, dtype=np.float32).reshape(-1, 1, 2)
+        det["poly"] = arr
+        det["polygons"] = [arr]
+
+    def _set_det_polygons(self, det, polygon_list):
+        """Set detection polygons list, updating legacy primary poly too."""
+        cleaned = []
+        for poly in polygon_list:
+            arr = np.array(poly, dtype=np.float32).reshape(-1, 1, 2)
+            if len(arr) >= 3:
+                cleaned.append(arr)
+        if cleaned:
+            det["polygons"] = cleaned
+            det["poly"] = cleaned[0]
+        else:
+            det.pop("polygons", None)
+            det.pop("poly", None)
+
+    def _set_det_holes(self, det, hole_list):
+        """Set detection hole polygons list."""
+        cleaned = []
+        for poly in hole_list:
+            arr = np.array(poly, dtype=np.float32).reshape(-1, 1, 2)
+            if len(arr) >= 3:
+                cleaned.append(arr)
+        if cleaned:
+            det["holes"] = cleaned
+        else:
+            det.pop("holes", None)
+
+    def _det_get_absolute_polygons(self, det):
+        """Return polygon islands as absolute image coordinates."""
+        box = det.get("box", det)
+        bx1 = float(box.get("x1", 0.0))
+        by1 = float(box.get("y1", 0.0))
+        polygons_abs = []
+        for rel in self._det_get_polygons_relative(det):
+            pts = rel.reshape(-1, 2).astype(np.float32) + [bx1, by1]
+            if len(pts) >= 3:
+                polygons_abs.append(pts)
+        return polygons_abs
+
+    def _det_get_absolute_holes(self, det):
+        """Return hole polygons as absolute image coordinates."""
+        box = det.get("box", det)
+        bx1 = float(box.get("x1", 0.0))
+        by1 = float(box.get("y1", 0.0))
+        holes_abs = []
+        for rel in self._det_get_holes_relative(det):
+            pts = rel.reshape(-1, 2).astype(np.float32) + [bx1, by1]
+            if len(pts) >= 3:
+                holes_abs.append(pts)
+        return holes_abs
+
+    def _get_active_polygon(self, det):
+        """Get currently selected polygon island for a detection."""
+        rings = self._det_get_polygons_relative(det) if self.selected_island_kind == "outer" else self._det_get_holes_relative(det)
+        if not rings:
+            self.selected_island_kind = "outer"
+            rings = self._det_get_polygons_relative(det)
+            if not rings:
+                return None
+        idx = int(max(0, min(self.selected_island_idx, len(rings) - 1)))
+        self.selected_island_idx = idx
+        return rings[idx]
+
+    def add_island_to_selected(self):
+        """Add a new polygon island inside selected detection box."""
+        idx = self.canvas.selected_obj_idx
+        if idx == -1 or idx >= len(self.last_results):
+            self.status_var.set("Select an instance first.")
+            return
+        det = self.last_results[idx]
+        box = det.get('box', det)
+        x1, y1, x2, y2 = float(box['x1']), float(box['y1']), float(box['x2']), float(box['y2'])
+        w = max(6.0, x2 - x1)
+        h = max(6.0, y2 - y1)
+        cx, cy = w * 0.5, h * 0.5
+        rw, rh = max(4.0, w * 0.2), max(4.0, h * 0.2)
+        poly = np.array(
+            [[[cx - rw, cy - rh]], [[cx + rw, cy - rh]], [[cx + rw, cy + rh]], [[cx - rw, cy + rh]]],
+            dtype=np.float32
+        )
+        polygons = self._det_get_polygons_relative(det)
+        polygons.append(poly)
+        self._save_history()
+        self._set_det_polygons(det, polygons)
+        self.selected_island_idx = len(polygons) - 1
+        self.canvas.selected_point_idx = -1
+        self.redraw_current_frame()
+        self.status_var.set(f"Added island {self.selected_island_idx + 1}/{len(polygons)}")
+
+    def add_hole_to_selected(self):
+        """Add a new hole polygon inside selected detection box."""
+        idx = self.canvas.selected_obj_idx
+        if idx == -1 or idx >= len(self.last_results):
+            self.status_var.set("Select an instance first.")
+            return
+        det = self.last_results[idx]
+        box = det.get('box', det)
+        x1, y1, x2, y2 = float(box['x1']), float(box['y1']), float(box['x2']), float(box['y2'])
+        w = max(10.0, x2 - x1)
+        h = max(10.0, y2 - y1)
+        cx, cy = w * 0.5, h * 0.5
+        rw, rh = max(3.0, w * 0.12), max(3.0, h * 0.12)
+        hole = np.array(
+            [[[cx - rw, cy - rh]], [[cx + rw, cy - rh]], [[cx + rw, cy + rh]], [[cx - rw, cy + rh]]],
+            dtype=np.float32
+        )
+        holes = self._det_get_holes_relative(det)
+        holes.append(hole)
+        self._save_history()
+        self._set_det_holes(det, holes)
+        self.selected_island_kind = "hole"
+        self.selected_island_idx = len(holes) - 1
+        self.canvas.selected_point_idx = -1
+        self.redraw_current_frame()
+        self.status_var.set(f"Added hole {self.selected_island_idx + 1}/{len(holes)}")
+
+    def cycle_selected_island(self):
+        """Cycle active island on selected detection."""
+        idx = self.canvas.selected_obj_idx
+        if idx == -1 or idx >= len(self.last_results):
+            self.status_var.set("Select an instance first.")
+            return
+        det = self.last_results[idx]
+        outers = self._det_get_polygons_relative(det)
+        holes = self._det_get_holes_relative(det)
+        if not outers and not holes:
+            self.status_var.set("No rings on selected instance.")
+            return
+
+        if self.selected_island_kind == "outer":
+            if outers:
+                self.selected_island_idx = (self.selected_island_idx + 1) % len(outers)
+                if self.selected_island_idx == 0 and holes:
+                    self.selected_island_kind = "hole"
+                    self.selected_island_idx = 0
+            else:
+                self.selected_island_kind = "hole"
+                self.selected_island_idx = (self.selected_island_idx + 1) % len(holes)
+        else:
+            if holes:
+                self.selected_island_idx = (self.selected_island_idx + 1) % len(holes)
+                if self.selected_island_idx == 0 and outers:
+                    self.selected_island_kind = "outer"
+                    self.selected_island_idx = 0
+            else:
+                self.selected_island_kind = "outer"
+                self.selected_island_idx = (self.selected_island_idx + 1) % len(outers)
+
+        self.canvas.selected_point_idx = -1
+        self.redraw_current_frame()
+        total = len(self._det_get_polygons_relative(det)) if self.selected_island_kind == "outer" else len(self._det_get_holes_relative(det))
+        self.status_var.set(f"Active {self.selected_island_kind} {self.selected_island_idx + 1}/{max(1, total)}")
+
+    def remove_selected_island(self):
+        """Remove active ring from selected detection."""
+        idx = self.canvas.selected_obj_idx
+        if idx == -1 or idx >= len(self.last_results):
+            self.status_var.set("Select an instance first.")
+            return
+        det = self.last_results[idx]
+        if self.selected_island_kind == "hole":
+            holes = self._det_get_holes_relative(det)
+            if not holes:
+                self.status_var.set("No holes to remove.")
+                return
+            self._save_history()
+            del holes[self.selected_island_idx]
+            self.selected_island_idx = max(0, min(self.selected_island_idx, len(holes) - 1))
+            self._set_det_holes(det, holes)
+            if not holes:
+                self.selected_island_kind = "outer"
+                self.selected_island_idx = 0
+        else:
+            polygons = self._det_get_polygons_relative(det)
+            if not polygons:
+                self.status_var.set("No outer islands to remove.")
+                return
+            if len(polygons) == 1:
+                messagebox.showwarning("Remove Island", "Instance has a single outer island. Delete instance if needed.")
+                return
+            self._save_history()
+            del polygons[self.selected_island_idx]
+            self.selected_island_idx = max(0, min(self.selected_island_idx, len(polygons) - 1))
+            self._set_det_polygons(det, polygons)
+        self.canvas.selected_point_idx = -1
+        self.redraw_current_frame()
+        self.status_var.set(f"Removed ring. Active {self.selected_island_kind} {self.selected_island_idx + 1}")
+
+    # --- Event Handlers ---
+
+    def on_class_switch(self, *args):
+        """Load stored parameters when the user switches classes in the dropdown."""
+        cls = self.tk_vars['active_class'].get()
+        p = self.class_params.get(cls, self._default_class_params())
+
+        # We use a flag to prevent on_param_change from being triggered
+        # while we are just updating the sliders to match the selected class
+        self._updating_ui = True
+        self.tk_vars['canny_high'].set(p["high"])
+        self.tk_vars['morph_kernel'].set(p["morph"])
+        self.tk_vars['poly_epsilon'].set(p["eps"])
+        self._updating_ui = False
+
+        self.redraw_current_frame()
+
+    def _on_tool_switch(self, *args):
+        """Sync canvas interaction mode with selected tool."""
+        is_measure = self.tk_vars['active_tool'].get() == "measure"
+        self.canvas.suspend_pan = is_measure
+        if is_measure:
+            self.status_var.set("Measure mode: left-drag line, right-drag ROI, g/c/x toggles")
+        self.redraw_current_frame()
+
+    def on_param_change(self, *args):
+        """Save slider values and CLEAR polygon ONLY for selected object."""
+        if getattr(self, '_updating_ui', False):
+            return
+
+        # Cancel previous scheduled redraw if user is still sliding
+        if self._after_id:
+            self.root.after_cancel(self._after_id)
+
+        def perform_update():
+            cls = self.tk_vars['active_class'].get()
+            self.class_params[cls].update({
+                "high": self.tk_vars['canny_high'].get(),
+                "morph": self.tk_vars['morph_kernel'].get(),
+                "eps": self.tk_vars['poly_epsilon'].get()
+            })
+
+            # ONLY clear polygon for the SELECTED object, not all objects of this class
+            if self.canvas.selected_obj_idx != -1 and self.canvas.selected_obj_idx < len(self.last_results):
+                det = self.last_results[self.canvas.selected_obj_idx]
+                label = det.get('name', det.get('label', self._default_class_name()))
+
+                # Only clear if the selected object matches the active class
+                if label == cls:
+                    if 'poly' in det or 'polygons' in det:
+                        det.pop('poly', None)
+                        det.pop('polygons', None)
+                        self.status_var.set(f"Parameters updated for selected {cls} - click Regenerate Edge")
+                else:
+                    self.status_var.set(f"Note: Selected object is '{label}', not '{cls}'. Switch class to edit.")
+            else:
+                self.status_var.set(f"Parameters saved for class '{cls}' - will apply to new detections")
+
+            self.redraw_current_frame()
+            self._after_id = None
+
+        # Delay execution by 150ms to wait for slider to settle
+        self._after_id = self.root.after(150, perform_update)
+
+    def _save_history(self):
+        """Save a deep copy of current detections to the history stack."""
+        import copy
+        # We store a copy of the list and the dictionaries inside
+        snapshot = copy.deepcopy(self.last_results)
+        self.history.append(snapshot)
+
+    def undo_action(self):
+        """Restore the last saved state from history."""
+        if not self.history:
+            self.status_var.set("Nothing to undo")
+            return
+
+        self.last_results = self.history.pop()
+        self.canvas.selected_obj_idx = -1
+        self.selected_island_idx = 0
+        self.selected_island_kind = "outer"
+        self.redraw_current_frame()
+        self.status_var.set("Action undone")
+
+    def _run_sam_inference(self, x, y):
+        """Send a point to the DGX SAM backend and create a new detection."""
+        if self.current_raw_frame is None: return
+
+        try:
+            self.root.config(cursor="watch")
+            self.status_var.set(f"SAM: Segmenting at [{int(x)}, {int(y)}]...")
+            self.root.update_idletasks()
+
+            result = self.inference_client.segment_anything(self.current_raw_frame, x, y)
+            poly_pts = result.get('polygon', [])
+
+            if not poly_pts:
+                self.status_var.set("SAM: No object found at that location.")
+                return
+
+            # Convert list of [x, y] to numpy format (N, 1, 2)
+            poly_arr = np.array(poly_pts, dtype=np.float32).reshape(-1, 1, 2)
+
+            # Calculate a bounding box for the polygon
+            x_coords = [p[0] for p in poly_pts]
+            y_coords = [p[1] for p in poly_pts]
+            x1, y1, x2, y2 = min(x_coords), min(y_coords), max(x_coords), max(y_coords)
+
+            # Polygons in our system are stored relative to box origin
+            poly_relative = poly_arr - [x1, y1]
+
+            new_det = {
+                "box": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
+                "name": self.tk_vars['active_class'].get(),
+                "poly": poly_relative.astype(np.float32),
+                "polygons": [poly_relative.astype(np.float32)],
+                "conf": result.get('score', 1.0)
+            }
+
+            self._save_history()
+            self.last_results.append(new_det)
+            self.canvas.selected_obj_idx = len(self.last_results) - 1
+            self.selected_island_kind = "outer"
+            self.redraw_current_frame()
+            self.status_var.set(f"SAM: Object segmented successfully ({len(poly_pts)} points).")
+
+        except Exception as e:
+            messagebox.showerror("SAM Error", str(e))
+            self.status_var.set("SAM inference failed.")
+        finally:
+            self.root.config(cursor="")
+
+    # --- Image Loading ---
+
+    def load_local_image(self):
+        """Load image from local file system."""
+        path = filedialog.askopenfilename(
+            filetypes=[("Images", "*.jpg *.jpeg *.png *.bmp")]
+        )
+        if path:
+            img = cv2.imread(path)
+            if img is not None:
+                self.exit_review_mode(silent=True)
+                self._stop_streaming()
+                self.current_raw_frame = img
+                self.current_image_id = None
+                self.current_annotation_id = None
+                self.current_local_image_path = Path(path)
+                self.last_results = []
+                self.selected_island_idx = 0
+                self.selected_island_kind = "outer"
+                self.redraw_current_frame()
+                self.status_var.set(f"Loaded: {path}")
+
+    def fetch_db_image(self):
+        """Fetch image from database server."""
+        try:
+            self.exit_review_mode(silent=True)
+            self.root.config(cursor="watch")
+            # Fetch returns (img, metadata)
+            result = self.db_client.fetch_next_raw_image()
+
+            # Check if result is just an image or (image, metadata)
+            if isinstance(result, tuple):
+                img, metadata = result
+                self.current_image_id = metadata.get('image', {}).get('id')
+                self.current_annotation_id = metadata.get('annotation_id')
+            else:
+                # Backwards compatibility - just image
+                img = result
+                self.current_image_id = None
+                self.current_annotation_id = None
+
+            self._stop_streaming()
+            self.current_raw_frame = img
+            self.current_local_image_path = None
+            self.last_results = []  # Clear previous annotations
+            self.selected_island_idx = 0
+            self.selected_island_kind = "outer"
+            self.redraw_current_frame()
+
+            status_msg = f"Fetched image from database"
+            if self.current_image_id:
+                status_msg += f" (ID: {self.current_image_id})"
+            self.status_var.set(status_msg)
+        except Exception as e:
+            messagebox.showerror("DB Error", str(e))
+            self.status_var.set("Database fetch failed")
+            import traceback
+            traceback.print_exc()
+        finally:
+            self.root.config(cursor="")
+
+    # --- Inference ---
+
+    def request_inference(self):
+        """Request /predict inference for the currently displayed frame snapshot."""
+        if self.current_raw_frame is None:
+            messagebox.showwarning("Warning", "No image loaded to predict.")
+            return
+
+        frame_for_predict = self.current_raw_frame.copy()
+        from_stream = isinstance(getattr(self, '_last_stream_metadata', None), dict) and bool(self._last_stream_metadata)
+        source_label = "live snapshot" if from_stream else "current image"
+
+        try:
+            self.root.config(cursor="watch")
+            self.root.update_idletasks()
+
+            self.status_var.set(f"Running /predict inference on {source_label}...")
+            self.root.update_idletasks()
+            self.last_results = self.inference_client.predict(frame_for_predict)
+
+            # Optional SAM refinement runs AFTER a fresh /predict call.
+            if self.use_sam_refine.get() and self.last_results:
+                refine_indices = [
+                    i for i, det in enumerate(self.last_results)
+                    if not self._det_has_polygon(det)
+                ]
+                if refine_indices:
+                    self.status_var.set(f"SAM: Auto-refining {len(refine_indices)} /predict detections...")
+                    self.root.update_idletasks()
+                    boxes_to_refine = []
+                    for i in refine_indices:
+                        b = self.last_results[i].get('box', self.last_results[i])
+                        boxes_to_refine.append([int(b['x1']), int(b['y1']), int(b['x2']), int(b['y2'])])
+
+                    sam_results = self.inference_client.segment_multi_box(frame_for_predict, boxes_to_refine)
+                    for local_idx, res in enumerate(sam_results):
+                        poly_pts = res.get('polygon', [])
+                        if not poly_pts:
+                            continue
+                        det_idx = refine_indices[local_idx]
+                        box = self.last_results[det_idx].get('box', self.last_results[det_idx])
+                        poly_arr = np.array(poly_pts, dtype=np.float32).reshape(-1, 1, 2)
+                        self._set_det_single_polygon(self.last_results[det_idx], poly_arr - [box['x1'], box['y1']])
+                        self.last_results[det_idx].pop('suppress_auto_polygon', None)
+
+            self.redraw_current_frame()
+            self.root.update_idletasks()
+            self.status_var.set(f"Prediction complete via /predict/. Found {len(self.last_results)} objects.")
+
+        except Exception as e:
+            messagebox.showerror("Inference Error", str(e))
+            self.status_var.set("Inference failed")
+        finally:
+            self.root.config(cursor="")
+
+    def request_depth_map(self):
+        """Request FastDepth map and show it in a dedicated preview window."""
+        if self.current_raw_frame is None:
+            messagebox.showwarning("Warning", "No image loaded for depth analysis.")
+            return
+
+        try:
+            self.root.config(cursor="watch")
+            backend = self.depth_backend_var.get().strip() or "depthpro"
+            depth_max_side = int(self.depth_max_side_var.get())
+            self.status_var.set(f"Running depth inference ({backend}, max_side={depth_max_side})...")
+            self.root.update_idletasks()
+            depth_img = self.inference_client.predict_depth(
+                self.current_raw_frame,
+                colorize=True,
+                depth_model=backend,
+                depth_max_side=depth_max_side
+            )
+            self._show_depth_preview(depth_img)
+            self.status_var.set(f"Depth map ready ({backend}, max_side={depth_max_side}).")
+        except Exception as e:
+            messagebox.showerror("Depth Error", str(e))
+            self.status_var.set("Depth inference failed")
+        finally:
+            self.root.config(cursor="")
+
+    def _show_depth_preview(self, depth_img):
+        """Display the depth map in a non-modal window."""
+        if self.depth_preview_window is None or not self.depth_preview_window.winfo_exists():
+            self.depth_preview_window = tk.Toplevel(self.root)
+            self.depth_preview_window.title("FastDepth Preview")
+            self.depth_preview_window.geometry("900x700")
+            self.depth_preview_window.configure(bg="#111")
+            self.depth_preview_label = tk.Label(self.depth_preview_window, bg="#111")
+            self.depth_preview_label.pack(fill=tk.BOTH, expand=True, padx=8, pady=8)
+
+        h, w = depth_img.shape[:2]
+        max_w, max_h = 1200, 900
+        scale = min(max_w / max(1, w), max_h / max(1, h), 1.0)
+        if scale < 1.0:
+            depth_img = cv2.resize(depth_img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
+
+        if depth_img.ndim == 2:
+            rgb = cv2.cvtColor(depth_img, cv2.COLOR_GRAY2RGB)
+        else:
+            rgb = cv2.cvtColor(depth_img, cv2.COLOR_BGR2RGB)
+
+        pil = Image.fromarray(rgb)
+        self.depth_preview_photo = ImageTk.PhotoImage(pil)
+        self.depth_preview_label.configure(image=self.depth_preview_photo)
+        self.depth_preview_window.lift()
+
+    def refine_boxes_with_sam(self):
+        """Send current rectangle boxes to SAM and update polygons."""
+        if self.current_raw_frame is None:
+            messagebox.showwarning("Warning", "No image loaded.")
+            return
+        if not self.last_results:
+            messagebox.showwarning("Warning", "No boxes available. Add or predict boxes first.")
+            return
+
+        try:
+            self.root.config(cursor="watch")
+            refine_indices = [
+                i for i, det in enumerate(self.last_results)
+                if not self._det_has_polygon(det)
+            ]
+            if not refine_indices:
+                self.status_var.set("SAM: All boxes already have polygons; nothing to refine.")
+                return
+
+            self.status_var.set(f"SAM: Refining {len(refine_indices)} boxes...")
+            self.root.update_idletasks()
+
+            boxes_to_refine = []
+            for idx in refine_indices:
+                det = self.last_results[idx]
+                box = det.get('box', det)
+                boxes_to_refine.append([int(box['x1']), int(box['y1']), int(box['x2']), int(box['y2'])])
+
+            sam_results = self.inference_client.segment_multi_box(self.current_raw_frame, boxes_to_refine)
+            refined = 0
+            for local_idx, res in enumerate(sam_results):
+                poly_pts = res.get('polygon', [])
+                if not poly_pts:
+                    continue
+                det_idx = refine_indices[local_idx]
+                box = self.last_results[det_idx].get('box', self.last_results[det_idx])
+                poly_arr = np.array(poly_pts, dtype=np.float32).reshape(-1, 1, 2)
+                self._set_det_single_polygon(self.last_results[det_idx], poly_arr - [box['x1'], box['y1']])
+                self.last_results[det_idx].pop('suppress_auto_polygon', None)
+                self.last_results[det_idx]['conf'] = res.get('score', self.last_results[det_idx].get('conf', 1.0))
+                refined += 1
+
+            self.redraw_current_frame()
+            self.status_var.set(f"SAM: Refined {refined}/{len(refine_indices)} boxes")
+        except Exception as e:
+            messagebox.showerror("SAM Error", str(e))
+            self.status_var.set("SAM box refinement failed")
+        finally:
+            self.root.config(cursor="")
+
+    # --- Streaming ---
+
+    def toggle_stream(self):
+        """Toggle live streaming on/off."""
+        if self.zmq_client and self.zmq_client.is_streaming():
+            self._stop_streaming()
+        else:
+            self._start_streaming()
+
+    def _start_streaming(self):
+        """Start ZMQ live streaming."""
+        self.stream_out_timestamps.clear()
+        self.stream_fps_in = 0.0
+        self.stream_fps_out = 0.0
+        self.stream_fps_pred = 0.0
+        self._last_server_frame_id = None
+        self._last_server_frame_ts = None
+        self.zmq_client = ZMQStreamClient(
+            self.zmq_det_url.get(),
+            on_frame_callback=self._on_zmq_frame,
+            on_focus_callback=self._on_focus_update
+        )
+        if not self.focus_enabled_var.get():
+            self.zmq_client.disable_focus()
+        self.zmq_client.start()
+        self.stream_btn.config(text="Stop Live", bg="red")
+        cuda_text = "ON" if getattr(self.zmq_client, 'cuda_enabled', False) else "OFF"
+        print(f"INFO: GUI stream CUDA focus: {cuda_text}", flush=True)
+        self.status_var.set(f"Streaming... CUDA focus: {cuda_text}")
+
+    def _stop_streaming(self):
+        """Stop ZMQ live streaming."""
+        if self.zmq_client:
+            self.zmq_client.stop()
+            self.zmq_client = None
+        self.stream_out_timestamps.clear()
+        self.stream_fps_in = 0.0
+        self.stream_fps_out = 0.0
+        self.stream_fps_pred = 0.0
+        self._last_server_frame_id = None
+        self._last_server_frame_ts = None
+        self._pending_stream_frame = None
+        self._pending_stream_detections = None
+        self._pending_stream_metadata = None
+        self._last_stream_metadata = {}
+        self._stream_render_scheduled = False
+        self.stream_btn.config(text="Connect Live", bg="blue")
+        self.status_var.set("Streaming stopped")
+
+    def toggle_focus_stream(self):
+        """Enable/disable focus-score computation for live stream."""
+        new_state = not self.focus_enabled_var.get()
+        self.focus_enabled_var.set(new_state)
+
+        if self.zmq_client and self.zmq_client.is_streaming():
+            if new_state:
+                self.zmq_client.enable_focus()
+            else:
+                self.zmq_client.disable_focus()
+
+        if new_state:
+            self.focus_btn.config(text="🎯 Focus ON", bg="#17a2b8")
+            self.status_var.set("Focus scoring enabled")
+        else:
+            self.focus_btn.config(text="🎯 Focus OFF", bg="#6c757d")
+            self.status_var.set("Focus scoring disabled")
+
+    def _on_zmq_frame(self, frame, detections, metadata=None):
+        """Callback for new ZMQ frame.
+        Keep only newest frame and render in UI at a capped rate to stay live."""
+        now = time.time()
+
+        if isinstance(metadata, dict):
+            source_fps = metadata.get('source_fps')
+            if isinstance(source_fps, (int, float)):
+                self.stream_fps_in = float(source_fps)
+            else:
+                frame_id = metadata.get('frame_id')
+                if isinstance(frame_id, int):
+                    if self._last_server_frame_id is not None and self._last_server_frame_ts is not None:
+                        dt = now - self._last_server_frame_ts
+                        df = frame_id - self._last_server_frame_id
+                        if dt > 0 and df >= 0:
+                            self.stream_fps_in = float(df) / float(dt)
+                    self._last_server_frame_id = frame_id
+                    self._last_server_frame_ts = now
+
+            prediction_fps = metadata.get('prediction_fps')
+            if isinstance(prediction_fps, (int, float)):
+                self.stream_fps_pred = float(prediction_fps)
+
+        # Overwrite pending payload: this intentionally drops stale frames.
+        self._pending_stream_frame = frame
+        self._pending_stream_detections = detections
+        self._pending_stream_metadata = metadata
+
+        if not self._stream_render_scheduled:
+            self._stream_render_scheduled = True
+            self.root.after(0, self._flush_latest_stream_frame)
+
+    def _flush_latest_stream_frame(self):
+        """Render latest pending stream frame, dropping any intermediate backlog."""
+        self._stream_render_scheduled = False
+        if self._pending_stream_frame is None:
+            return
+
+        now = time.time()
+        if self.stream_ui_max_fps > 0:
+            min_interval = 1.0 / float(self.stream_ui_max_fps)
+            dt = now - self._last_ui_render_ts
+            if dt < min_interval:
+                delay_ms = max(1, int((min_interval - dt) * 1000))
+                self._stream_render_scheduled = True
+                self.root.after(delay_ms, self._flush_latest_stream_frame)
+                return
+
+        frame = self._pending_stream_frame
+        detections = self._pending_stream_detections if self._pending_stream_detections is not None else []
+        metadata = self._pending_stream_metadata if isinstance(self._pending_stream_metadata, dict) else {}
+        self._pending_stream_frame = None
+        self._pending_stream_detections = None
+        self._pending_stream_metadata = None
+
+        self.current_raw_frame = frame
+        self.last_results = detections
+        self._last_stream_metadata = metadata
+        self.update_canvas(frame, metadata=metadata)
+        self._last_ui_render_ts = now
+
+        # Render FPS is measured on UI flushes, not on received packets.
+        self.stream_out_timestamps.append(now)
+        while self.stream_out_timestamps and (now - self.stream_out_timestamps[0]) > 1.0:
+            self.stream_out_timestamps.popleft()
+        self.stream_fps_out = float(len(self.stream_out_timestamps))
+
+    def _on_focus_update(self, score):
+        """Callback for focus score update."""
+        self.focus_history.append(score)
+        self.root.after_idle(self.redraw_focus_plot)
+
+    # --- Display & Canvas Interaction ---
+
+    def regenerate_selected_edge(self):
+        """Force regeneration of polygon for selected detection using brush strokes or SAM."""
+        if self.canvas.selected_obj_idx == -1:
+            self.status_var.set("No detection selected")
+            return
+
+        if self.canvas.selected_obj_idx >= len(self.last_results):
+            return
+
+        det = self.last_results[self.canvas.selected_obj_idx]
+        box = det.get('box', det)
+
+        # --- NEW: SAM Refinement Path ---
+        if self.use_sam_refine.get():
+            try:
+                self.root.config(cursor="watch")
+                self.status_var.set("SAM: Refining based on box...")
+                self.root.update_idletasks()
+
+                res = self.inference_client.segment_anything_box(
+                    self.current_raw_frame,
+                    box['x1'], box['y1'], box['x2'], box['y2']
+                )
+                poly_pts = res.get('polygon', [])
+                if poly_pts:
+                    poly_arr = np.array(poly_pts, dtype=np.float32).reshape(-1, 1, 2)
+                    self._set_det_single_polygon(det, poly_arr - [box['x1'], box['y1']])
+                    det.pop('suppress_auto_polygon', None)
+                    self.status_var.set("SAM: Edge refined successfully")
+                else:
+                    self.status_var.set("SAM: Could not find object in box")
+            except Exception as e:
+                self.status_var.set(f"SAM Error: {str(e)}")
+            finally:
+                self.root.config(cursor="")
+                self.redraw_current_frame()
+            return
+
+        # Check if we have manual mask
+        has_manual_mask = 'manual_mask' in det
+        print(f"Regenerating edge: has_manual_mask={has_manual_mask}")
+
+        if has_manual_mask:
+            manual_mask = det['manual_mask']
+            has_bg = np.any(manual_mask == 0)
+            has_fg = np.any(manual_mask == 1)
+            print(f"  Manual mask: has_bg={has_bg}, has_fg={has_fg}")
+
+        # Delete polygon to force regeneration
+        if 'poly' in det or 'polygons' in det:
+            det.pop('poly', None)
+            det.pop('polygons', None)
+            print("  Deleted existing polygon(s)")
+        det.pop('suppress_auto_polygon', None)
+
+        # Trigger redraw which will regenerate the polygon
+        self.redraw_current_frame()
+
+        # Check if manual mask still exists after redraw
+        has_manual_mask_after = 'manual_mask' in det
+        print(f"  After redraw: has_manual_mask={has_manual_mask_after}")
+
+        if has_manual_mask_after:
+            self.status_var.set("Edge regenerated - paint more or click again to refine")
+        else:
+            self.status_var.set("Edge regenerated")
+
+    def clear_brush_strokes(self):
+        """Clear brush strokes for selected detection."""
+        if self.canvas.selected_obj_idx == -1:
+            self.status_var.set("No detection selected")
+            return
+
+        if self.canvas.selected_obj_idx >= len(self.last_results):
+            return
+
+        self._save_history()
+        det = self.last_results[self.canvas.selected_obj_idx]
+
+        # Clear manual mask and polygon
+        if 'manual_mask' in det:
+            del det['manual_mask']
+        det.pop('poly', None)
+        det.pop('polygons', None)
+
+        self.redraw_current_frame()
+        self.status_var.set("Brush strokes cleared for selected detection")
+
+    def redraw_focus_plot(self):
+        """Redraw focus history plot."""
+        window = self.tk_vars['plot_window_size'].get()
+        self.ctrl_panel.update_focus_plot(list(self.focus_history), window)
+
+    # --- Training & Annotation Submission ---
+
+    def _convert_to_yolo_segmentation(self):
+        """Convert detections to YOLO segmentation format.
+        YOLO segmentation format per line:
+        class_id x1 y1 x2 y2 x3 y3 ... (normalized coordinates 0-1)
+        """
+        if self.current_raw_frame is None or not self.last_results:
+            return []
+
+        img_h, img_w = self.current_raw_frame.shape[:2]
+        yolo_lines = []
+
+        for det in self.last_results:
+            # Get class
+            class_name = det.get('name', det.get('label', self._default_class_name()))
+            class_id = self.class_to_id.get(class_name, 0)
+
+            box = det.get('box', det)
+            polygons = self._det_get_absolute_polygons(det)
+
+            if polygons:
+                for points in polygons:
+                    normalized_points = []
+                    for pt in points:
+                        nx = pt[0] / img_w
+                        ny = pt[1] / img_h
+                        normalized_points.extend([nx, ny])
+                    line = f"{class_id} " + " ".join(f"{coord:.6f}" for coord in normalized_points)
+                    yolo_lines.append(line)
+            else:
+                x1, y1 = box.get('x1', 0), box.get('y1', 0)
+                x2, y2 = box.get('x2', 0), box.get('y2', 0)
+                cx = ((x1 + x2) / 2) / img_w
+                cy = ((y1 + y2) / 2) / img_h
+                w = (x2 - x1) / img_w
+                h = (y2 - y1) / img_h
+                line = f"{class_id} {cx:.6f} {cy:.6f} {w:.6f} {h:.6f}"
+                yolo_lines.append(line)
+
+        return yolo_lines
+
+    def _save_local_annotation_files(self, file_stem, yolo_seg_lines, yolo_det_lines):
+        """Persist image + YOLO labels under the configured local output folder."""
+        root = Path(self.local_output_dir)
+        targets = {
+            "segmentation": yolo_seg_lines,
+            "detection": yolo_det_lines,
+        }
+        for task_type, lines in targets.items():
+            base_dir = root / task_type
+            img_dir = base_dir / "images"
+            lbl_dir = base_dir / "labels"
+            img_dir.mkdir(parents=True, exist_ok=True)
+            lbl_dir.mkdir(parents=True, exist_ok=True)
+            cv2.imwrite(str(img_dir / f"{file_stem}.jpg"), self.current_raw_frame)
+            with open(lbl_dir / f"{file_stem}.txt", "w", encoding="utf-8") as f:
+                f.write("\n".join(lines) + ("\n" if lines else ""))
+
+        classes_txt = root / "classes.txt"
+        class_names = [name for _, name in sorted((idx, name) for name, idx in self.class_to_id.items())]
+        classes_txt.write_text("\n".join(class_names) + ("\n" if class_names else ""), encoding="utf-8")
+        (root / "annotation_mode.txt").write_text(f"{self.annotation_mode}\n", encoding="utf-8")
+
+    @staticmethod
+    def _polygon_area(points):
+        """Compute polygon area for COCO annotations."""
+        arr = np.array(points, dtype=np.float32).reshape(-1, 1, 2)
+        return float(abs(cv2.contourArea(arr)))
+
+    @staticmethod
+    def _rle_encode_binary_mask(mask):
+        """Encode uint8 binary mask into COCO uncompressed RLE."""
+        pixels = np.asarray(mask, dtype=np.uint8).flatten(order="F")
+        counts = []
+        prev = 0
+        run_len = 0
+        for pix in pixels:
+            val = 1 if pix else 0
+            if val == prev:
+                run_len += 1
+            else:
+                counts.append(run_len)
+                run_len = 1
+                prev = val
+        counts.append(run_len)
+        return {"size": [int(mask.shape[0]), int(mask.shape[1])], "counts": counts}
+
+    @staticmethod
+    def _rle_decode_binary_mask(seg):
+        """Decode COCO uncompressed RLE dict into uint8 binary mask."""
+        size = seg.get("size", [])
+        counts = seg.get("counts", [])
+        if not isinstance(size, list) or len(size) != 2 or not isinstance(counts, list):
+            return None
+        h, w = int(size[0]), int(size[1])
+        flat = np.zeros(h * w, dtype=np.uint8)
+        idx = 0
+        val = 0
+        for run in counts:
+            run = int(run)
+            if run <= 0:
+                val = 1 - val
+                continue
+            end = min(idx + run, flat.size)
+            if val == 1:
+                flat[idx:end] = 1
+            idx = end
+            if idx >= flat.size:
+                break
+            val = 1 - val
+        return flat.reshape((h, w), order="F")
+
+    def _build_mask_from_rings(self, img_h, img_w, polygons_abs, holes_abs):
+        """Rasterize outer/hole rings to binary mask (outer minus holes)."""
+        mask = np.zeros((img_h, img_w), dtype=np.uint8)
+        for poly in polygons_abs:
+            pts = np.round(poly).astype(np.int32).reshape(-1, 1, 2)
+            cv2.fillPoly(mask, [pts], 1)
+        for hole in holes_abs:
+            pts = np.round(hole).astype(np.int32).reshape(-1, 1, 2)
+            cv2.fillPoly(mask, [pts], 0)
+        return mask
+
+    def _save_local_coco_annotation_files(self, file_stem):
+        """Persist current image+annotations in COCO instance format."""
+        root = Path(self.local_output_dir) / "coco"
+        images_dir = root / "images"
+        images_dir.mkdir(parents=True, exist_ok=True)
+
+        image_filename = f"{file_stem}.jpg"
+        cv2.imwrite(str(images_dir / image_filename), self.current_raw_frame)
+
+        coco_path = root / "annotations.json"
+        if coco_path.exists():
+            try:
+                coco = json.loads(coco_path.read_text(encoding="utf-8"))
+            except Exception:
+                coco = {}
+        else:
+            coco = {}
+
+        coco.setdefault("info", {"description": "AareLC local COCO export"})
+        coco.setdefault("licenses", [])
+        coco.setdefault("images", [])
+        coco.setdefault("annotations", [])
+        coco["categories"] = [
+            {"id": idx + 1, "name": name}
+            for name, idx in sorted(self.class_to_id.items(), key=lambda kv: kv[1])
+        ]
+        coco["meta"] = {"annotation_mode": self.annotation_mode}
+
+        img_h, img_w = self.current_raw_frame.shape[:2]
+        existing_img = next((img for img in coco["images"] if img.get("file_name") == image_filename), None)
+        if existing_img:
+            image_id = existing_img["id"]
+            existing_img.update({"width": int(img_w), "height": int(img_h)})
+        else:
+            image_id = (max((img.get("id", 0) for img in coco["images"]), default=0) + 1)
+            coco["images"].append({
+                "id": image_id,
+                "file_name": image_filename,
+                "width": int(img_w),
+                "height": int(img_h),
+            })
+
+        coco["annotations"] = [ann for ann in coco["annotations"] if ann.get("image_id") != image_id]
+        next_ann_id = max((ann.get("id", 0) for ann in coco["annotations"]), default=0) + 1
+
+        for det in self.last_results:
+            class_name = det.get('name', det.get('label', self._default_class_name()))
+            category_id = self.class_to_id.get(class_name, 0) + 1
+            box = det.get('box', det)
+            x1, y1 = float(box['x1']), float(box['y1'])
+            x2, y2 = float(box['x2']), float(box['y2'])
+            bw, bh = max(0.0, x2 - x1), max(0.0, y2 - y1)
+
+            polygons_abs = self._det_get_absolute_polygons(det)
+            holes_abs = self._det_get_absolute_holes(det)
+            if not polygons_abs:
+                polygons_abs = [np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]], dtype=np.float32)]
+
+            if holes_abs:
+                mask = self._build_mask_from_rings(img_h, img_w, polygons_abs, holes_abs)
+                ys, xs = np.where(mask > 0)
+                if len(xs) == 0 or len(ys) == 0:
+                    continue
+                min_x, max_x = float(np.min(xs)), float(np.max(xs))
+                min_y, max_y = float(np.min(ys)), float(np.max(ys))
+                segmentation_payload = self._rle_encode_binary_mask(mask)
+                area_total = float(np.sum(mask > 0))
+                bbox_payload = [min_x, min_y, max_x - min_x + 1.0, max_y - min_y + 1.0]
+            else:
+                segmentation = []
+                area_total = 0.0
+                for poly in polygons_abs:
+                    flat = [float(v) for point in poly for v in point]
+                    if len(flat) >= 6:
+                        segmentation.append(flat)
+                        area_total += self._polygon_area(poly)
+                if not segmentation:
+                    continue
+                segmentation_payload = segmentation
+                bbox_payload = [x1, y1, bw, bh]
+
+            coco["annotations"].append({
+                "id": next_ann_id,
+                "image_id": image_id,
+                "category_id": category_id,
+                "segmentation": segmentation_payload,
+                "area": float(area_total),
+                "bbox": bbox_payload,
+                "iscrowd": 0,
+            })
+            next_ann_id += 1
+
+        coco_path.write_text(json.dumps(coco, indent=2), encoding="utf-8")
+        (root / "annotation_mode.txt").write_text(f"{self.annotation_mode}\n", encoding="utf-8")
+
+    def send_to_training(self):
+        """Save YOLO segmentation annotations to database and locally for training."""
+        if self.review_mode:
+            self.save_review_annotation()
+            return
+
+        if self.current_raw_frame is None:
+            messagebox.showwarning("Warning", "No image loaded.")
+            return
+
+        if not self.last_results:
+            messagebox.showwarning("Warning", "No annotations to save.")
+            return
+
+        try:
+            img_h, img_w = self.current_raw_frame.shape[:2]
+
+            if self._is_coco_multi_mode():
+                if self.current_local_image_path is not None:
+                    file_stem = self.current_local_image_path.stem
+                elif self.current_image_id is not None:
+                    file_stem = f"img_{self.current_image_id}"
+                else:
+                    file_stem = datetime.now().strftime("manual_%Y%m%d_%H%M%S")
+
+                confirm_msg = (
+                    f"Save {len(self.last_results)} annotations in COCO multi-polygon mode "
+                    f"to local folder only?\n\nMode: {self._get_annotation_mode_label()}"
+                )
+                if not messagebox.askyesno("Confirm", confirm_msg):
+                    return
+
+                self.root.config(cursor="watch")
+                self._save_local_coco_annotation_files(file_stem)
+                self.status_var.set(f"Saved COCO annotation: {file_stem}")
+                messagebox.showinfo(
+                    "Success",
+                    (
+                        f"Saved '{file_stem}' in COCO format under:\n{self.local_output_dir / 'coco'}\n\n"
+                        "File: annotations.json (multi-polygon capable)"
+                    ),
+                )
+                return
+
+            if any(self._det_get_holes_relative(det) for det in self.last_results):
+                if not messagebox.askyesno(
+                    "YOLO Limitation",
+                    (
+                        "Some instances contain hole polygons, but YOLO txt export cannot encode holes.\n"
+                        "Holes will be ignored in YOLO export.\n\nContinue?"
+                    ),
+                ):
+                    return
+
+            # 1. Prepare YOLO data (Normalized)
+            segments = []
+            yolo_seg_lines = [] # For polygons
+            yolo_det_lines = [] # For bounding boxes
+
+            for det in self.last_results:
+                class_name = det.get('name', det.get('label', self._default_class_name()))
+                class_id = self.class_to_id.get(class_name, 0)
+                box = det.get('box', det)
+
+                # --- 1a. Generate Detection Line (cx, cy, w, h) ---
+                x1, y1, x2, y2 = float(box['x1']), float(box['y1']), float(box['x2']), float(box['y2'])
+                cx = ((x1 + x2) / 2) / img_w
+                cy = ((y1 + y2) / 2) / img_h
+                bw = (x2 - x1) / img_w
+                bh = (y2 - y1) / img_h
+                yolo_det_lines.append(f"{class_id} {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
+
+                # --- 1b. Generate Segmentation Line (x1, y1, x2, y2...) ---
+                polygons = self._det_get_absolute_polygons(det)
+                if polygons:
+                    for points in polygons:
+                        normalized_points = []
+                        for pt in points:
+                            nx, ny = float(pt[0] / img_w), float(pt[1] / img_h)
+                            normalized_points.extend([nx, ny])
+
+                        segments.append({
+                            "class_id": class_id,
+                            "points": normalized_points,
+                            "box": [cx, cy, bw, bh]
+                        })
+                        pts_str = " ".join(f"{p:.6f}" for p in normalized_points)
+                        yolo_seg_lines.append(f"{class_id} {pts_str}")
+                else:
+                    # Fallback: Use box corners as a 4-point polygon
+                    norm_poly = [x1 / img_w, y1 / img_h, x2 / img_w, y1 / img_h, x2 / img_w, y2 / img_h, x1 / img_w,
+                                 y2 / img_h]
+                    segments.append({
+                        "class_id": class_id,
+                        "points": norm_poly,
+                        "box": [cx, cy, bw, bh]
+                    })
+                    pts_str = " ".join(f"{p:.6f}" for p in norm_poly)
+                    yolo_seg_lines.append(f"{class_id} {pts_str}")
+
+            save_mode = "database + local training sets" if self.current_image_id is not None else "local folder only"
+            if not messagebox.askyesno("Confirm", f"Save {len(segments)} annotations to {save_mode}?"):
+                return
+
+            self.root.config(cursor="watch")
+
+            if self.current_image_id is None:
+                if self.current_local_image_path is not None:
+                    file_stem = self.current_local_image_path.stem
+                else:
+                    file_stem = datetime.now().strftime("manual_%Y%m%d_%H%M%S")
+
+                self._save_local_annotation_files(file_stem, yolo_seg_lines, yolo_det_lines)
+                self.status_var.set(f"Saved local annotation: {file_stem}")
+                messagebox.showinfo(
+                    "Success",
+                    (
+                        f"Saved '{file_stem}' under:\n{self.local_output_dir}\n\n"
+                        "Subfolders created: segmentation/, detection/, classes.txt"
+                    ),
+                )
+                return
+
+            # --- 3. LOCAL SAVING (DB workflow, legacy path layout) ---
+            file_stem = f"img_{self.current_image_id}"
+
+            for task_type in ["seg_training", "det_training"]:
+                base_dir = Path(f"data/img_sets/{task_type}")
+                img_dir, lbl_dir = base_dir / "images", base_dir / "labels"
+                img_dir.mkdir(parents=True, exist_ok=True)
+                lbl_dir.mkdir(parents=True, exist_ok=True)
+
+                # Save the image in both places (required for YOLO training structure)
+                cv2.imwrite(str(img_dir / f"{file_stem}.jpg"), self.current_raw_frame)
+
+                # Save appropriate label format
+                lines = yolo_seg_lines if "seg" in task_type else yolo_det_lines
+                with open(lbl_dir / f"{file_stem}.txt", "w") as f:
+                    f.write("\n".join(lines))
+
+            # --- 4. DATABASE SAVING ---
+            db_result = self.db_client.save_annotation(
+                self.current_image_id,
+                segments,
+                username=self.pref_username.get()
+            )
+            status_msg = f"Saved locally ({file_stem}) and to DB (ID: {db_result.get('annotation_id')})"
+            self.status_var.set((status_msg[:120] + '...') if len(status_msg) > 123 else status_msg)
+
+            # FIX: Removed the undefined 'img_path' variable and updated the message
+            messagebox.showinfo(
+                "Success",
+                f"Annotated image '{file_stem}' saved to local training sets (Detection & Segmentation) and synced to database."
+            )
+
+            # 5. Fetch next image automatically
+            self.fetch_db_image()
+
+        except Exception as e:
+            messagebox.showerror("Error", f"Failed to save:\n{str(e)}")
+            import traceback; traceback.print_exc()
+        finally:
+            self.root.config(cursor="")
+
+    def skip_current_image(self):
+        """Skip the current image and fetch the next one."""
+        if self.current_image_id is None:
+            messagebox.showwarning(
+                "Warning",
+                "No image to skip. Please use 'Next Image' button first."
+            )
+            return
+
+        try:
+            # Confirm skip
+            response = messagebox.askyesno(
+                "Confirm Skip",
+                f"Skip image ID {self.current_image_id}?\n\n"
+                "This image will be marked as skipped in the database."
+            )
+
+            if not response:
+                return
+
+            self.root.config(cursor="watch")
+            result = self.db_client.skip_image(self.current_image_id)
+
+            self.status_var.set(f"Skipped image ID {self.current_image_id}")
+
+            # Fetch next image automatically
+            self.fetch_db_image()
+
+        except Exception as e:
+            messagebox.showerror("Error", f"Failed to skip image:\n{str(e)}")
+            self.status_var.set("Skip failed")
+            import traceback
+            traceback.print_exc()
+        finally:
+            self.root.config(cursor="")
+
+    def show_sync_dialog(self):
+        """Show dialog to sync missing local images and labels from DB."""
+        dialog = tk.Toplevel(self.root)
+        dialog.title("Sync DB -> Local")
+        dialog.geometry("640x420")
+        dialog.minsize(520, 340)
+        dialog.resizable(True, True)
+        dialog.transient(self.root)
+        dialog.grab_set()
+
+        frame = tk.Frame(dialog, padx=20, pady=20)
+        frame.pack(fill=tk.BOTH, expand=True)
+
+        tk.Label(
+            frame,
+            text="Copy only missing local image/annotation files from DB records.",
+            font=('Arial', 10),
+        ).grid(row=0, column=0, columnspan=2, sticky=tk.W, pady=(0, 12))
+
+        tk.Label(frame, text="Since (YYYY-MM-DD or ISO):").grid(
+            row=1, column=0, sticky=tk.W, pady=6
+        )
+        tk.Entry(frame, textvariable=self.sync_since, width=32).grid(
+            row=1, column=1, sticky=tk.W, padx=8
+        )
+
+        tk.Label(frame, text="Until (YYYY-MM-DD or ISO):").grid(
+            row=2, column=0, sticky=tk.W, pady=6
+        )
+        tk.Entry(frame, textvariable=self.sync_until, width=32).grid(
+            row=2, column=1, sticky=tk.W, padx=8
+        )
+
+        tk.Label(frame, text="Limit:").grid(row=3, column=0, sticky=tk.W, pady=6)
+        tk.Entry(frame, textvariable=self.sync_limit, width=12).grid(
+            row=3, column=1, sticky=tk.W, padx=8
+        )
+
+        tk.Checkbutton(
+            frame,
+            text="Include unannotated images",
+            variable=self.sync_include_unannotated,
+        ).grid(row=4, column=0, columnspan=2, sticky=tk.W, pady=(10, 4))
+
+        tk.Checkbutton(
+            frame,
+            text="Dry run (preview only, no files written)",
+            variable=self.sync_dry_run,
+        ).grid(row=5, column=0, columnspan=2, sticky=tk.W, pady=4)
+
+        hint = (
+            "Tip: run dry-run first, then uncheck dry-run to copy files."
+        )
+        tk.Label(frame, text=hint, fg="#555", font=('Arial', 9)).grid(
+            row=6, column=0, columnspan=2, sticky=tk.W, pady=(10, 4)
+        )
+
+        def run_sync():
+            try:
+                limit = int(self.sync_limit.get())
+                if limit <= 0:
+                    raise ValueError("Limit must be > 0")
+
+                self.root.config(cursor="watch")
+                dialog.config(cursor="watch")
+                self.status_var.set("Syncing local files from database...")
+                self.root.update_idletasks()
+
+                result = self.db_client.sync_local_annotations(
+                    since=self.sync_since.get().strip() or None,
+                    until=self.sync_until.get().strip() or None,
+                    include_unannotated=self.sync_include_unannotated.get(),
+                    dry_run=self.sync_dry_run.get(),
+                    limit=limit,
+                )
+
+                stats = result.get("stats", {})
+                failures = result.get("failures", [])
+
+                summary = (
+                    f"Scanned: {stats.get('scanned', 0)}\n"
+                    f"Images copied: {stats.get('image_copied', 0)}\n"
+                    f"Images existing: {stats.get('image_existing', 0)}\n"
+                    f"Images failed: {stats.get('image_failed', 0)}\n"
+                    f"Labels copied: {stats.get('label_copied', 0)}\n"
+                    f"Labels existing: {stats.get('label_existing', 0)}\n"
+                    f"Labels failed: {stats.get('label_failed', 0)}"
+                )
+
+                if failures:
+                    sample_lines = [f[:80] + "..." if len(f) > 80 else f for f in failures[:10]]
+                    sample = "\n".join(sample_lines)
+                    messagebox.showwarning(
+                        "Sync Completed with Warnings",
+                        f"{summary}\n\nFirst failures:\n{sample}",
+                    )
+                else:
+                    messagebox.showinfo("Sync Completed", summary)
+
+                self.status_var.set("DB->local sync completed")
+            except Exception as e:
+                messagebox.showerror("Sync Error", str(e))
+                self.status_var.set("DB->local sync failed")
+            finally:
+                self.root.config(cursor="")
+                dialog.config(cursor="")
+
+        button_row = tk.Frame(frame)
+        button_row.grid(row=7, column=0, columnspan=2, sticky=tk.EW, pady=(18, 0))
+
+        tk.Button(button_row, text="Close", command=dialog.destroy, width=12).pack(
+            side=tk.LEFT, padx=8
+        )
+        tk.Button(
+            button_row,
+            text="Run Sync",
+            command=run_sync,
+            bg="#007bff",
+            fg="white",
+            width=12,
+            font=('Arial', 10, 'bold'),
+        ).pack(side=tk.LEFT, padx=8)
+
+    def show_zmq_inference_control_dialog(self):
+        """Control runtime settings of remote zmq_inference server via HTTP API."""
+        dialog = tk.Toplevel(self.root)
+        dialog.title("ZMQ Inference Control")
+        dialog.geometry("900x780")
+        dialog.minsize(760, 700)
+        dialog.resizable(True, True)
+        dialog.transient(self.root)
+        dialog.grab_set()
+
+        frame = tk.Frame(dialog, padx=16, pady=14)
+        frame.pack(fill=tk.BOTH, expand=True)
+        frame.grid_columnconfigure(1, weight=1)
+
+        control_url_var = tk.StringVar(value=self.pref_zmq_control_url.get().strip() or "http://localhost:8090")
+        control_token_var = tk.StringVar(value=self.pref_zmq_control_token.get().strip())
+        conf_var = tk.DoubleVar(value=0.25)
+        infer_scale_var = tk.DoubleVar(value=1.0)
+        skip_var = tk.IntVar(value=0)
+        max_fps_var = tk.DoubleVar(value=0.0)
+        device_var = tk.StringVar(value="0")
+        shard_count_var = tk.IntVar(value=1)
+        shard_index_var = tk.IntVar(value=0)
+        model_choice_var = tk.StringVar(value="")
+        model_seg_hint_var = tk.StringVar(value="Segmentation capable: unknown")
+        cuda_hint_var = tk.StringVar(value="Server CUDA: unknown")
+        task_var = tk.StringVar(value="auto")
+        tracker_var = tk.StringVar(value="none")
+        bayer_flip_var = tk.BooleanVar(value=False)
+        publish_enabled_var = tk.BooleanVar(value=True)
+        compute_target_point_var = tk.BooleanVar(value=False)
+        focus_enabled_var = tk.BooleanVar(value=False)
+        focus_epics_enabled_var = tk.BooleanVar(value=False)
+        focus_pv_var = tk.StringVar(value="X10SA-ES-MS:cam1:FocusScore")
+        focus_every_var = tk.IntVar(value=3)
+        focus_scale_var = tk.DoubleVar(value=0.5)
+        focus_pv_min_period_var = tk.DoubleVar(value=100.0)
+        model_items_by_label = {}
+        server_cuda_available = {"value": False}
+        model_refresh_job = {"id": None}
+        runtime_model_paths = {"engine": "", "pt": ""}
+
+        tk.Label(frame, text="Control URL:", font=('Arial', 10, 'bold')).grid(row=0, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=control_url_var, width=52).grid(row=0, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Control Token:").grid(row=1, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=control_token_var, width=30, show="*").grid(row=1, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Confidence:").grid(row=2, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=conf_var, width=16).grid(row=2, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Infer Scale:").grid(row=3, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=infer_scale_var, width=16).grid(row=3, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Skip Frames:").grid(row=4, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=skip_var, width=16).grid(row=4, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Max Predict FPS (0=off):").grid(row=5, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=max_fps_var, width=16).grid(row=5, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Model (models/):").grid(row=6, column=0, sticky=tk.W, pady=4)
+        model_combo = ttk.Combobox(frame, textvariable=model_choice_var, width=58, state="readonly")
+        model_combo.grid(row=6, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, textvariable=model_seg_hint_var, fg="#126b2c", font=('Arial', 9, 'bold')).grid(
+            row=7, column=1, sticky=tk.W, padx=8, pady=(0, 2)
+        )
+        tk.Label(frame, textvariable=cuda_hint_var, fg="#1c4f8a", font=('Arial', 9, 'bold')).grid(
+            row=8, column=1, sticky=tk.W, padx=8, pady=(0, 2)
+        )
+
+        tk.Label(frame, text="Task:").grid(row=9, column=0, sticky=tk.W, pady=4)
+        task_combo = ttk.Combobox(
+            frame,
+            textvariable=task_var,
+            values=("auto", "detect", "segment"),
+            width=14,
+            state="readonly",
+        )
+        task_combo.grid(row=9, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Tracker:").grid(row=10, column=0, sticky=tk.W, pady=4)
+        tracker_combo = ttk.Combobox(
+            frame,
+            textvariable=tracker_var,
+            values=("none", "bytetrack", "botsort"),
+            width=14,
+            state="readonly",
+        )
+        tracker_combo.grid(row=10, column=1, sticky=tk.W, padx=8)
+
+        tk.Label(frame, text="Device (cpu or GPU idx):").grid(row=11, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=device_var, width=16).grid(row=11, column=1, sticky=tk.W, padx=8)
+        tk.Label(frame, text="Shard Count (1=off, 0=auto):").grid(row=12, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=shard_count_var, width=16).grid(row=12, column=1, sticky=tk.W, padx=8)
+        tk.Label(frame, text="Shard Index:").grid(row=13, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=shard_index_var, width=16).grid(row=13, column=1, sticky=tk.W, padx=8)
+
+        tk.Checkbutton(frame, text="Bayer Flip", variable=bayer_flip_var).grid(
+            row=14, column=1, sticky=tk.W, padx=8, pady=(6, 2)
+        )
+        tk.Checkbutton(frame, text="Publish Output", variable=publish_enabled_var).grid(
+            row=15, column=1, sticky=tk.W, padx=8, pady=(2, 2)
+        )
+        tk.Checkbutton(frame, text="Compute Target Point", variable=compute_target_point_var).grid(
+            row=16, column=1, sticky=tk.W, padx=8, pady=(2, 2)
+        )
+        tk.Checkbutton(frame, text="Compute Focus Score", variable=focus_enabled_var).grid(
+            row=17, column=1, sticky=tk.W, padx=8, pady=(2, 2)
+        )
+        tk.Checkbutton(frame, text="Write Focus to EPICS", variable=focus_epics_enabled_var).grid(
+            row=18, column=1, sticky=tk.W, padx=8, pady=(2, 2)
+        )
+        tk.Label(frame, text="Focus EPICS PV:").grid(row=19, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=focus_pv_var, width=52).grid(row=19, column=1, sticky=tk.W, padx=8)
+        tk.Label(frame, text="Focus Every N Frames:").grid(row=20, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=focus_every_var, width=16).grid(row=20, column=1, sticky=tk.W, padx=8)
+        tk.Label(frame, text="Focus Scale (0-1]:").grid(row=21, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=focus_scale_var, width=16).grid(row=21, column=1, sticky=tk.W, padx=8)
+        tk.Label(frame, text="EPICS Min Period (ms):").grid(row=22, column=0, sticky=tk.W, pady=4)
+        tk.Entry(frame, textvariable=focus_pv_min_period_var, width=16).grid(row=22, column=1, sticky=tk.W, padx=8)
+
+        hint = (
+            "Apply changes to the running zmq_inference service.\n"
+            "Model/task changes trigger hot reload for all downstream ZMQ consumers."
+        )
+        tk.Label(frame, text=hint, fg="#555", justify=tk.LEFT, font=('Arial', 9)).grid(
+            row=23, column=0, columnspan=2, sticky=tk.W, pady=(10, 6)
+        )
+
+        def _base_url():
+            base = control_url_var.get().strip().rstrip("/")
+            if not base:
+                raise ValueError("Control URL is empty")
+            return base
+
+        def _norm_path(value):
+            if not value:
+                return ""
+            return os.path.normpath(os.path.expanduser(str(value).strip()))
+
+        def _canonical_model_ref(value):
+            """Normalize model path refs to improve matching across server/client cwd differences."""
+            raw = str(value or "").strip()
+            if not raw:
+                return ""
+            normalized = raw.replace("\\", "/")
+            if "/models/" in normalized:
+                return "models/" + normalized.split("/models/", 1)[1]
+            if normalized.startswith("../models/"):
+                return "models/" + normalized[len("../models/"):]
+            if normalized.startswith("./models/"):
+                return "models/" + normalized[len("./models/"):]
+            return normalized
+
+        def _set_segmentation_hint(item):
+            if not item:
+                model_seg_hint_var.set("Segmentation capable: unknown")
+                return
+            yes_no = "yes" if bool(item.get("segmentation_capable")) else "no"
+            model_seg_hint_var.set(f"Segmentation capable: {yes_no}")
+
+        def _model_label(item):
+            name = str(item.get("name") or item.get("project_path") or item.get("path") or "model")
+            kind = str(item.get("kind", "")).upper()
+            seg = "SEG" if bool(item.get("segmentation_capable")) else "DET"
+            kind_lower = str(item.get("kind", "")).lower()
+            cuda_mark = ""
+            if server_cuda_available["value"] and kind_lower in ("engine", "engines"):
+                cuda_mark = " | CUDA"
+            if kind:
+                return f"{name} [{kind} | {seg}{cuda_mark}]"
+            return f"{name} [{seg}]"
+
+        def _match_model_item(model_path):
+            if not model_path:
+                return None
+            normalized = _norm_path(_canonical_model_ref(model_path))
+            for item in model_items_by_label.values():
+                candidates = {
+                    _norm_path(_canonical_model_ref(item.get("path"))),
+                    _norm_path(_canonical_model_ref(item.get("project_path"))),
+                    _norm_path(_canonical_model_ref(item.get("name"))),
+                }
+                if normalized in candidates:
+                    return item
+            return None
+
+        def _apply_model_item(item, update_task=True):
+            if not item:
+                return
+            if update_task:
+                task_hint = str(item.get("task_hint") or "").lower()
+                if task_hint in ("detect", "segment"):
+                    task_var.set(task_hint)
+            _set_segmentation_hint(item)
+
+        def refresh_models(quiet=False):
+            try:
+                base = _base_url()
+                token = control_token_var.get().strip()
+                headers = {"X-API-Key": token} if token else None
+
+                resp = requests.get(f"{base}/models", headers=headers, timeout=3.0)
+                resp.raise_for_status()
+                payload = resp.json()
+                models = payload.get("models", [])
+                caps = payload.get("capabilities", {})
+                server_cuda_available["value"] = bool(caps.get("cuda_available", False))
+                cuda_hint_var.set(f"Server CUDA: {'yes' if server_cuda_available['value'] else 'no'}")
+            except Exception:
+                # Fallback for older servers without /models endpoint.
+                local_models = []
+                models_root = Path(__file__).resolve().parent / "models"
+                if models_root.exists():
+                    for p in sorted(models_root.rglob("*")):
+                        if not p.is_file() or p.suffix.lower() not in {".pt", ".engine", ".engines", ".yaml", ".yml", ".onnx"}:
+                            continue
+                        rel = str(p.relative_to(models_root))
+                        seg = any(tok in p.name.lower() for tok in ("seg", "segment", "mask"))
+                        local_models.append(
+                            {
+                                "name": rel,
+                                "path": str(p),
+                                "project_path": str(Path("models") / rel),
+                                "kind": p.suffix.lower().lstrip("."),
+                                "segmentation_capable": seg,
+                                "task_hint": "segment" if seg else "detect",
+                            }
+                        )
+                models = local_models
+                if not quiet and not models:
+                    messagebox.showwarning(
+                        "Models Unavailable",
+                        "Could not fetch models from server and no local models folder entries were found.",
+                    )
+
+            # Ensure current runtime model is selectable even if it is outside scanned models/.
+            current_model_path = runtime_model_paths.get("engine", "").strip() or runtime_model_paths.get("pt", "").strip()
+            if current_model_path:
+                current_norm = _norm_path(_canonical_model_ref(current_model_path))
+                has_match = False
+                for item in models:
+                    candidates = {
+                        _norm_path(_canonical_model_ref(item.get("path"))),
+                        _norm_path(_canonical_model_ref(item.get("project_path"))),
+                        _norm_path(_canonical_model_ref(item.get("name"))),
+                    }
+                    if current_norm in candidates:
+                        has_match = True
+                        break
+                if not has_match:
+                    normalized_current = _canonical_model_ref(current_model_path)
+                    current_name = normalized_current.split("/")[-1] if "/" in normalized_current else normalized_current
+                    current_kind = "engine" if normalized_current.endswith((".engine", ".engines")) else "pt"
+                    seg_cap = (
+                        str(task_var.get()).strip().lower() == "segment"
+                        or any(tok in current_name.lower() for tok in ("seg", "segment", "mask"))
+                    )
+                    models.insert(
+                        0,
+                        {
+                            "name": current_name,
+                            "path": current_model_path,
+                            "project_path": normalized_current,
+                            "kind": current_kind,
+                            "segmentation_capable": seg_cap,
+                            "task_hint": "segment" if seg_cap else "detect",
+                        },
+                    )
+
+            labels = []
+            model_items_by_label.clear()
+            for item in models:
+                label = _model_label(item)
+                if len(label) > 60:
+                    label = label[:57] + "..."
+                labels.append(label)
+                model_items_by_label[label] = item
+
+            model_combo["values"] = labels
+            if not labels:
+                model_choice_var.set("")
+                _set_segmentation_hint(None)
+                return
+
+            current_model_path = runtime_model_paths.get("engine", "").strip() or runtime_model_paths.get("pt", "").strip()
+            selected_item = _match_model_item(current_model_path)
+            if selected_item:
+                selected_label = _model_label(selected_item)
+                model_choice_var.set(selected_label)
+                _set_segmentation_hint(selected_item)
+            else:
+                model_choice_var.set(labels[0])
+                _set_segmentation_hint(model_items_by_label.get(labels[0]))
+
+        def _on_model_selected(_event=None):
+            item = model_items_by_label.get(model_choice_var.get())
+            _apply_model_item(item, update_task=True)
+
+        model_combo.bind("<<ComboboxSelected>>", _on_model_selected)
+
+        def load_current(quiet=False):
+            try:
+                base = _base_url()
+                token = control_token_var.get().strip()
+                headers = {"X-API-Key": token} if token else None
+                self.root.config(cursor="watch")
+                dialog.config(cursor="watch")
+                resp = requests.get(f"{base}/config", headers=headers, timeout=3.0)
+                resp.raise_for_status()
+                data = resp.json()
+                cfg = data.get("config", data)
+                caps = data.get("capabilities", {})
+                if "cuda_available" in caps:
+                    server_cuda_available["value"] = bool(caps.get("cuda_available"))
+                    cuda_hint_var.set(f"Server CUDA: {'yes' if server_cuda_available['value'] else 'no'}")
+                conf_var.set(float(cfg.get("conf", conf_var.get())))
+                infer_scale_var.set(float(cfg.get("infer_scale", infer_scale_var.get())))
+                skip_var.set(int(cfg.get("skip", skip_var.get())))
+                max_fps_var.set(float(cfg.get("max_fps", max_fps_var.get())))
+                device_var.set(str(cfg.get("device", device_var.get())))
+                shard_count_var.set(int(cfg.get("shard_count", shard_count_var.get())))
+                shard_index_var.set(int(cfg.get("shard_index", shard_index_var.get())))
+                task_var.set(str(cfg.get("task", task_var.get())))
+                tracker_var.set(str(cfg.get("tracker", tracker_var.get())))
+                runtime_model_paths["engine"] = str(cfg.get("engine", "")).strip()
+                runtime_model_paths["pt"] = str(cfg.get("pt", "")).strip()
+                bayer_flip_var.set(bool(cfg.get("bayer_flip", bayer_flip_var.get())))
+                publish_enabled_var.set(bool(cfg.get("publish_enabled", publish_enabled_var.get())))
+                compute_target_point_var.set(bool(cfg.get("compute_target_point", compute_target_point_var.get())))
+                focus_enabled_var.set(bool(cfg.get("focus_enabled", focus_enabled_var.get())))
+                focus_epics_enabled_var.set(bool(cfg.get("focus_epics_enabled", focus_epics_enabled_var.get())))
+                focus_pv_var.set(str(cfg.get("focus_pv", focus_pv_var.get())))
+                focus_every_var.set(int(cfg.get("focus_every", focus_every_var.get())))
+                focus_scale_var.set(float(cfg.get("focus_scale", focus_scale_var.get())))
+                focus_pv_min_period_var.set(float(cfg.get("focus_pv_min_period_ms", focus_pv_min_period_var.get())))
+                refresh_models(quiet=True)
+                current_item = _match_model_item(runtime_model_paths.get("engine") or runtime_model_paths.get("pt"))
+                if current_item:
+                    model_choice_var.set(_model_label(current_item))
+                    _set_segmentation_hint(current_item)
+                else:
+                    _set_segmentation_hint(None)
+                self.pref_zmq_control_url.set(base)
+                self.pref_zmq_control_token.set(token)
+                self.status_var.set("Loaded ZMQ inference runtime config")
+            except Exception as e:
+                if not quiet:
+                    messagebox.showerror("Load Error", f"Failed loading config:\n{e}")
+                self.status_var.set("Failed loading ZMQ inference config")
+            finally:
+                self.root.config(cursor="")
+                dialog.config(cursor="")
+
+        def _schedule_refresh_on_url_change(*_args):
+            if model_refresh_job["id"] is not None:
+                try:
+                    dialog.after_cancel(model_refresh_job["id"])
+                except Exception:
+                    pass
+            def _run():
+                model_refresh_job["id"] = None
+                try:
+                    load_current(quiet=True)
+                except Exception:
+                    pass
+            model_refresh_job["id"] = dialog.after(500, _run)
+
+        control_url_var.trace_add("write", _schedule_refresh_on_url_change)
+
+        def apply_changes():
+            try:
+                base = _base_url()
+                token = control_token_var.get().strip()
+                headers = {"X-API-Key": token} if token else None
+                selected_item = model_items_by_label.get(model_choice_var.get())
+                if not selected_item:
+                    raise ValueError("Select a model from models/ before applying.")
+                selected_kind = str(selected_item.get("kind") or "").lower()
+                selected_project_path = str(
+                    selected_item.get("project_path") or selected_item.get("name") or ""
+                ).strip()
+                if not selected_project_path:
+                    raise ValueError("Selected model has no usable models/ path.")
+                selected_engine = selected_project_path if selected_kind in ("engine", "engines") else ""
+                selected_pt = selected_project_path if selected_kind not in ("engine", "engines") else ""
+                device_value = str(device_var.get()).strip()
+                if not device_value:
+                    raise ValueError("Device is required (cpu or CUDA index).")
+                shard_count_value = int(shard_count_var.get())
+                shard_index_value = int(shard_index_var.get())
+                payload = {
+                    "conf": float(conf_var.get()),
+                    "infer_scale": float(infer_scale_var.get()),
+                    "skip": int(skip_var.get()),
+                    "max_fps": float(max_fps_var.get()),
+                    "device": device_value,
+                    "shard_count": shard_count_value,
+                    "shard_index": shard_index_value,
+                    "task": str(task_var.get()),
+                    "tracker": str(tracker_var.get()),
+                    "bayer_flip": bool(bayer_flip_var.get()),
+                    "publish_enabled": bool(publish_enabled_var.get()),
+                    "compute_target_point": bool(compute_target_point_var.get()),
+                    "focus_enabled": bool(focus_enabled_var.get()),
+                    "focus_epics_enabled": bool(focus_epics_enabled_var.get()),
+                    "focus_pv": str(focus_pv_var.get()).strip(),
+                    "focus_every": int(focus_every_var.get()),
+                    "focus_scale": float(focus_scale_var.get()),
+                    "focus_pv_min_period_ms": float(focus_pv_min_period_var.get()),
+                    "engine": selected_engine,
+                    "pt": selected_pt,
+                }
+                self.root.config(cursor="watch")
+                dialog.config(cursor="watch")
+                resp = requests.post(f"{base}/config", json=payload, headers=headers, timeout=6.0)
+                if resp.status_code >= 400:
+                    raise RuntimeError(f"HTTP {resp.status_code}: {resp.text}")
+                out = resp.json()
+                self.pref_zmq_control_url.set(base)
+                self.pref_zmq_control_token.set(token)
+                messagebox.showinfo(
+                    "Config Applied",
+                    f"Updated runtime config.\nModel reloaded: {bool(out.get('model_reloaded', False))}",
+                )
+                self.status_var.set("Applied ZMQ inference runtime config")
+            except Exception as e:
+                messagebox.showerror("Apply Error", f"Failed applying config:\n{e}")
+                self.status_var.set("Failed applying ZMQ inference config")
+            finally:
+                self.root.config(cursor="")
+                dialog.config(cursor="")
+
+        button_row = tk.Frame(frame)
+        button_row.grid(row=24, column=0, columnspan=2, sticky=tk.EW, pady=(12, 0))
+        tk.Button(button_row, text="Close", command=dialog.destroy, width=12).pack(side=tk.LEFT, padx=6)
+        tk.Button(button_row, text="Refresh Models", command=refresh_models, width=14).pack(side=tk.LEFT, padx=6)
+        tk.Button(button_row, text="Load Current", command=load_current, width=14).pack(side=tk.LEFT, padx=6)
+        tk.Button(
+            button_row,
+            text="Apply",
+            command=apply_changes,
+            bg="#28a745",
+            fg="white",
+            width=12,
+            font=('Arial', 10, 'bold'),
+        ).pack(side=tk.LEFT, padx=6)
+
+        # Initialize from the currently selected server.
+        load_current(quiet=True)
+
+    # --- Preferences ---
+
+    def show_preferences(self):
+        """Show a single consolidated preferences dialog."""
+        dialog = tk.Toplevel(self.root)
+        dialog.title("Server Settings")
+        dialog.geometry("640x500")
+        dialog.minsize(500, 390)
+        dialog.resizable(True, True)
+        dialog.transient(self.root)
+        dialog.grab_set()
+
+        main_frame = tk.Frame(dialog, padx=20, pady=20)
+        main_frame.pack(fill=tk.BOTH, expand=True)
+        original_stream_url = self.zmq_det_url.get().strip()
+
+        # Define fields to display
+        config_fields = [
+            ("Inference API URL:", self.pref_inference_url, False),
+            ("Database API URL:", self.pref_db_url, False),
+            ("Image Download URL:", self.pref_download_url, False),
+            ("ZMQ Control URL:", self.pref_zmq_control_url, False),
+            ("ZMQ Control Token:", self.pref_zmq_control_token, True),
+            ("ZMQ Unified Stream:", self.zmq_det_url, False),
+            ("Shared Password:", self.pref_shared_pw, True),
+            ("Annotator Name:", self.pref_username, False)
+        ]
+        combo_values_by_label = {
+            "Image Download URL:": self.pref_download_url_options,
+            "ZMQ Control URL:": self.pref_zmq_control_options,
+            "ZMQ Unified Stream:": self.pref_zmq_stream_options,
+        }
+
+        for i, (label_text, var, is_pwd) in enumerate(config_fields):
+            tk.Label(main_frame, text=label_text, font=('Arial', 10)).grid(row=i, column=0, sticky=tk.W, pady=8)
+            if label_text in combo_values_by_label and not is_pwd:
+                entry = ttk.Combobox(main_frame, textvariable=var, values=combo_values_by_label[label_text], width=38)
+            else:
+                entry = tk.Entry(main_frame, textvariable=var, width=40, show="*" if is_pwd else "")
+            entry.grid(row=i, column=1, sticky=tk.EW, padx=10)
+
+        def apply_and_close():
+            # Update the actual clients with the new values
+            self.inference_client.set_server_url(self.pref_inference_url.get())
+            self.db_client.set_server_url(self.pref_db_url.get())
+            self.db_client.set_shared_password(self.pref_shared_pw.get())
+
+            if hasattr(self.db_client, 'set_download_base'):
+                self.db_client.set_download_base(self.pref_download_url.get())
+
+            new_stream_url = self.zmq_det_url.get().strip()
+            if new_stream_url and new_stream_url != original_stream_url:
+                try:
+                    parsed = urllib.parse.urlparse(new_stream_url)
+                    host = parsed.hostname
+                    if host:
+                        inferred_control = f"http://{host}:8090"
+                        self.pref_zmq_control_url.set(inferred_control)
+                except Exception:
+                    pass
+
+            self.status_var.set("Settings updated successfully.")
+            dialog.destroy()
+
+        btn_frame = tk.Frame(main_frame)
+        btn_frame.grid(row=len(config_fields), column=0, columnspan=2, pady=20)
+
+        tk.Button(btn_frame, text="Cancel", command=dialog.destroy, width=12).pack(side=tk.LEFT, padx=10)
+        tk.Button(
+            btn_frame,
+            text="Save Changes",
+            command=apply_and_close,
+            bg="#28a745",
+            fg="white",
+            width=12,
+            font=('Arial', 10, 'bold')
+        ).pack(side=tk.LEFT, padx=10)
+
+
+if __name__ == "__main__":
+    # --- X11 CRASH PREVENTATIVE MEASURES ---
+    # Force legacy rendering to avoid RenderAddGlyphs BadLength errors on virtual displays
+    os.environ["GDK_RENDERING"] = "image"
+    os.environ["XFT_CONFIG"] = "/dev/null"
+    os.environ["XLIB_SKIP_ARGB_VISUALS"] = "1"
+
+    try:
+        root = tk.Tk()
+
+        # Disable X Input Methods and Anti-aliasing which trigger the BadLength error
+        root.tk.call('tk', 'useinputmethods', '0')
+
+        # Force standard core bitmap fonts for ALL widgets globally
+        # This bypasses the RENDER extension entirely
+        root.option_add("*Font", "fixed")
+        root.option_add("*TLabel*Font", "fixed")
+        root.option_add("*TButton*Font", "fixed")
+        root.option_add("*Menu*Font", "fixed")
+
+        gui = InferenceGUI(root)
+        root.mainloop()
+    except tk.TclError as e:
+        print(f"CRITICAL: Could not connect to X11 display. Check your SSH -X forwarding.")
+        print(f"Error: {e}")
diff --git a/src/gui/review_mixin.py b/src/gui/review_mixin.py
new file mode 100644
index 0000000..1096f8e
--- /dev/null
+++ b/src/gui/review_mixin.py
@@ -0,0 +1,474 @@
+"""Review dataset workflow mixin (YOLO/COCO)."""
+
+from pathlib import Path
+import json
+
+import cv2
+import numpy as np
+from tkinter import filedialog, messagebox
+
+
+class ReviewMixin:
+    """Methods for opening, navigating, parsing, and saving review datasets."""
+
+    def open_review_folder(self):
+        """Open local YOLO or COCO review dataset."""
+        selected = filedialog.askdirectory(title="Select review root (or images folder)")
+        if not selected:
+            return
+
+        root = Path(selected)
+        coco_candidates = [root / "annotations.json", root / "coco" / "annotations.json"]
+        coco_path = next((p for p in coco_candidates if p.exists()), None)
+
+        if coco_path is not None:
+            try:
+                coco = json.loads(coco_path.read_text(encoding="utf-8"))
+            except Exception as e:
+                messagebox.showerror("COCO Load Error", f"Failed reading COCO file:\n{e}")
+                return
+
+            images = coco.get("images", [])
+            annotations = coco.get("annotations", [])
+            anns_by_image = {}
+            for ann in annotations:
+                anns_by_image.setdefault(ann.get("image_id"), []).append(ann)
+
+            base_dir = coco_path.parent
+            image_search_dirs = [base_dir / "images", root / "images", base_dir, root]
+            items = []
+            for img in images:
+                file_name = str(img.get("file_name", "")).strip()
+                if not file_name:
+                    continue
+                found_path = None
+                for d in image_search_dirs:
+                    candidate = d / file_name
+                    if candidate.exists():
+                        found_path = candidate
+                        break
+                if found_path is None:
+                    continue
+                items.append({
+                    "format": "coco",
+                    "image_id": img.get("id"),
+                    "image_path": found_path,
+                    "file_name": file_name,
+                    "annotations": anns_by_image.get(img.get("id"), []),
+                })
+
+            if not items:
+                messagebox.showwarning(
+                    "No COCO Images",
+                    "Found annotations.json but no readable image files referenced by COCO images[].",
+                )
+                return
+
+            self._stop_streaming()
+            self.review_mode = True
+            self.review_format = "coco"
+            self.review_coco_path = coco_path
+            self.review_coco_data = coco
+            self.review_items = sorted(items, key=lambda it: str(it["image_path"]))
+            self.review_index = 0
+            self.current_review_item = None
+            self.load_review_item(0)
+            return
+
+        images_dir = root / "images" if (root / "images").is_dir() else root
+        labels_dir = root / "labels" if (root / "labels").is_dir() else root
+        image_exts = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
+
+        items = []
+        for img_path in sorted(images_dir.rglob("*")):
+            if not img_path.is_file() or img_path.suffix.lower() not in image_exts:
+                continue
+            rel = img_path.relative_to(images_dir)
+            label_path = (labels_dir / rel).with_suffix(".txt")
+            if not label_path.exists():
+                label_path = labels_dir / f"{img_path.stem}.txt"
+            if label_path.exists():
+                items.append({"image_path": img_path, "label_path": label_path})
+
+        if not items:
+            messagebox.showwarning(
+                "No Pairs Found",
+                "No image+label pairs found. Expected either:\n"
+                "1) <root>/images + <root>/labels with matching names\n"
+                "2) images and .txt labels in same folder",
+            )
+            return
+
+        self._stop_streaming()
+        self.review_mode = True
+        self.review_format = "yolo"
+        self.review_coco_path = None
+        self.review_coco_data = None
+        self.review_items = items
+        self.review_index = 0
+        self.current_review_item = None
+        self.load_review_item(0)
+
+    def exit_review_mode(self, silent=False):
+        """Exit local review mode and clear review state."""
+        was_active = self.review_mode
+        self.review_mode = False
+        self.review_format = None
+        self.review_coco_path = None
+        self.review_coco_data = None
+        self.review_items = []
+        self.review_index = -1
+        self.current_review_item = None
+        if was_active and not silent:
+            self.status_var.set("Exited review mode")
+
+    def prev_review_image(self):
+        """Load previous review image."""
+        if not self.review_mode or not self.review_items:
+            messagebox.showwarning("Review Mode", "Open a review folder first.")
+            return
+        if self.review_index <= 0:
+            self.status_var.set("Already at first review image")
+            return
+        self.load_review_item(self.review_index - 1)
+
+    def next_review_image(self):
+        """Load next review image."""
+        if not self.review_mode or not self.review_items:
+            messagebox.showwarning("Review Mode", "Open a review folder first.")
+            return
+        if self.review_index >= len(self.review_items) - 1:
+            self.status_var.set("Already at last review image")
+            return
+        self.load_review_item(self.review_index + 1)
+
+    def _on_review_next_shortcut(self, event=None):
+        """Keyboard shortcut: next image in review mode."""
+        if not self.review_mode:
+            return
+        self.next_review_image()
+
+    def _on_review_prev_shortcut(self, event=None):
+        """Keyboard shortcut: previous image in review mode."""
+        if not self.review_mode:
+            return
+        self.prev_review_image()
+
+    def _parse_yolo_label_to_detections(self, label_path: Path, img_w: int, img_h: int):
+        """Parse YOLO label file (segmentation preferred, detection tolerated) into local detections."""
+        detections = []
+        try:
+            lines = label_path.read_text(encoding="utf-8").splitlines()
+        except Exception:
+            return detections
+
+        for line in lines:
+            stripped = line.strip()
+            if not stripped:
+                continue
+            parts = stripped.split()
+            if len(parts) < 5:
+                continue
+
+            try:
+                class_id = int(float(parts[0]))
+                vals = [float(v) for v in parts[1:]]
+            except Exception:
+                continue
+
+            pts = []
+            if len(vals) >= 6 and len(vals) % 2 == 0:
+                for i in range(0, len(vals), 2):
+                    x = min(max(vals[i] * img_w, 0.0), img_w - 1.0)
+                    y = min(max(vals[i + 1] * img_h, 0.0), img_h - 1.0)
+                    pts.append([x, y])
+            elif len(vals) == 4:
+                # bbox line fallback -> convert to rectangle polygon
+                cx, cy, bw, bh = vals
+                x1 = (cx - bw / 2.0) * img_w
+                y1 = (cy - bh / 2.0) * img_h
+                x2 = (cx + bw / 2.0) * img_w
+                y2 = (cy + bh / 2.0) * img_h
+                x1 = min(max(x1, 0.0), img_w - 1.0)
+                y1 = min(max(y1, 0.0), img_h - 1.0)
+                x2 = min(max(x2, 0.0), img_w - 1.0)
+                y2 = min(max(y2, 0.0), img_h - 1.0)
+                pts = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+            else:
+                continue
+
+            arr = np.array(pts, dtype=np.float32)
+            x1, y1 = float(np.min(arr[:, 0])), float(np.min(arr[:, 1]))
+            x2, y2 = float(np.max(arr[:, 0])), float(np.max(arr[:, 1]))
+            poly = (arr - [x1, y1]).reshape(-1, 1, 2).astype(np.float32)
+            class_name = self.id_to_class.get(class_id, self._default_class_name())
+
+            detections.append(
+                {
+                    "box": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
+                    "name": class_name,
+                    "poly": poly,
+                    "polygons": [poly],
+                    "conf": 1.0,
+                }
+            )
+
+        return detections
+
+    def _parse_coco_item_to_detections(self, item):
+        """Parse COCO image item into local detections with multi-island polygons."""
+        detections = []
+        coco = self.review_coco_data or {}
+        categories = coco.get("categories", [])
+        cat_by_id = {cat.get("id"): cat.get("name", f"class_{cat.get('id')}") for cat in categories}
+        img = cv2.imread(str(item.get("image_path", "")))
+
+        for ann in item.get("annotations", []):
+            seg = ann.get("segmentation", [])
+            polygons_abs = []
+            holes_abs = []
+
+            if isinstance(seg, dict):
+                mask = self._rle_decode_binary_mask(seg)
+                if mask is not None:
+                    contours, hierarchy = cv2.findContours((mask * 255).astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+                    if hierarchy is not None and len(contours) > 0:
+                        for i, cnt in enumerate(contours):
+                            arr = cnt.reshape(-1, 2).astype(np.float32)
+                            if len(arr) < 3:
+                                continue
+                            parent = hierarchy[0][i][3]
+                            if parent == -1:
+                                polygons_abs.append(arr)
+                            else:
+                                holes_abs.append(arr)
+            else:
+                if not isinstance(seg, list):
+                    seg = []
+                for poly in seg:
+                    if not isinstance(poly, list) or len(poly) < 6 or (len(poly) % 2) != 0:
+                        continue
+                    pts = []
+                    for i in range(0, len(poly), 2):
+                        try:
+                            x = float(poly[i])
+                            y = float(poly[i + 1])
+                        except Exception:
+                            pts = []
+                            break
+                        pts.append([x, y])
+                    if len(pts) >= 3:
+                        polygons_abs.append(np.array(pts, dtype=np.float32))
+
+            if not polygons_abs:
+                bbox = ann.get("bbox", [0, 0, 0, 0])
+                if isinstance(bbox, list) and len(bbox) >= 4:
+                    x, y, w, h = [float(v) for v in bbox[:4]]
+                    polygons_abs = [np.array([[x, y], [x + w, y], [x + w, y + h], [x, y + h]], dtype=np.float32)]
+                else:
+                    continue
+
+            all_pts = np.vstack(polygons_abs)
+            x1 = float(np.min(all_pts[:, 0]))
+            y1 = float(np.min(all_pts[:, 1]))
+            x2 = float(np.max(all_pts[:, 0]))
+            y2 = float(np.max(all_pts[:, 1]))
+            rel_polygons = [
+                (poly - [x1, y1]).reshape(-1, 1, 2).astype(np.float32)
+                for poly in polygons_abs
+            ]
+            rel_holes = [
+                (poly - [x1, y1]).reshape(-1, 1, 2).astype(np.float32)
+                for poly in holes_abs
+            ]
+            class_id = ann.get("category_id")
+            try:
+                class_id_int = int(class_id)
+            except Exception:
+                class_id_int = 1
+            class_name = cat_by_id.get(class_id, self.id_to_class.get(class_id_int - 1, self._default_class_name()))
+
+            detections.append(
+                {
+                    "box": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
+                    "name": class_name,
+                    "poly": rel_polygons[0],
+                    "polygons": rel_polygons,
+                    "holes": rel_holes,
+                    "conf": 1.0,
+                    "_review_category_id": class_id,
+                    "_review_ann_id": ann.get("id"),
+                }
+            )
+
+        return detections
+
+    def load_review_item(self, index: int):
+        """Load review image and corresponding local annotation."""
+        if index < 0 or index >= len(self.review_items):
+            return
+
+        item = self.review_items[index]
+        img = cv2.imread(str(item["image_path"]))
+        if img is None:
+            messagebox.showerror("Load Error", f"Failed to read image:\n{item['image_path']}")
+            return
+
+        self.current_raw_frame = img
+        self.current_image_id = None
+        self.current_annotation_id = None
+        self.current_local_image_path = item["image_path"]
+        if item.get("format") == "coco" or self.review_format == "coco":
+            self.last_results = self._parse_coco_item_to_detections(item)
+        else:
+            self.last_results = self._parse_yolo_label_to_detections(
+                item["label_path"], img.shape[1], img.shape[0]
+            )
+        self.canvas.selected_obj_idx = -1
+        self.selected_island_idx = 0
+        self.selected_island_kind = "outer"
+        self.review_index = index
+        self.current_review_item = item
+        self.redraw_current_frame()
+        mode = "COCO" if (item.get("format") == "coco" or self.review_format == "coco") else "YOLO"
+        self.status_var.set(
+            f"Review {self.review_index + 1}/{len(self.review_items)} [{mode}]: {item['image_path'].name}"
+        )
+
+    def _build_segmentation_lines_from_current(self):
+        """Build YOLO segmentation lines from current detections (polygon-only output format)."""
+        if self.current_raw_frame is None:
+            return []
+        img_h, img_w = self.current_raw_frame.shape[:2]
+        lines = []
+        for det in self.last_results:
+            class_name = det.get("name", det.get("label", self._default_class_name()))
+            class_id = self.class_to_id.get(class_name, 0)
+            box = det.get("box", det)
+            polygons = self._det_get_absolute_polygons(det)
+            if not polygons:
+                x1, y1 = float(box.get("x1", 0)), float(box.get("y1", 0))
+                x2, y2 = float(box.get("x2", 0)), float(box.get("y2", 0))
+                polygons = [np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]], dtype=np.float32)]
+
+            for points in polygons:
+                norm = []
+                for pt in points:
+                    nx = float(np.clip(pt[0] / img_w, 0.0, 1.0))
+                    ny = float(np.clip(pt[1] / img_h, 0.0, 1.0))
+                    norm.extend([nx, ny])
+                lines.append(f"{class_id} " + " ".join(f"{p:.6f}" for p in norm))
+        return lines
+
+    def save_review_annotation(self):
+        """Save current edited annotation back to local review label file."""
+        if not self.review_mode or not self.current_review_item:
+            messagebox.showwarning("Review Mode", "No active review item.")
+            return
+        if self.current_raw_frame is None:
+            messagebox.showwarning("Warning", "No image loaded.")
+            return
+
+        if self.current_review_item.get("format") == "coco" or self.review_format == "coco":
+            self._save_review_coco_annotation()
+            return
+
+        lines = self._build_segmentation_lines_from_current()
+        label_path = self.current_review_item["label_path"]
+        try:
+            label_path.parent.mkdir(parents=True, exist_ok=True)
+            label_path.write_text("\n".join(lines) + ("\n" if lines else ""), encoding="utf-8")
+            self.status_var.set(f"Saved review annotation: {label_path.name}")
+        except Exception as e:
+            messagebox.showerror("Save Error", f"Failed writing label:\n{e}")
+
+    def _save_review_coco_annotation(self):
+        """Save current review image annotations back to COCO annotations.json."""
+        item = self.current_review_item or {}
+        image_id = item.get("image_id")
+        if image_id is None:
+            messagebox.showerror("Save Error", "COCO review item missing image_id.")
+            return
+        if self.review_coco_path is None or self.review_coco_data is None:
+            messagebox.showerror("Save Error", "No active COCO review dataset loaded.")
+            return
+
+        try:
+            coco = self.review_coco_data
+            coco.setdefault("images", [])
+            coco.setdefault("annotations", [])
+            coco.setdefault("categories", [])
+            categories = coco.get("categories", [])
+            cat_by_name = {str(cat.get("name")): cat.get("id") for cat in categories if cat.get("name")}
+            max_cat_id = max((int(cat.get("id", 0)) for cat in categories), default=0)
+
+            coco["annotations"] = [ann for ann in coco["annotations"] if ann.get("image_id") != image_id]
+            next_ann_id = max((int(ann.get("id", 0)) for ann in coco["annotations"]), default=0) + 1
+
+            for det in self.last_results:
+                class_name = str(det.get("name", det.get("label", self._default_class_name())))
+                category_id = cat_by_name.get(class_name)
+                if category_id is None:
+                    stored_cat_id = det.get("_review_category_id")
+                    if isinstance(stored_cat_id, int):
+                        category_id = stored_cat_id
+                    else:
+                        max_cat_id += 1
+                        category_id = max_cat_id
+                        categories.append({"id": category_id, "name": class_name})
+                    cat_by_name[class_name] = category_id
+
+                box = det.get("box", det)
+                x1, y1 = float(box.get("x1", 0.0)), float(box.get("y1", 0.0))
+                x2, y2 = float(box.get("x2", 0.0)), float(box.get("y2", 0.0))
+                bw, bh = max(0.0, x2 - x1), max(0.0, y2 - y1)
+
+                polygons_abs = self._det_get_absolute_polygons(det)
+                holes_abs = self._det_get_absolute_holes(det)
+                if not polygons_abs:
+                    polygons_abs = [np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]], dtype=np.float32)]
+
+                if holes_abs:
+                    img_h, img_w = self.current_raw_frame.shape[:2]
+                    mask = self._build_mask_from_rings(img_h, img_w, polygons_abs, holes_abs)
+                    ys, xs = np.where(mask > 0)
+                    if len(xs) == 0 or len(ys) == 0:
+                        continue
+                    min_x, max_x = float(np.min(xs)), float(np.max(xs))
+                    min_y, max_y = float(np.min(ys)), float(np.max(ys))
+                    segmentation_payload = self._rle_encode_binary_mask(mask)
+                    area_total = float(np.sum(mask > 0))
+                    bbox_payload = [min_x, min_y, max_x - min_x + 1.0, max_y - min_y + 1.0]
+                else:
+                    segmentation = []
+                    area_total = 0.0
+                    for poly in polygons_abs:
+                        flat = [float(v) for point in poly for v in point]
+                        if len(flat) >= 6:
+                            segmentation.append(flat)
+                            area_total += self._polygon_area(poly)
+                    if not segmentation:
+                        continue
+                    segmentation_payload = segmentation
+                    bbox_payload = [x1, y1, bw, bh]
+
+                coco["annotations"].append(
+                    {
+                        "id": next_ann_id,
+                        "image_id": image_id,
+                        "category_id": int(category_id),
+                        "segmentation": segmentation_payload,
+                        "area": float(area_total),
+                        "bbox": bbox_payload,
+                        "iscrowd": 0,
+                    }
+                )
+                next_ann_id += 1
+
+            # Keep in-memory review item synchronized for immediate reload/navigation.
+            image_anns = [ann for ann in coco["annotations"] if ann.get("image_id") == image_id]
+            self.current_review_item["annotations"] = image_anns
+            self.review_coco_path.write_text(json.dumps(coco, indent=2), encoding="utf-8")
+            self.status_var.set(f"Saved COCO review annotation: {self.current_review_item['image_path'].name}")
+        except Exception as e:
+            messagebox.showerror("Save Error", f"Failed writing COCO annotations:\n{e}")
diff --git a/src/inference/inferenceserver.py b/src/inference/inferenceserver.py
new file mode 100644
index 0000000..bf581c3
--- /dev/null
+++ b/src/inference/inferenceserver.py
@@ -0,0 +1,1391 @@
+from fastapi import FastAPI, File, UploadFile, Query, Body
+from fastapi.responses import Response
+from pathlib import Path
+import uvicorn
+import numpy as np
+from PIL import Image
+import io
+import json
+import platform
+import os
+import time
+from typing import Optional, List
+import logging
+import torch
+import argparse
+import cv2
+from dataclasses import replace
+import threading
+
+logger = logging.getLogger("inferenceserver")
+if not logger.handlers:
+    handler = logging.StreamHandler()
+    fmt = logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s")
+    handler.setFormatter(fmt)
+    logger.addHandler(handler)
+logger.setLevel(logging.INFO)
+app = FastAPI()
+
+@app.get('/health')
+async def health():
+    return {
+        "status": "ok",
+        "sam_loaded": _SAM_PREDICTOR is not None,
+        "depthpro_loaded": _DEPTHPRO_RUNTIME is not None,
+        "depth_anything_v1_loaded": _DEPTH_ANYTHING_V1_RUNTIME is not None,
+        "depth_anything_v1_trt_loaded": _DEPTH_ANYTHING_V1_TRT_RUNTIME is not None,
+        "depth_anything_v2_loaded": _DEPTH_ANYTHING_V2_RUNTIME is not None,
+        "depth_anything_v2_tiny_loaded": _DEPTH_ANYTHING_V2_TINY_RUNTIME is not None,
+        "depth_anything_v2_trt_loaded": _DEPTH_ANYTHING_V2_TRT_RUNTIME is not None,
+        "midas_v21_small_loaded": _MIDAS_V21_SMALL_RUNTIME is not None,
+    }
+
+# Detect capabilities
+is_jetson = 'tegra' in platform.uname().machine.lower() or (os.path.exists('/proc/device-tree/model') and 'NVIDIA' in open('/proc/device-tree/model').read())
+cuda_available = torch.cuda.is_available()
+
+# Globals for default paths/runtime inference tuning
+GLOBAL_PT_PATH = None
+GLOBAL_ENGINE_PATH = None
+GLOBAL_CONF_THRESHOLD = 0.25
+GLOBAL_SCALE_FACTOR = 1.0
+GLOBAL_INFER_SCALE_FACTOR = 1.0
+GLOBAL_TASK = "auto"
+
+# simple cache for models
+_MODEL_CACHE = {}
+_SAM_PREDICTOR = None
+_DEPTHPRO_RUNTIME = None
+_DEPTH_ANYTHING_V1_RUNTIME = None
+_DEPTH_ANYTHING_V1_TRT_RUNTIME = None
+_DEPTH_ANYTHING_V2_RUNTIME = None
+_DEPTH_ANYTHING_V2_TINY_RUNTIME = None
+_DEPTH_ANYTHING_V2_TRT_RUNTIME = None
+_MIDAS_V21_SMALL_RUNTIME = None
+_DEPTH_GPU_LOCK = threading.Lock()
+
+
+def _resolve_depthpro_model_path() -> Path:
+    base_dir = Path(__file__).resolve().parent.parent
+    cli = os.environ.get("DEPTHPRO_CHECKPOINT_PATH", "").strip()
+    if cli:
+        return Path(cli).expanduser()
+    return base_dir / "models" / "depth_pro.pt"
+
+
+def get_depthpro_runtime():
+    """Lazy loader for Apple Depth Pro runtime."""
+    global _DEPTHPRO_RUNTIME
+    if _DEPTHPRO_RUNTIME is not None:
+        return _DEPTHPRO_RUNTIME
+
+    checkpoint = _resolve_depthpro_model_path()
+    if not checkpoint.exists():
+        raise FileNotFoundError(
+            f"Depth Pro checkpoint not found at: {checkpoint}. "
+            "Set DEPTHPRO_CHECKPOINT_PATH or start with --depthpro-checkpoint."
+        )
+
+    try:
+        import depth_pro
+        from depth_pro.depth_pro import DEFAULT_MONODEPTH_CONFIG_DICT
+    except Exception as e:
+        raise RuntimeError(
+            f"Depth Pro import failed: {e}. Install apple/ml-depth-pro package in the same venv."
+        ) from e
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    precision = torch.float16 if device.type == "cuda" else torch.float32
+    config = replace(DEFAULT_MONODEPTH_CONFIG_DICT, checkpoint_uri=str(checkpoint))
+    model, transform = depth_pro.create_model_and_transforms(
+        config=config,
+        device=device,
+        precision=precision,
+    )
+    model.eval()
+
+    _DEPTHPRO_RUNTIME = {
+        "model": model,
+        "transform": transform,
+        "device": device,
+        "precision": precision,
+        "path": str(checkpoint),
+    }
+    logger.info("Loaded Depth Pro checkpoint: %s on %s (%s)", checkpoint, device, precision)
+    return _DEPTHPRO_RUNTIME
+
+
+def _resolve_depth_anything_v2_model_id() -> str:
+    return os.environ.get(
+        "DEPTH_ANYTHING_V2_MODEL_ID",
+        "depth-anything/Depth-Anything-V2-Small-hf"
+    ).strip()
+
+
+def _resolve_depth_anything_v2_tiny_model_id() -> str:
+    model_id = os.environ.get(
+        "DEPTH_ANYTHING_V2_TINY_MODEL_ID",
+        "depth-anything/Depth-Anything-V2-Small-hf"
+    ).strip()
+    if model_id == "depth-anything/Depth-Anything-V2-Tiny-hf":
+        logger.warning(
+            "Requested model id '%s' is not available on HF; falling back to "
+            "'depth-anything/Depth-Anything-V2-Small-hf'.",
+            model_id,
+        )
+        return "depth-anything/Depth-Anything-V2-Small-hf"
+    return model_id
+
+
+def _resolve_depth_anything_v1_model_id() -> str:
+    return os.environ.get(
+        "DEPTH_ANYTHING_V1_MODEL_ID",
+        "LiheYoung/depth-anything-small-hf"
+    ).strip()
+
+
+def _resolve_midas_v21_small_model_id() -> str:
+    return os.environ.get(
+        "MIDAS_V21_SMALL_MODEL_ID",
+        "Intel/dpt-swinv2-tiny-256"
+    ).strip()
+
+
+def _resolve_depth_anything_v1_trt_engine_path() -> Path:
+    base_dir = Path(__file__).resolve().parent.parent
+    cli = os.environ.get("DEPTH_ANYTHING_V1_TRT_ENGINE", "").strip()
+    if cli:
+        return Path(cli).expanduser()
+    return base_dir / "models" / "depth_anything_v1_small.engine"
+
+
+def _resolve_depth_anything_v2_trt_engine_path() -> Path:
+    base_dir = Path(__file__).resolve().parent.parent
+    cli = os.environ.get("DEPTH_ANYTHING_V2_TRT_ENGINE", "").strip()
+    if cli:
+        return Path(cli).expanduser()
+    return base_dir / "models" / "depth_anything_v2_small.engine"
+
+
+def _resolve_depth_anything_v2_max_side() -> int:
+    raw = os.environ.get("DEPTH_ANYTHING_V2_MAX_SIDE", "0").strip()
+    try:
+        v = int(raw)
+    except Exception:
+        return 0
+    return max(0, v)
+
+
+def get_depth_anything_v1_runtime():
+    """Lazy loader for Depth Anything V1 runtime via transformers."""
+    global _DEPTH_ANYTHING_V1_RUNTIME
+    if _DEPTH_ANYTHING_V1_RUNTIME is not None:
+        return _DEPTH_ANYTHING_V1_RUNTIME
+
+    model_id = _resolve_depth_anything_v1_model_id()
+    try:
+        from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+    except Exception as e:
+        raise RuntimeError(
+            f"Depth Anything V1 import failed: {e}. Install with `pip install transformers accelerate`."
+        ) from e
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    processor = AutoImageProcessor.from_pretrained(model_id)
+    model = AutoModelForDepthEstimation.from_pretrained(model_id)
+    model.to(device)
+    model.eval()
+
+    _DEPTH_ANYTHING_V1_RUNTIME = {
+        "model_id": model_id,
+        "processor": processor,
+        "model": model,
+        "device": device,
+    }
+    logger.info("Loaded Depth Anything V1 model: %s on %s", model_id, device)
+    return _DEPTH_ANYTHING_V1_RUNTIME
+
+
+def get_depth_anything_v2_runtime():
+    """Lazy loader for Depth Anything V2 runtime via transformers."""
+    global _DEPTH_ANYTHING_V2_RUNTIME
+    if _DEPTH_ANYTHING_V2_RUNTIME is not None:
+        return _DEPTH_ANYTHING_V2_RUNTIME
+
+    model_id = _resolve_depth_anything_v2_model_id()
+    try:
+        from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+    except Exception as e:
+        raise RuntimeError(
+            f"Depth Anything V2 import failed: {e}. Install with `pip install transformers accelerate`."
+        ) from e
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    processor = AutoImageProcessor.from_pretrained(model_id)
+    model = AutoModelForDepthEstimation.from_pretrained(model_id)
+    model.to(device)
+    model.eval()
+
+    _DEPTH_ANYTHING_V2_RUNTIME = {
+        "model_id": model_id,
+        "processor": processor,
+        "model": model,
+        "device": device,
+    }
+    logger.info("Loaded Depth Anything V2 model: %s on %s", model_id, device)
+    return _DEPTH_ANYTHING_V2_RUNTIME
+
+
+def get_depth_anything_v2_tiny_runtime():
+    """Lazy loader for Depth Anything V2 Tiny runtime via transformers."""
+    global _DEPTH_ANYTHING_V2_TINY_RUNTIME
+    if _DEPTH_ANYTHING_V2_TINY_RUNTIME is not None:
+        return _DEPTH_ANYTHING_V2_TINY_RUNTIME
+
+    model_id = _resolve_depth_anything_v2_tiny_model_id()
+    try:
+        from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+    except Exception as e:
+        raise RuntimeError(
+            f"Depth Anything V2 Tiny import failed: {e}. Install with `pip install transformers accelerate`."
+        ) from e
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    processor = AutoImageProcessor.from_pretrained(model_id)
+    model = AutoModelForDepthEstimation.from_pretrained(model_id)
+    model.to(device)
+    model.eval()
+
+    _DEPTH_ANYTHING_V2_TINY_RUNTIME = {
+        "model_id": model_id,
+        "processor": processor,
+        "model": model,
+        "device": device,
+    }
+    logger.info("Loaded Depth Anything V2 Tiny model: %s on %s", model_id, device)
+    return _DEPTH_ANYTHING_V2_TINY_RUNTIME
+
+
+def get_midas_v21_small_runtime():
+    """Lazy loader for MiDaS-like small depth runtime via transformers."""
+    global _MIDAS_V21_SMALL_RUNTIME
+    if _MIDAS_V21_SMALL_RUNTIME is not None:
+        return _MIDAS_V21_SMALL_RUNTIME
+
+    model_id = _resolve_midas_v21_small_model_id()
+    try:
+        from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+    except Exception as e:
+        raise RuntimeError(
+            f"MiDaS v2.1 small import failed: {e}. Install with `pip install transformers accelerate`."
+        ) from e
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    processor = AutoImageProcessor.from_pretrained(model_id)
+    model = AutoModelForDepthEstimation.from_pretrained(model_id)
+    model.to(device)
+    model.eval()
+
+    _MIDAS_V21_SMALL_RUNTIME = {
+        "model_id": model_id,
+        "processor": processor,
+        "model": model,
+        "device": device,
+    }
+    logger.info("Loaded MiDaS v2.1 small model: %s on %s", model_id, device)
+    return _MIDAS_V21_SMALL_RUNTIME
+
+
+def _create_trt_depth_runtime(engine_path: Path, model_id: str, label: str):
+    if not engine_path.exists():
+        raise FileNotFoundError(f"{label} TensorRT engine not found at: {engine_path}")
+
+    try:
+        import tensorrt as trt
+        import pycuda.driver as cuda
+        # Use primary CUDA context to reduce conflicts with torch CUDA runtime.
+        import pycuda.autoprimaryctx  # noqa: F401
+    except Exception as e:
+        raise RuntimeError(
+            f"TensorRT backend import failed: {e}. Install TensorRT Python + pycuda on this host."
+        ) from e
+
+    try:
+        from transformers import AutoImageProcessor
+        processor = AutoImageProcessor.from_pretrained(model_id)
+    except Exception as e:
+        raise RuntimeError(
+            f"{label} processor load failed for model id '{model_id}': {e}"
+        ) from e
+
+    trt_logger = trt.Logger(trt.Logger.WARNING)
+    with open(engine_path, "rb") as f:
+        engine_bytes = f.read()
+    runtime = trt.Runtime(trt_logger)
+    engine = runtime.deserialize_cuda_engine(engine_bytes)
+    if engine is None:
+        raise RuntimeError(f"Failed to deserialize TensorRT engine: {engine_path}")
+
+    is_trt10 = hasattr(engine, "num_io_tensors")
+    input_idx = None
+    output_idx = None
+    input_name = None
+    output_name = None
+
+    if is_trt10:
+        for i in range(engine.num_io_tensors):
+            tname = engine.get_tensor_name(i)
+            mode = engine.get_tensor_mode(tname)
+            if mode == trt.TensorIOMode.INPUT and input_name is None:
+                input_name = tname
+            elif mode == trt.TensorIOMode.OUTPUT and output_name is None:
+                output_name = tname
+        if input_name is None or output_name is None:
+            raise RuntimeError(f"Invalid TensorRT 10 I/O tensors for {engine_path}")
+        in_dtype = trt.nptype(engine.get_tensor_dtype(input_name))
+        out_dtype = trt.nptype(engine.get_tensor_dtype(output_name))
+    else:
+        for i in range(engine.num_bindings):
+            if engine.binding_is_input(i) and input_idx is None:
+                input_idx = i
+            if (not engine.binding_is_input(i)) and output_idx is None:
+                output_idx = i
+        if input_idx is None or output_idx is None:
+            raise RuntimeError(f"Invalid TensorRT bindings for {engine_path}")
+        in_dtype = trt.nptype(engine.get_binding_dtype(input_idx))
+        out_dtype = trt.nptype(engine.get_binding_dtype(output_idx))
+
+    rt = {
+        "label": label,
+        "engine_path": str(engine_path),
+        "model_id": model_id,
+        "processor": processor,
+        "trt": trt,
+        "cuda": cuda,
+        "engine": engine,
+        "is_trt10": is_trt10,
+        "input_idx": input_idx,
+        "output_idx": output_idx,
+        "input_name": input_name,
+        "output_name": output_name,
+        "input_dtype": in_dtype,
+        "output_dtype": out_dtype,
+    }
+    logger.info("Loaded %s TensorRT engine: %s", label, engine_path)
+    return rt
+
+
+def _infer_depth_anything_trt(runtime, model_rgb, orig_h: int, orig_w: int):
+    inputs = runtime["processor"](images=model_rgb, return_tensors="pt")
+    pixel_values = inputs["pixel_values"].detach().cpu().numpy()
+    pixel_values = np.ascontiguousarray(pixel_values.astype(runtime["input_dtype"], copy=False))
+
+    engine = runtime["engine"]
+    cuda = runtime["cuda"]
+    context = engine.create_execution_context()
+    stream = cuda.Stream()
+
+    if runtime["is_trt10"]:
+        input_name = runtime["input_name"]
+        output_name = runtime["output_name"]
+        context.set_input_shape(input_name, tuple(pixel_values.shape))
+        out_shape = tuple(context.get_tensor_shape(output_name))
+        if any(dim <= 0 for dim in out_shape):
+            raise RuntimeError(f"Unresolved TensorRT output shape: {out_shape}")
+
+        out_host = np.empty(out_shape, dtype=runtime["output_dtype"])
+        d_input = cuda.mem_alloc(pixel_values.nbytes)
+        d_output = cuda.mem_alloc(out_host.nbytes)
+        context.set_tensor_address(input_name, int(d_input))
+        context.set_tensor_address(output_name, int(d_output))
+
+        cuda.memcpy_htod_async(d_input, pixel_values, stream)
+        ok = context.execute_async_v3(stream_handle=stream.handle)
+        if not ok:
+            raise RuntimeError("TensorRT execute_async_v3 returned failure")
+        cuda.memcpy_dtoh_async(out_host, d_output, stream)
+        stream.synchronize()
+    else:
+        input_idx = runtime["input_idx"]
+        output_idx = runtime["output_idx"]
+        context.set_binding_shape(input_idx, tuple(pixel_values.shape))
+        out_shape = tuple(context.get_binding_shape(output_idx))
+        if any(dim <= 0 for dim in out_shape):
+            raise RuntimeError(f"Unresolved TensorRT output shape: {out_shape}")
+
+        out_host = np.empty(out_shape, dtype=runtime["output_dtype"])
+        d_input = cuda.mem_alloc(pixel_values.nbytes)
+        d_output = cuda.mem_alloc(out_host.nbytes)
+        bindings = [None] * engine.num_bindings
+        bindings[input_idx] = int(d_input)
+        bindings[output_idx] = int(d_output)
+
+        cuda.memcpy_htod_async(d_input, pixel_values, stream)
+        ok = context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+        if not ok:
+            raise RuntimeError("TensorRT execute_async_v2 returned failure")
+        cuda.memcpy_dtoh_async(out_host, d_output, stream)
+        stream.synchronize()
+
+    pred = np.array(out_host, dtype=np.float32).squeeze()
+    if pred.ndim != 2:
+        raise RuntimeError(f"Unexpected TensorRT depth output shape: {pred.shape}")
+    return cv2.resize(pred, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR)
+
+
+def get_depth_anything_v1_trt_runtime():
+    global _DEPTH_ANYTHING_V1_TRT_RUNTIME
+    if _DEPTH_ANYTHING_V1_TRT_RUNTIME is not None:
+        return _DEPTH_ANYTHING_V1_TRT_RUNTIME
+    rt = _create_trt_depth_runtime(
+        _resolve_depth_anything_v1_trt_engine_path(),
+        _resolve_depth_anything_v1_model_id(),
+        "Depth Anything V1 TRT",
+    )
+    _DEPTH_ANYTHING_V1_TRT_RUNTIME = rt
+    return _DEPTH_ANYTHING_V1_TRT_RUNTIME
+
+
+def get_depth_anything_v2_trt_runtime():
+    global _DEPTH_ANYTHING_V2_TRT_RUNTIME
+    if _DEPTH_ANYTHING_V2_TRT_RUNTIME is not None:
+        return _DEPTH_ANYTHING_V2_TRT_RUNTIME
+    _DEPTH_ANYTHING_V2_TRT_RUNTIME = _create_trt_depth_runtime(
+        _resolve_depth_anything_v2_trt_engine_path(),
+        _resolve_depth_anything_v2_model_id(),
+        "Depth Anything V2 TRT",
+    )
+    return _DEPTH_ANYTHING_V2_TRT_RUNTIME
+
+def get_sam_predictor():
+    """Lazy loader for SAM to keep YOLO startup fast."""
+    global _SAM_PREDICTOR
+    if _SAM_PREDICTOR is not None:
+        return _SAM_PREDICTOR
+
+    from segment_anything import sam_model_registry, SamPredictor
+    
+    # Resolve absolute path to models directory
+    base_dir = Path(__file__).resolve().parent.parent
+    checkpoint = base_dir / "models" / "sam_vit_h_4b8939.pth"
+    
+    if not checkpoint.exists():
+        logger.error(f"SAM weights not found at: {checkpoint}")
+        raise FileNotFoundError(f"SAM weights not found at: {checkpoint}")
+
+    model_type = "vit_h"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    logger.info(f"Loading SAM ({model_type}) on {device}...")
+    sam = sam_model_registry[model_type](checkpoint=checkpoint)
+    sam.to(device=device)
+    _SAM_PREDICTOR = SamPredictor(sam)
+    return _SAM_PREDICTOR
+
+
+def _validate_scale_factor(name: str, value: float) -> float:
+    v = float(value)
+    if not (0.0 < v <= 1.0):
+        raise ValueError(f"{name} must be in (0,1], got {value}")
+    return v
+
+
+def _validate_confidence(value: float) -> float:
+    v = float(value)
+    if not (0.0 <= v <= 1.0):
+        raise ValueError(f"conf must be in [0,1], got {value}")
+    return v
+
+
+def _validate_task(value: str) -> str:
+    v = str(value).strip().lower()
+    if v not in {"auto", "detect", "segment"}:
+        raise ValueError(f"task must be one of: auto, detect, segment (got {value})")
+    return v
+
+
+def _scale_detection_geometry(det: dict, scale_back_x: float, scale_back_y: float) -> None:
+    box = det.get('box')
+    if isinstance(box, dict):
+        for k in ('x1', 'x2', 'xmin', 'xmax'):
+            if k in box and box[k] is not None:
+                box[k] = float(box[k]) * scale_back_x
+        for k in ('y1', 'y2', 'ymin', 'ymax'):
+            if k in box and box[k] is not None:
+                box[k] = float(box[k]) * scale_back_y
+
+    segs = det.get('segments')
+    if isinstance(segs, list):
+        scaled_segments = []
+        for point in segs:
+            if isinstance(point, (list, tuple)) and len(point) >= 2:
+                scaled_segments.append([
+                    float(point[0]) * scale_back_x,
+                    float(point[1]) * scale_back_y,
+                ])
+        det['segments'] = scaled_segments
+
+
+def _coerce_image_array(np_arr: np.ndarray) -> np.ndarray:
+    if np_arr.ndim == 2:
+        np_arr = np.stack([np_arr] * 3, axis=-1)
+    elif np_arr.ndim == 3 and np_arr.shape[2] == 1:
+        np_arr = np.repeat(np_arr, 3, axis=2)
+    elif np_arr.ndim == 3 and np_arr.shape[2] >= 3:
+        np_arr = np_arr[:, :, :3]
+    else:
+        raise ValueError(f"Unsupported array shape for image conversion: {np_arr.shape}")
+
+    if np_arr.dtype == np.uint8:
+        return np_arr
+    if np_arr.dtype == np.uint16:
+        # preserve dynamic range when down-casting to 8-bit
+        return (np_arr / 257.0).clip(0, 255).astype(np.uint8)
+    return np.clip(np_arr, 0, 255).astype(np.uint8)
+
+
+def _read_env_int(name: str, default: int | None = None) -> int | None:
+    raw = os.getenv(name, "").strip()
+    if not raw:
+        return default
+    try:
+        return int(raw)
+    except Exception:
+        return default
+
+
+def _decode_uploaded_file_to_rgb_array(
+    image_bytes: bytes,
+    raw_format: str,
+    raw_width: int | None,
+    raw_height: int | None,
+    raw_channels: int,
+    raw_dtype: str,
+    raw_offset_bytes: int = 0,
+) -> np.ndarray:
+    fmt = str(raw_format or "auto").strip().lower()
+    if fmt not in {"auto", "image", "npy", "binary"}:
+        raise ValueError(f"raw_format must be one of: auto, image, npy, binary (got {raw_format})")
+
+    pil_err = None
+    npy_err = None
+
+    if fmt in {"auto", "image"}:
+        try:
+            pil_img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+            return np.array(pil_img)
+        except Exception as e:
+            pil_err = e
+            if fmt == "image":
+                raise
+
+    if fmt in {"auto", "npy"}:
+        try:
+            arr = np.load(io.BytesIO(image_bytes), allow_pickle=False)
+            if not isinstance(arr, np.ndarray):
+                raise ValueError("npy payload did not contain an ndarray")
+            return _coerce_image_array(arr)
+        except Exception as e:
+            npy_err = e
+            if fmt == "npy":
+                raise
+
+    if fmt in {"auto", "binary"}:
+        if raw_width is None or raw_height is None:
+            if fmt == "binary":
+                raise ValueError("raw_width and raw_height are required for raw_format=binary")
+        else:
+            try:
+                dtype = np.dtype(raw_dtype)
+            except Exception as e:
+                raise ValueError(f"Invalid raw_dtype '{raw_dtype}': {e}") from e
+
+            channels = int(raw_channels)
+            if channels < 1:
+                raise ValueError("raw_channels must be >= 1")
+
+            offset = int(raw_offset_bytes)
+            total = len(image_bytes)
+            if offset < 0 or offset > total:
+                raise ValueError(f"raw_offset_bytes must be in [0,{total}], got {offset}")
+
+            payload = memoryview(image_bytes)[offset:]
+            expected = int(raw_width) * int(raw_height) * channels * dtype.itemsize
+            got = len(payload)
+            if got != expected:
+                raise ValueError(
+                    f"Raw byte size mismatch: payload={got} bytes (total={total}, offset={offset}), "
+                    f"expected {expected} for shape=({raw_height},{raw_width},{channels}) dtype={dtype}"
+                )
+
+            arr = np.frombuffer(payload, dtype=dtype)
+            if channels == 1:
+                arr = arr.reshape((int(raw_height), int(raw_width)))
+            else:
+                arr = arr.reshape((int(raw_height), int(raw_width), channels))
+            return _coerce_image_array(arr)
+
+    raise ValueError(
+        "Could not decode upload as image, npy, or binary raw frame. "
+        f"PIL error: {pil_err}; npy error: {npy_err}. "
+        "For raw binary, pass raw_format=binary&raw_width=...&raw_height=... (optionally raw_channels, raw_dtype, raw_offset)."
+    )
+
+def get_polygon_from_bbox(img_np, box):
+    """
+    Uses specialized CV pipeline to find loop edges:
+    Denoise -> CLAHE -> Canny -> Dilate -> Largest Contour -> Approx.
+    """
+    try:
+        # Map common YOLO JSON keys to local variables
+        x1 = box.get('x1') or box.get('xmin')
+        y1 = box.get('y1') or box.get('ymin')
+        x2 = box.get('x2') or box.get('xmax')
+        y2 = box.get('y2') or box.get('ymax') # FIX: Added box.get('y2')
+    
+        if None in (x1, y1, x2, y2):
+            logger.warning(f"Invalid box coordinates: {box}")
+            return []
+            
+        x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])
+    except (KeyError, TypeError, ValueError) as e:
+        logger.error(f"Error parsing box coordinates: {e}")
+        return []
+
+    # Crop the image to the bounding box
+    roi = img_np[y1:y2, x1:x2]
+    if roi.size == 0:
+        return []
+
+    # Convert to grayscale and enhance
+    gray = cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY)
+    
+    # 1. Aggressive Denoising (loops are organic, noise is high-frequency)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    
+    # 2. Local Contrast Enhancement
+    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+    enhanced = clahe.apply(blurred)
+
+    # 3. Multi-scale Edge Detection
+    # We use a wide range for Canny to capture faint edges
+    v = np.median(enhanced)
+    lower = int(max(0, (1.0 - 0.33) * v))
+    upper = int(min(255, (1.0 + 0.33) * v))
+    edges = cv2.Canny(enhanced, lower, upper)
+
+    # 4. Morphological Closing to bridge gaps in the loop circle
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
+    closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
+    
+    # 5. Fill the holes (if the loop is a closed circle, this captures the whole interior)
+    # This is very effective for 'loop_all'
+    contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if not contours:
+        return []
+
+    # Take the largest contour by area
+    largest_cnt = max(contours, key=cv2.contourArea)
+    
+    # Ensure a smooth, closed shape for the loop
+    largest_cnt = cv2.convexHull(largest_cnt)
+
+    # Simplify the contour for the frontend
+    epsilon = 0.01 * cv2.arcLength(largest_cnt, True)
+    approx = cv2.approxPolyDP(largest_cnt, epsilon, True)
+
+    # Shift points back to global image coordinates
+    polygon = []
+    for p in approx:
+        polygon.append([float(p[0][0] + x1), float(p[0][1] + y1)])
+
+    return polygon
+
+def load_model(name: str, task: str | None = None):
+    # allow absolute paths, repo-root models/<name>, or a bare filename
+    repo_models_dir = Path(__file__).resolve().parent.parent / 'models'
+    
+    if name == "DEFAULT":
+        # Check for engine first - if the user provided it, they want to use it
+        if GLOBAL_ENGINE_PATH and os.path.exists(GLOBAL_ENGINE_PATH):
+            resolved = Path(GLOBAL_ENGINE_PATH).resolve()
+        elif GLOBAL_PT_PATH and os.path.exists(GLOBAL_PT_PATH):
+            resolved = Path(GLOBAL_PT_PATH).resolve()
+        else:
+            # Original search logic fallback
+            name = os.environ.get('DEFAULT_MODEL', 'best.pt')
+            input_path = Path(name)
+            # prefer explicit paths if they exist
+            if input_path.is_absolute() or input_path.exists():
+                resolved = input_path.resolve()
+            else:
+                candidate = repo_models_dir / name
+                # try exact candidate first
+                if candidate.exists():
+                    resolved = candidate.resolve()
+                else:
+                    # try common extensions
+                    resolved = None
+                    for ext in ('.pt', '.engine'):
+                        cand = candidate.with_suffix(ext) if not candidate.suffix else candidate
+                        if cand.exists():
+                            resolved = cand.resolve()
+                            break
+                    if resolved is None:
+                        raise FileNotFoundError(f"Model file not found: {name} (searched {input_path} and {repo_models_dir})")
+
+    else:
+        input_path = Path(name)
+        if input_path.is_absolute() or input_path.exists():
+            resolved = input_path.resolve()
+        else:
+            # search in models dir
+            resolved = repo_models_dir / name
+            if not resolved.exists():
+                 raise FileNotFoundError(f"Model file not found: {name}")
+
+    effective_task = _validate_task(task if task is not None else GLOBAL_TASK)
+
+    path_str = str(resolved)
+    cache_key = f"{path_str}::task={effective_task}"
+    if cache_key in _MODEL_CACHE:
+        return _MODEL_CACHE[cache_key]
+
+    # lazy import to avoid requiring ultralytics at module import time
+    from ultralytics import YOLO
+
+    # Load whatever is requested (engine or pt).
+    # Ultralytics handles backend by extension; task can be forced for engine exports.
+    logger.info("Loading model file: %s (task=%s)", path_str, effective_task)
+    kwargs = {} if effective_task == "auto" else {"task": effective_task}
+    m = YOLO(path_str, **kwargs)
+
+    _MODEL_CACHE[cache_key] = m
+    return m
+
+@app.post('/predict/')
+async def predict(
+    file: Optional[UploadFile] = File(None),
+    array: Optional[List] = Body(None),
+    model: Optional[str] = Query(None),
+    conf: Optional[float] = Query(None, ge=0.0, le=1.0),
+    scale: Optional[float] = Query(None, gt=0.0, le=1.0),
+    infer_scale: Optional[float] = Query(None, gt=0.0, le=1.0),
+    task: Optional[str] = Query(None),
+    raw_format: str = Query("auto"),
+    raw_width: Optional[int] = Query(None, ge=1),
+    raw_height: Optional[int] = Query(None, ge=1),
+    raw_channels: Optional[int] = Query(None, ge=1, le=8),
+    raw_dtype: Optional[str] = Query(None),
+    raw_offset: Optional[int] = Query(None, ge=0),
+):
+    model_name = model or "DEFAULT"
+    logger.info("Predict request received; model=%s", model_name)
+    effective_task = str(GLOBAL_TASK if task is None else task).strip().lower()
+    try:
+        effective_task = _validate_task(effective_task)
+    except ValueError as e:
+        return {"error": str(e)}
+
+    try:
+        m = load_model(model_name, effective_task)
+        logger.info("Loaded model: %s (task=%s)", model_name, effective_task)
+    except Exception as e:
+        logger.exception("Failed to load model %s", model_name)
+        return {"error": f"Failed to load model {model_name}: {e}"}
+
+    if array is None and file is None:
+        logger.warning("No input provided: both 'file' and 'array' are None")
+        return {"error": "Either 'file' (multipart upload) or JSON body 'array' (nested lists) must be provided."}
+
+    # If JSON ndarray provided, convert to numpy array (expect nested lists)
+    if array is not None:
+        try:
+            np_arr = np.array(array)
+            logger.info("Received array: shape=%s, dtype=%s, min=%s, max=%s", np_arr.shape, getattr(np_arr, "dtype", None), np_arr.min() if np_arr.size else None, np_arr.max() if np_arr.size else None)
+            # If grayscale provided as 2D, convert to 3-channel
+            if np_arr.ndim == 2:
+                logger.info("Converting 2D grayscale to 3-channel RGB")
+                np_arr = np.stack([np_arr]*3, axis=-1)
+            # Ensure uint8 and values in 0-255
+            if np_arr.dtype != np.uint8:
+                logger.info("Casting array dtype %s to uint8 with clipping", np_arr.dtype)
+                np_arr = np.clip(np_arr, 0, 255).astype(np.uint8)
+            # Convert to PIL Image (YOLO.predict accepts arrays too, but keeping consistent)
+            img = Image.fromarray(np_arr)
+            logger.info("Converted array to PIL Image: mode=%s, size=%s", img.mode, img.size)
+        except Exception as e:
+            logger.exception("Failed to parse 'array' body into an image")
+            return {"error": f"Failed to parse 'array' body into an image: {e}"}
+    else:
+        # file provided path
+        try:
+            image_bytes = await file.read()
+            logger.info("Read uploaded file: filename=%s, bytes=%d", getattr(file, "filename", None), len(image_bytes))
+            filename = str(getattr(file, "filename", "") or "")
+            env_raw_width = _read_env_int("AARELC_RAW_WIDTH")
+            env_raw_height = _read_env_int("AARELC_RAW_HEIGHT")
+            env_raw_channels = _read_env_int("AARELC_RAW_CHANNELS", 1)
+            env_raw_offset = _read_env_int("AARELC_RAW_OFFSET_BYTES", 0)
+
+            effective_raw_format = (raw_format or "auto").strip().lower()
+            if effective_raw_format == "auto" and filename.lower().endswith((".raw", ".bin", ".dat")):
+                effective_raw_format = "binary"
+            effective_raw_width = raw_width if raw_width is not None else env_raw_width
+            effective_raw_height = raw_height if raw_height is not None else env_raw_height
+            effective_raw_channels = int(raw_channels if raw_channels is not None else env_raw_channels)
+            effective_raw_dtype = str(raw_dtype or os.getenv("AARELC_RAW_DTYPE", "uint8"))
+            effective_raw_offset = int(raw_offset if raw_offset is not None else env_raw_offset)
+
+            decoded_np = _decode_uploaded_file_to_rgb_array(
+                image_bytes=image_bytes,
+                raw_format=effective_raw_format,
+                raw_width=effective_raw_width,
+                raw_height=effective_raw_height,
+                raw_channels=effective_raw_channels,
+                raw_dtype=effective_raw_dtype,
+                raw_offset_bytes=effective_raw_offset,
+            )
+            img = Image.fromarray(decoded_np)
+            logger.info(
+                "Decoded upload to RGB array: filename=%s shape=%s dtype=%s raw_format=%s raw_width=%s raw_height=%s raw_channels=%s raw_dtype=%s raw_offset=%s",
+                filename,
+                decoded_np.shape,
+                decoded_np.dtype,
+                effective_raw_format,
+                effective_raw_width,
+                effective_raw_height,
+                effective_raw_channels,
+                effective_raw_dtype,
+                effective_raw_offset,
+            )
+        except Exception as e:
+            logger.exception("Failed to read uploaded file")
+            return {"error": f"Failed to read uploaded file: {e}"}
+
+    original_np = np.array(img)
+    # Ultralytics low-level preprocess assumes OpenCV-style BGR ndarray input.
+    # Keep RGB only for downstream polygon extraction.
+    original_bgr = cv2.cvtColor(original_np, cv2.COLOR_RGB2BGR)
+    original_h, original_w = original_np.shape[:2]
+
+    effective_conf = float(GLOBAL_CONF_THRESHOLD if conf is None else conf)
+    effective_scale = float(GLOBAL_SCALE_FACTOR if scale is None else scale)
+    effective_infer_scale = float(GLOBAL_INFER_SCALE_FACTOR if infer_scale is None else infer_scale)
+    try:
+        effective_conf = _validate_confidence(effective_conf)
+        effective_scale = _validate_scale_factor('scale', effective_scale)
+        effective_infer_scale = _validate_scale_factor('infer_scale', effective_infer_scale)
+    except ValueError as e:
+        return {"error": str(e)}
+
+    # Inference path uses BGR to match zmq_inference.py behavior.
+    work_np = original_bgr
+    if effective_scale < 1.0:
+        scaled_w = max(1, int(round(original_w * effective_scale)))
+        scaled_h = max(1, int(round(original_h * effective_scale)))
+        work_np = cv2.resize(work_np, (scaled_w, scaled_h), interpolation=cv2.INTER_AREA)
+
+    infer_np = work_np
+    if effective_infer_scale < 1.0:
+        infer_h, infer_w = infer_np.shape[:2]
+        scaled_w = max(1, int(round(infer_w * effective_infer_scale)))
+        scaled_h = max(1, int(round(infer_h * effective_infer_scale)))
+        infer_np = cv2.resize(infer_np, (scaled_w, scaled_h), interpolation=cv2.INTER_AREA)
+
+    infer_h, infer_w = infer_np.shape[:2]
+    scale_back_x = float(original_w) / float(infer_w)
+    scale_back_y = float(original_h) / float(infer_h)
+
+    logger.info(
+        "Predict settings: conf=%.3f scale=%.3f infer_scale=%.3f infer_input=%dx%d original=%dx%d input_order=BGR",
+        effective_conf,
+        effective_scale,
+        effective_infer_scale,
+        infer_w,
+        infer_h,
+        original_w,
+        original_h,
+    )
+
+    start = time.perf_counter()
+    try:
+        logger.info("Executing manual GPU->CPU hand-off for Orin stability")
+        
+        # 1. Initialize the internal predictor if it doesn't exist
+        if not m.predictor:
+            m.predict(np.zeros((640, 640, 3), dtype=np.uint8), device=0)
+        
+        # 2. Use the low-level predictor steps
+        # This allows us to run inference on GPU but post-process on CPU
+        p = m.predictor
+        p.args.conf = effective_conf
+        p.args.iou = 0.45
+        
+        # Pre-process image for the model (CPU/GPU)
+        im = p.preprocess([infer_np])
+        
+        # RUN INFERENCE ON GPU (The heavy lifting)
+        with torch.no_grad():
+            preds = p.inference(im)
+        
+        # MOVE RAW PREDICTIONS TO CPU IMMEDIATELY
+        # This is the "Magic" that prevents the torchvision::nms crash
+        if isinstance(preds, (list, tuple)):
+            preds = [x.cpu() for x in preds]
+        else:
+            preds = preds.cpu()
+        
+        # RUN POST-PROCESS ON CPU (The cleanup)
+        # We pass the CPU tensors so it uses the working CPU NMS kernel
+        results = p.postprocess(preds, im.cpu(), [infer_np])
+        
+        logger.info("Inference (GPU) and Post-process (CPU) successful")
+    except Exception as e:
+        logger.exception("Error during manual model prediction")
+        return {"error": f"Model prediction failed: {e}"}
+            
+    elapsed_ms = (time.perf_counter() - start) * 1000.0
+    try:
+        parsed = json.loads(results[0].to_json())
+        # parsed is a list of detections
+        num_detections = len(parsed) if isinstance(parsed, list) else 0
+        logger.info("Prediction finished: time_ms=%.2f, detections=%d", elapsed_ms, num_detections)
+    except Exception as e:
+        logger.exception("Failed to parse model results to JSON")
+        return {"error": f"Failed to parse model results: {e}"}
+
+    # Map detections back to original image coordinates when scaling is applied.
+    try:
+        if isinstance(parsed, list) and (scale_back_x != 1.0 or scale_back_y != 1.0):
+            for det in parsed:
+                if isinstance(det, dict):
+                    _scale_detection_geometry(det, scale_back_x, scale_back_y)
+    except Exception:
+        logger.exception("Failed to rescale detection geometry")
+
+    # Polygon generation: prefer YOLO mask polygons (same source as zmq_inference.py),
+    # then fall back to contour-from-bbox only when mask polygons are unavailable.
+    try:
+        yolo_segments: list[list[list[float]]] = []
+        result0 = results[0] if isinstance(results, (list, tuple)) else results
+        masks = getattr(result0, 'masks', None)
+        masks_xy = masks.xy if (masks is not None and hasattr(masks, 'xy')) else None
+        if masks_xy is not None:
+            for poly in masks_xy:
+                try:
+                    arr = np.asarray(poly, dtype=np.float32)
+                    if arr.ndim == 2 and arr.shape[0] >= 3:
+                        arr_scaled = np.stack([
+                            arr[:, 0] * scale_back_x,
+                            arr[:, 1] * scale_back_y,
+                        ], axis=1)
+                        yolo_segments.append(arr_scaled.tolist())
+                    else:
+                        yolo_segments.append([])
+                except Exception:
+                    yolo_segments.append([])
+
+        if isinstance(parsed, list):
+            for idx, det in enumerate(parsed):
+                if not isinstance(det, dict):
+                    continue
+
+                seg = yolo_segments[idx] if idx < len(yolo_segments) else []
+                if seg:
+                    det['segments'] = seg
+                else:
+                    # YOLO JSON typically looks like {"box": {"x1":..., "y1":...}, "conf":..., ...}
+                    box_data = det.get('box', det)
+                    det['segments'] = get_polygon_from_bbox(original_np, box_data)
+    except Exception:
+        logger.exception("Failed to generate segments")
+        # We don't return an error here so the user at least gets the boxes
+
+    return {
+        "model": model_name,
+        "inference_time_ms": elapsed_ms,
+        "settings": {
+            "conf": effective_conf,
+            "scale": effective_scale,
+            "infer_scale": effective_infer_scale,
+            "task": effective_task,
+        },
+        "results": parsed,
+    }
+
+
+@app.post('/depth/')
+async def predict_depth(
+    file: Optional[UploadFile] = File(None),
+    array: Optional[List] = Body(None),
+    colorize: bool = Query(True),
+    depth_model: str = Query("depthpro"),
+    depth_max_side: int = Query(0, ge=0, le=4096),
+):
+    """Run depth inference and return depth map as PNG bytes."""
+    req_t0 = time.perf_counter()
+    if array is None and file is None:
+        return {"error": "Either 'file' or JSON body 'array' must be provided."}
+
+    backend = (depth_model or "depthpro").strip().lower()
+    if backend in {"depth-anything-v2", "depthanythingv2", "dav2"}:
+        backend = "depth_anything_v2"
+    if backend in {"depth-anything-v2-tiny", "depthanythingv2tiny", "dav2tiny"}:
+        backend = "depth_anything_v2_tiny"
+    if backend in {"depth-anything-v1", "depthanythingv1", "dav1"}:
+        backend = "depth_anything_v1"
+    if backend in {"depth-anything-v1-trt", "depthanythingv1trt", "dav1trt"}:
+        backend = "depth_anything_v1_trt"
+    if backend in {"depth-anything-v2-trt", "depthanythingv2trt", "dav2trt"}:
+        backend = "depth_anything_v2_trt"
+    if backend in {"midas-v21-small", "midasv21small", "midas_small"}:
+        backend = "midas_v21_small"
+    if backend not in {
+        "depthpro",
+        "depth_anything_v1",
+        "depth_anything_v1_trt",
+        "depth_anything_v2",
+        "depth_anything_v2_tiny",
+        "depth_anything_v2_trt",
+        "midas_v21_small",
+    }:
+        return {
+            "error": (
+                f"Unsupported depth_model '{depth_model}'. Use one of: "
+                "'depthpro', 'depth_anything_v1', 'depth_anything_v1_trt', "
+                "'depth_anything_v2', 'depth_anything_v2_tiny', 'depth_anything_v2_trt', "
+                "'midas_v21_small'."
+            )
+        }
+
+    try:
+        if backend == "depthpro":
+            runtime = get_depthpro_runtime()
+        elif backend == "depth_anything_v1":
+            runtime = get_depth_anything_v1_runtime()
+        elif backend == "depth_anything_v1_trt":
+            runtime = get_depth_anything_v1_trt_runtime()
+        elif backend == "depth_anything_v2":
+            runtime = get_depth_anything_v2_runtime()
+        elif backend == "depth_anything_v2_tiny":
+            runtime = get_depth_anything_v2_tiny_runtime()
+        elif backend == "midas_v21_small":
+            runtime = get_midas_v21_small_runtime()
+        else:
+            runtime = get_depth_anything_v2_trt_runtime()
+    except Exception as e:
+        logger.exception("Failed to load depth runtime backend=%s", backend)
+        return {"error": f"Failed to load depth model backend={backend}: {e}"}
+
+    try:
+        prep_t0 = time.perf_counter()
+        if array is not None:
+            np_arr = np.array(array)
+            if np_arr.ndim == 2:
+                np_arr = np.stack([np_arr] * 3, axis=-1)
+            if np_arr.dtype != np.uint8:
+                np_arr = np.clip(np_arr, 0, 255).astype(np.uint8)
+            img_bgr = cv2.cvtColor(np_arr, cv2.COLOR_RGB2BGR)
+        else:
+            image_bytes = await file.read()
+            img_bgr = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
+            if img_bgr is None:
+                return {"error": "Failed to decode uploaded image."}
+        prep_ms = (time.perf_counter() - prep_t0) * 1000.0
+    except Exception as e:
+        logger.exception("Failed to parse depth input")
+        return {"error": f"Failed to parse input image: {e}"}
+
+    try:
+        with _DEPTH_GPU_LOCK:
+            orig_h, orig_w = img_bgr.shape[:2]
+            infer_t0 = time.perf_counter()
+            img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+            if backend == "depthpro":
+                # Depth Pro transform expects RGB image; output depth is metric-like relative map.
+                x = runtime["transform"](img_rgb)
+                with torch.no_grad():
+                    pred = runtime["model"].infer(x, f_px=None)
+                depth = pred["depth"].detach().float().cpu().numpy().squeeze()
+            elif backend in {"depth_anything_v1", "depth_anything_v2", "depth_anything_v2_tiny", "midas_v21_small"}:
+                configured_max_side = _resolve_depth_anything_v2_max_side()
+                target_max_side = depth_max_side if depth_max_side > 0 else configured_max_side
+                model_rgb = img_rgb
+                if target_max_side > 0:
+                    current_max = max(orig_h, orig_w)
+                    if current_max > target_max_side:
+                        scale = float(target_max_side) / float(current_max)
+                        scaled_w = max(1, int(round(orig_w * scale)))
+                        scaled_h = max(1, int(round(orig_h * scale)))
+                        model_rgb = cv2.resize(img_rgb, (scaled_w, scaled_h), interpolation=cv2.INTER_AREA)
+
+                inputs = runtime["processor"](images=model_rgb, return_tensors="pt")
+                inputs = {k: v.to(runtime["device"]) for k, v in inputs.items()}
+                with torch.no_grad():
+                    outputs = runtime["model"](**inputs)
+                    pred = outputs.predicted_depth  # (B, H, W)
+                    pred = torch.nn.functional.interpolate(
+                        pred.unsqueeze(1),
+                        size=(orig_h, orig_w),
+                        mode="bilinear",
+                        align_corners=False,
+                    )
+                depth = pred.squeeze().detach().float().cpu().numpy()
+            else:
+                configured_max_side = _resolve_depth_anything_v2_max_side()
+                target_max_side = depth_max_side if depth_max_side > 0 else configured_max_side
+                model_rgb = img_rgb
+                if target_max_side > 0:
+                    current_max = max(orig_h, orig_w)
+                    if current_max > target_max_side:
+                        scale = float(target_max_side) / float(current_max)
+                        scaled_w = max(1, int(round(orig_w * scale)))
+                        scaled_h = max(1, int(round(orig_h * scale)))
+                        model_rgb = cv2.resize(img_rgb, (scaled_w, scaled_h), interpolation=cv2.INTER_AREA)
+                depth = _infer_depth_anything_trt(runtime, model_rgb, orig_h, orig_w)
+
+                # Extra sync before leaving TRT path to reduce stream/context hazards with torch.
+                if torch.cuda.is_available():
+                    torch.cuda.synchronize()
+            infer_ms = (time.perf_counter() - infer_t0) * 1000.0
+
+        if depth.ndim != 2:
+            return {"error": f"Unexpected depth output shape: {depth.shape}"}
+
+        post_t0 = time.perf_counter()
+        depth = np.nan_to_num(depth.astype(np.float32), nan=0.0, posinf=0.0, neginf=0.0)
+        dmin, dmax = float(depth.min()), float(depth.max())
+        if dmax <= dmin:
+            normalized = np.zeros_like(depth, dtype=np.uint8)
+        else:
+            normalized = ((depth - dmin) / (dmax - dmin) * 255.0).astype(np.uint8)
+
+        normalized = cv2.resize(normalized, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR)
+        output = cv2.applyColorMap(normalized, cv2.COLORMAP_INFERNO) if colorize else normalized
+
+        ok, png_buf = cv2.imencode('.png', output)
+        if not ok:
+            return {"error": "Failed to encode depth map PNG."}
+        post_ms = (time.perf_counter() - post_t0) * 1000.0
+        total_ms = (time.perf_counter() - req_t0) * 1000.0
+        logger.info(
+            "Depth timing: backend=%s total_ms=%.2f prep_ms=%.2f infer_ms=%.2f post_ms=%.2f "
+            "image=%dx%d depth_max_side=%d colorize=%s",
+            backend, total_ms, prep_ms, infer_ms, post_ms, orig_w, orig_h, depth_max_side, colorize
+        )
+        return Response(content=png_buf.tobytes(), media_type="image/png")
+    except Exception as e:
+        logger.exception("Depth prediction failed")
+        return {"error": f"Depth prediction failed: {e}"}
+
+@app.post('/sam/')
+async def sam_segment(
+    file: UploadFile = File(...),
+    point_x: int = Query(...),
+    point_y: int = Query(...),
+    label: int = Query(1) # 1 for foreground
+):
+    """Segment an object using a single point prompt."""
+    try:
+        image_bytes = await file.read()
+        img_np = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
+        img_rgb = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
+
+        predictor = get_sam_predictor()
+        predictor.set_image(img_rgb)
+
+        input_point = np.array([[point_x, point_y]])
+        input_label = np.array([label])
+
+        masks, scores, _ = predictor.predict(
+            point_coords=input_point,
+            point_labels=input_label,
+            multimask_output=True,
+        )
+
+        # Pick the best mask
+        best_idx = np.argmax(scores)
+        mask = masks[best_idx].astype(np.uint8) * 255
+
+        # Convert mask to polygon
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            return {"polygon": []}
+
+        cnt = max(contours, key=cv2.contourArea)
+        
+        # Smoothing for clean YOLO annotations
+        epsilon = 0.002 * cv2.arcLength(cnt, True)
+        approx = cv2.approxPolyDP(cnt, epsilon, True)
+        
+        polygon = approx.reshape(-1, 2).tolist()
+        return {
+            "polygon": polygon, 
+            "score": float(scores[best_idx]),
+            "center": [point_x, point_y]
+        }
+    except Exception as e:
+        logger.exception("SAM Error")
+        return {"error": str(e)}
+
+@app.post('/sam_box/')
+async def sam_segment_box(
+        file: UploadFile = File(...),
+        x1: int = Query(...),
+        y1: int = Query(...),
+        x2: int = Query(...),
+        y2: int = Query(...)
+):
+    """Segment an object using a bounding box prompt."""
+    try:
+        image_bytes = await file.read()
+        img_np = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
+        img_rgb = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
+
+        predictor = get_sam_predictor()
+        predictor.set_image(img_rgb)
+
+        # SAM expects [x1, y1, x2, y2]
+        input_box = np.array([x1, y1, x2, y2])
+
+        masks, scores, _ = predictor.predict(
+            point_coords=None,
+            point_labels=None,
+            box=input_box[None, :],
+            multimask_output=False,
+        )
+
+        mask = masks[0].astype(np.uint8) * 255
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        if not contours:
+            return {"polygon": []}
+
+        cnt = max(contours, key=cv2.contourArea)
+        epsilon = 0.002 * cv2.arcLength(cnt, True)
+        approx = cv2.approxPolyDP(cnt, epsilon, True)
+
+        return {
+            "polygon": approx.reshape(-1, 2).tolist(),
+            "score": float(scores[0])
+        }
+    except Exception as e:
+        logger.exception("SAM Box Error")
+        return {"error": str(e)}
+
+
+@app.post('/sam_multi_box/')
+async def sam_segment_multi_box(
+        file: UploadFile = File(...),
+        boxes_json: str = Query(...)  # Expecting list of [x1, y1, x2, y2]
+):
+    """Segment multiple objects using a list of bounding box prompts."""
+    try:
+        boxes = json.loads(boxes_json)
+        if not boxes:
+            return {"results": []}
+
+        image_bytes = await file.read()
+        img_np = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
+        img_rgb = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
+
+        predictor = get_sam_predictor()
+        predictor.set_image(img_rgb)
+
+        results = []
+        for box in boxes:
+            input_box = np.array(box)
+            masks, scores, _ = predictor.predict(
+                point_coords=None,
+                point_labels=None,
+                box=input_box[None, :],
+                multimask_output=False,
+            )
+
+            mask = masks[0].astype(np.uint8) * 255
+            contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+            poly = []
+            if contours:
+                cnt = max(contours, key=cv2.contourArea)
+                epsilon = 0.002 * cv2.arcLength(cnt, True)
+                approx = cv2.approxPolyDP(cnt, epsilon, True)
+                poly = approx.reshape(-1, 2).tolist()
+
+            results.append({
+                "polygon": poly,
+                "score": float(scores[0])
+            })
+
+        return {"results": results}
+    except Exception as e:
+        logger.exception("SAM Multi Box Error")
+        return {"error": str(e)}
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--host', default='0.0.0.0')
+    parser.add_argument('--port', type=int, default=8002)
+    parser.add_argument('--pt', default='models/best.pt', help='Path to default .pt model')
+    parser.add_argument('--engine', default='models/best_v12_22092025.engine', help='Path to default .engine model')
+    parser.add_argument('--conf', type=float, default=0.25, help='Default confidence threshold in [0,1] for /predict')
+    parser.add_argument('--scale', type=float, default=1.0, help='Default pre-inference scale factor in (0,1] for /predict')
+    parser.add_argument('--infer-scale', type=float, default=1.0, help='Default inference scale factor in (0,1] for /predict')
+    parser.add_argument('--task', choices=['auto', 'detect', 'segment'], default='auto', help='Default YOLO task for /predict')
+    parser.add_argument('--depthpro-checkpoint', default='models/depth_pro.pt', help='Path to Apple Depth Pro checkpoint (.pt)')
+    parser.add_argument('--depth-anything-v1-model-id', default='LiheYoung/depth-anything-small-hf', help='HF model id for Depth Anything V1 backend')
+    parser.add_argument('--depth-anything-v2-model-id', default='depth-anything/Depth-Anything-V2-Small-hf', help='HF model id for Depth Anything V2 backend')
+    parser.add_argument('--depth-anything-v2-tiny-model-id', default='depth-anything/Depth-Anything-V2-Small-hf', help='HF model id for Depth Anything V2 Tiny backend (fallback default uses Small-hf)')
+    parser.add_argument('--midas-v21-small-model-id', default='Intel/dpt-swinv2-tiny-256', help='HF model id for MiDaS small backend')
+    parser.add_argument('--depth-anything-v1-trt-engine', default='models/depth_anything_v1_small.engine', help='TensorRT engine path for Depth Anything V1 TRT backend')
+    parser.add_argument('--depth-anything-v2-trt-engine', default='models/depth_anything_v2_small.engine', help='TensorRT engine path for Depth Anything V2 TRT backend')
+    args = parser.parse_args()
+
+    GLOBAL_PT_PATH = args.pt
+    GLOBAL_ENGINE_PATH = args.engine
+    try:
+        GLOBAL_CONF_THRESHOLD = _validate_confidence(args.conf)
+        GLOBAL_SCALE_FACTOR = _validate_scale_factor('scale', args.scale)
+        GLOBAL_INFER_SCALE_FACTOR = _validate_scale_factor('infer_scale', args.infer_scale)
+        GLOBAL_TASK = _validate_task(args.task)
+    except ValueError as e:
+        raise SystemExit(str(e)) from e
+    os.environ["DEPTHPRO_CHECKPOINT_PATH"] = args.depthpro_checkpoint
+    os.environ["DEPTH_ANYTHING_V1_MODEL_ID"] = args.depth_anything_v1_model_id
+    os.environ["DEPTH_ANYTHING_V2_MODEL_ID"] = args.depth_anything_v2_model_id
+    os.environ["DEPTH_ANYTHING_V2_TINY_MODEL_ID"] = args.depth_anything_v2_tiny_model_id
+    os.environ["MIDAS_V21_SMALL_MODEL_ID"] = args.midas_v21_small_model_id
+    os.environ["DEPTH_ANYTHING_V1_TRT_ENGINE"] = args.depth_anything_v1_trt_engine
+    os.environ["DEPTH_ANYTHING_V2_TRT_ENGINE"] = args.depth_anything_v2_trt_engine
+
+    # WARM UP: Pre-load both YOLO and SAM models for Spark/Orin
+    try:
+        print(f"Pre-loading default YOLO model...")
+        load_model("DEFAULT")
+        
+        print(f"Pre-loading SAM model (vit_h)...")
+        get_sam_predictor()
+        
+        print(f"All models loaded successfully on GPU.")
+    except Exception as e:
+        print(f"Warning: Could not pre-load models: {e}")
+
+    logger.info(
+        "Starting Inference Server. PT: %s, Engine: %s, conf: %.3f, scale: %.3f, infer_scale: %.3f, task: %s, DepthPro: %s, DAV1: %s, DAV2: %s, DAV2Tiny: %s, MiDaS: %s, DAV1_TRT: %s, DAV2_TRT: %s",
+        args.pt,
+        args.engine,
+        GLOBAL_CONF_THRESHOLD,
+        GLOBAL_SCALE_FACTOR,
+        GLOBAL_INFER_SCALE_FACTOR,
+        GLOBAL_TASK,
+        args.depthpro_checkpoint,
+        args.depth_anything_v1_model_id,
+        args.depth_anything_v2_model_id,
+        args.depth_anything_v2_tiny_model_id,
+        args.midas_v21_small_model_id,
+        args.depth_anything_v1_trt_engine,
+        args.depth_anything_v2_trt_engine
+    )
+    uvicorn.run(app, host=args.host, port=args.port)
diff --git a/src/inference/z_m_q_inference_monitor.py b/src/inference/z_m_q_inference_monitor.py
new file mode 100644
index 0000000..ac29869
--- /dev/null
+++ b/src/inference/z_m_q_inference_monitor.py
@@ -0,0 +1,61 @@
+import zmq
+import json
+import time
+import argparse
+
+def main():
+    parser = argparse.ArgumentParser(description="Monitor YOLO ZMQ Inference Stream")
+    parser.add_argument('--addr', default='tcp://localhost:9091', help='ZMQ PUB address to subscribe to')
+    parser.add_argument('--topic', default='detections', help='Topic to subscribe to (empty for all)')
+    args = parser.parse_args()
+
+    ctx = zmq.Context()
+    sub = ctx.socket(zmq.SUB)
+    
+    # Configure for low latency
+    sub.setsockopt(zmq.RCVHWM, 1000)
+    sub.connect(args.addr)
+    sub.setsockopt_string(zmq.SUBSCRIBE, args.topic)
+
+    print(f"📡 Monitoring detections on {args.addr} (Topic: '{args.topic}')...")
+
+    msg_count = 0
+    start_time = time.time()
+
+    try:
+        while True:
+            # Check for multipart (topic + json) or single part (just json)
+            parts = sub.recv_multipart()
+            msg_count += 1
+        
+            # FIX: Logic to find the JSON header in a multipart message
+            # Message might be [Topic, JSON, Pixels] or just [JSON, Pixels]
+            header_data = None
+            for p in parts[:-1]: # Don't look at the last part (pixels)
+                try:
+                    decoded = p.decode('utf-8')
+                    if '{' in decoded: # Likely JSON
+                        header_data = json.loads(decoded)
+                        break
+                except: continue
+
+            if header_data:
+                boxes = header_data.get('boxes', [])
+                timestamp = header_data.get('time', 0)
+                latency = (time.time() - timestamp) * 1000 if timestamp > 0 else 0
+
+                print(f"[{msg_count:04d}] Latency: {latency:6.1f}ms | Detections: {len(boxes)} | Img: {header_data.get('shape')}")
+                for det in boxes:
+                    print(f"  └─ Class: {det['label']} Conf: {det['conf']:.2f}")
+            else:
+                print(f"[{msg_count:04d}] Error: Could not find valid JSON header in {len(parts)} parts")
+
+    except KeyboardInterrupt:
+        elapsed = time.time() - start_time
+        print(f"\nStopped. Processed {msg_count} messages in {elapsed:.1f}s ({msg_count/elapsed:.1f} msg/s)")
+    finally:
+        sub.close()
+        ctx.term()
+
+if __name__ == "__main__":
+    main()
diff --git a/src/inference/zmq_inference.py b/src/inference/zmq_inference.py
new file mode 100644
index 0000000..fa237c5
--- /dev/null
+++ b/src/inference/zmq_inference.py
@@ -0,0 +1,1400 @@
+"""
+Simple ZMQ subscriber + YOLO inference for Jetson
+- Connects to a ZeroMQ publisher (camera) and receives image frames (JPEG bytes or multipart)
+- Runs inference using Ultralytics YOLO (loads .engine on Jetson if present)
+- Draws detection boxes and displays annotated video
+- Optional: republishes annotated frames to a ZMQ PUB socket
+
+Usage examples:
+  python src/zmq_inference.py --zmq tcp://x06da-pserv-01:9089 --topic '' --show
+#  python src/zmq_inference.py --zmq tcp://x06da-pserv-01:9089 --publish tcp://0.0.0.0:9090
+"""
+
+import argparse
+import time
+import json
+import os
+import sys
+import platform
+import hashlib
+from typing import Any
+
+import zmq
+import torch
+import cv2
+import numpy as np
+from threading import Thread, Lock
+from collections import deque
+
+from server.focus_epics import FocusPvWriter, compute_focus_score_bgr
+from server.runtime_config import collect_cli_overrides, load_runtime_config, save_runtime_config
+from server.model_utils import load_model, list_available_models, project_root
+from server.image_utils import decode_image, draw_boxes
+from server.stream_utils import start_http_server, submit_stream_frame
+from server.target_point import compute_target_point
+
+# Optional env-backed default for control API token.
+DEFAULT_CONTROL_TOKEN = (
+    os.getenv("AAREDB_ZMQ_CONTROL_TOKEN")
+    or os.getenv("AARELC_ZMQ_CONTROL_TOKEN")
+    or os.getenv("ZMQ_CONTROL_TOKEN")
+    or ""
+)
+
+# detect jetson
+is_jetson = 'tegra' in platform.uname().machine.lower() or (os.path.exists('/proc/device-tree/model') and 'NVIDIA' in open('/proc/device-tree/model').read())
+cuda_available = torch.cuda.is_available()
+cuda_device_count = torch.cuda.device_count() if cuda_available else 0
+
+def _parse_bool(value):
+    """Parse CLI bool values like true/false/1/0/yes/no."""
+    if isinstance(value, bool):
+        return value
+    lowered = str(value).strip().lower()
+    if lowered in {"1", "true", "t", "yes", "y", "on"}:
+        return True
+    if lowered in {"0", "false", "f", "no", "n", "off"}:
+        return False
+    raise argparse.ArgumentTypeError(f"Invalid boolean value: {value}")
+
+
+def _normalize_device_string(value: Any) -> str:
+    raw = str(value).strip().lower()
+    if not raw:
+        raise ValueError("device must be 'cpu' or a non-negative CUDA index")
+    if raw == 'cpu':
+        return 'cpu'
+    if raw.startswith('cuda:'):
+        raw = raw.split(':', 1)[1].strip()
+    if raw.isdigit():
+        return str(int(raw))
+    raise ValueError("device must be 'cpu' or a non-negative CUDA index (e.g. 0,1,2)")
+
+
+def _resolve_device(value: Any) -> str:
+    dev = _normalize_device_string(value)
+    if dev == 'cpu':
+        return dev
+    if not cuda_available or cuda_device_count <= 0:
+        raise ValueError("CUDA device requested but CUDA is not available")
+    idx = int(dev)
+    if idx < 0 or idx >= cuda_device_count:
+        raise ValueError(
+            f"CUDA device index {idx} out of range (available: 0..{cuda_device_count - 1})"
+        )
+    return dev
+
+
+def _resolve_sharding(device: str, shard_count: int, shard_index: int) -> tuple[int, int]:
+    """Resolve shard config; shard_count=0 enables auto mode for multi-GPU workers."""
+    sc = int(shard_count)
+    si = int(shard_index)
+
+    if sc < 0:
+        raise ValueError("shard_count must be >= 0")
+
+    if sc == 0:
+        if device != 'cpu' and cuda_available and cuda_device_count > 1:
+            dev_idx = int(device)
+            return cuda_device_count, dev_idx % cuda_device_count
+        return 1, 0
+
+    if sc == 1:
+        return 1, 0
+
+    if si < 0 or si >= sc:
+        raise ValueError(f"shard_index must be in [0, {sc - 1}] when shard_count={sc}")
+    return sc, si
+
+
+def _tick_fps_window(window: deque[float], now_ts: float | None = None) -> float:
+    now = time.time() if now_ts is None else float(now_ts)
+    window.append(now)
+    while window and (now - window[0]) > 1.0:
+        window.popleft()
+    return float(len(window))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--zmq', default='tcp://x06da-pserv-01:9089', help='ZMQ source address')
+    parser.add_argument('--topic', default='', help='ZMQ topic to subscribe (empty = all)')
+    parser.add_argument('--http-port', type=int, default=0, help='Optional HTTP MJPEG port to serve annotated stream (0=off)')
+    parser.add_argument('--encode-stream', action='store_true', help='Encode frames to MJPEG for HTTP streaming (disabled by default)')
+    parser.add_argument('--pt', default='../models/best.pt', help='Path to .pt model')
+    parser.add_argument('--engine', default='models/best.engine', help='Path to TensorRT engine')
+    parser.add_argument('--task', choices=['auto', 'detect', 'segment'], default='auto',
+                        help='Model task. auto infers from filename (contains "seg" => segment).')
+    parser.add_argument('--conf', type=float, default=0.25, help='Confidence threshold')
+    parser.add_argument('--show', action='store_true', help='Show annotated video with OpenCV')
+    parser.add_argument('--display-fps', action='store_true', help='Overlay FPS')
+    parser.add_argument('--bayer-flip', action='store_true', help='Flip image vertically on server before inference/publish (no GUI-side correction needed)')
+    parser.add_argument('--skip', type=int, default=0, help='Process every Nth frame (0=process all)')
+    parser.add_argument('--max-fps', type=float, default=0.0, help='Max processing FPS (0=unlimited)')
+    parser.add_argument('--jpg-quality', type=int, default=80, help='JPEG quality for streamed frames (1-100)')
+    parser.add_argument('--scale', type=float, default=1.0, help='Scale factor for streamed frames (0.0-1.0)')
+    parser.add_argument('--infer-scale', type=float, default=1.0, help='Scale factor for inference input (0.0-1.0). Use <1.0 to run model on smaller images for speed')
+    parser.add_argument(
+        '--device',
+        default=('0' if (cuda_available and cuda_device_count > 0) else 'cpu'),
+        help="Inference device: 'cpu' or CUDA index (e.g. 0,1,2)."
+    )
+    parser.add_argument(
+        '--shard-count',
+        type=int,
+        default=1,
+        help='Frame sharding across workers (1=disabled, 0=auto from GPU count).'
+    )
+    parser.add_argument(
+        '--shard-index',
+        type=int,
+        default=0,
+        help='Worker shard index in [0, shard_count-1]. Ignored when shard_count=1.'
+    )
+    parser.add_argument('--publish', default='', help='Optional ZeroMQ PUB address to publish JSON inference (e.g. tcp://0.0.0.0:9091)')
+    parser.add_argument(
+        '--publish-enabled',
+        type=_parse_bool,
+        default=True,
+        help='Enable publish output (true/false). Set false on a secondary server to avoid colliding with main publisher.'
+    )
+    parser.add_argument(
+        '--compute-target-point',
+        type=_parse_bool,
+        default=False,
+        help='Compute and publish target_point in metadata (true/false).'
+    )
+    parser.add_argument('--publish-format', choices=['bgr', 'bayer'], default='bgr', help='Publish image payload format. bayer is smaller/faster when source is Bayer')
+    parser.add_argument('--publish-topic', default='', help='Topic prefix for published inference messages (empty = no topic)')
+    parser.add_argument(
+        '--focus-enabled',
+        type=_parse_bool,
+        default=False,
+        help='Compute focus score on server (true/false).'
+    )
+    parser.add_argument(
+        '--focus-epics-enabled',
+        type=_parse_bool,
+        default=False,
+        help='Write focus score to EPICS PV (true/false).'
+    )
+    parser.add_argument(
+        '--focus-pv',
+        default='X10SA-ES-MS:cam1:FocusScore',
+        help='EPICS PV name for focus score writeback.'
+    )
+    parser.add_argument(
+        '--focus-every',
+        type=int,
+        default=3,
+        help='Compute focus every N frames (>=1). Higher is cheaper.'
+    )
+    parser.add_argument(
+        '--focus-scale',
+        type=float,
+        default=0.5,
+        help='Downscale factor for focus computation in (0,1]. Lower is faster.'
+    )
+    parser.add_argument(
+        '--focus-pv-min-period-ms',
+        type=float,
+        default=100.0,
+        help='Minimum EPICS write period in ms.'
+    )
+    parser.add_argument('--tracker', choices=['none', 'bytetrack', 'botsort'], default='none',
+                        help='Optional temporal tracker for smoother IDs/output.')
+    parser.add_argument('--control-host', default='127.0.0.1', help='Control API host for runtime config updates')
+    parser.add_argument('--control-port', type=int, default=0, help='Control API port (0 disables API)')
+    parser.add_argument(
+        '--config',
+        default=str(project_root() / "config" / "zmq_inference.json"),
+        help='Path to JSON runtime config file.'
+    )
+    parser.add_argument(
+        '--config-autosave',
+        type=_parse_bool,
+        default=True,
+        help='Persist runtime config to --config on startup and updates (true/false).'
+    )
+    parser.add_argument(
+        '--control-token',
+        default=DEFAULT_CONTROL_TOKEN,
+        help='Optional API token for config writes (X-API-Key header). '
+             'Defaults from AAREDB_ZMQ_CONTROL_TOKEN/AARELC_ZMQ_CONTROL_TOKEN/ZMQ_CONTROL_TOKEN env vars.'
+    )
+    parser.add_argument('--verbose', action='store_true', help='Print debug info about frames and inference')
+    parser_defaults = {
+        'conf': float(parser.get_default('conf')),
+        'infer_scale': float(parser.get_default('infer_scale')),
+        'max_fps': float(parser.get_default('max_fps')),
+        'focus_scale': float(parser.get_default('focus_scale')),
+        'focus_pv_min_period_ms': float(parser.get_default('focus_pv_min_period_ms')),
+        'skip': int(parser.get_default('skip')),
+        'focus_every': int(parser.get_default('focus_every')),
+        'shard_count': int(parser.get_default('shard_count')),
+        'shard_index': int(parser.get_default('shard_index')),
+        'tracker': str(parser.get_default('tracker')),
+        'task': str(parser.get_default('task')),
+    }
+    args = parser.parse_args()
+
+    cli_overrides = collect_cli_overrides(sys.argv)
+    file_cfg, file_cfg_error = load_runtime_config(args.config)
+    if file_cfg_error:
+        print(f"[Config] {file_cfg_error}", flush=True)
+    file_cfg = file_cfg if isinstance(file_cfg, dict) else {}
+
+    def _apply_file_arg(field: str, caster, arg_name: str | None = None) -> None:
+        if field not in file_cfg:
+            return
+        normalized = field.replace('-', '_')
+        if normalized in cli_overrides:
+            return
+        target = arg_name or normalized
+        try:
+            setattr(args, target, caster(file_cfg[field]))
+        except Exception as e:
+            print(f"[Config] ignoring invalid value for '{field}' from config file: {e}", flush=True)
+
+    # Apply startup/runtime values from config file unless explicitly overridden by CLI.
+    _apply_file_arg('zmq', str)
+    _apply_file_arg('topic', str)
+    _apply_file_arg('http_port', int)
+    _apply_file_arg('encode_stream', _parse_bool)
+    _apply_file_arg('pt', str)
+    _apply_file_arg('engine', str)
+    _apply_file_arg('task', str)
+    _apply_file_arg('conf', float)
+    _apply_file_arg('show', _parse_bool)
+    _apply_file_arg('display_fps', _parse_bool)
+    _apply_file_arg('bayer_flip', _parse_bool)
+    _apply_file_arg('skip', int)
+    _apply_file_arg('max_fps', float)
+    _apply_file_arg('jpg_quality', int)
+    _apply_file_arg('scale', float)
+    _apply_file_arg('infer_scale', float)
+    _apply_file_arg('device', str)
+    _apply_file_arg('shard_count', int)
+    _apply_file_arg('shard_index', int)
+    _apply_file_arg('publish', str)
+    _apply_file_arg('publish_enabled', _parse_bool)
+    _apply_file_arg('compute_target_point', _parse_bool)
+    _apply_file_arg('publish_format', str)
+    _apply_file_arg('publish_topic', str)
+    _apply_file_arg('focus_enabled', _parse_bool)
+    _apply_file_arg('focus_epics_enabled', _parse_bool)
+    _apply_file_arg('focus_pv', str)
+    _apply_file_arg('focus_every', int)
+    _apply_file_arg('focus_scale', float)
+    _apply_file_arg('focus_pv_min_period_ms', float)
+    _apply_file_arg('tracker', str)
+    _apply_file_arg('control_host', str)
+    _apply_file_arg('control_port', int)
+    _apply_file_arg('control_token', str)
+    _apply_file_arg('config_autosave', _parse_bool)
+    _apply_file_arg('verbose', _parse_bool)
+
+    try:
+        args.device = _resolve_device(args.device)
+    except ValueError as e:
+        raise SystemExit(f"Invalid device setting: {e}") from e
+    try:
+        args.shard_count, args.shard_index = _resolve_sharding(
+            device=args.device,
+            shard_count=args.shard_count,
+            shard_index=args.shard_index,
+        )
+    except ValueError as e:
+        raise SystemExit(f"Invalid sharding setting: {e}") from e
+
+    # ZMQ setup (source/publish sockets are initialized after sharding config is resolved)
+    ctx = zmq.Context.instance()
+    ctx.set(zmq.IO_THREADS, 4) # Ensure this is at least 4
+
+    sub = None
+    pub = None
+
+    http_server = None
+    if args.http_port and args.http_port > 0:
+        http_server = start_http_server(args.http_port, args.jpg_quality, args.scale)
+        print(f"HTTP MJPEG streamer running at http://0.0.0.0:{args.http_port}/stream")
+
+    # no publish option: we only subscribe and display locally
+
+    runtime_lock = Lock()
+    runtime_cfg = {
+        'conf': float(args.conf),
+        'infer_scale': float(args.infer_scale),
+        'skip': int(args.skip),
+        'max_fps': float(args.max_fps),
+        'device': str(args.device),
+        'shard_count': int(args.shard_count),
+        'shard_index': int(args.shard_index),
+        'bayer_flip': bool(args.bayer_flip),
+        'publish_enabled': bool(args.publish_enabled),
+        'compute_target_point': bool(args.compute_target_point),
+        'focus_enabled': bool(args.focus_enabled),
+        'focus_epics_enabled': bool(args.focus_epics_enabled),
+        'focus_pv': str(args.focus_pv),
+        'focus_every': max(1, int(args.focus_every)),
+        'focus_scale': float(args.focus_scale),
+        'focus_pv_min_period_ms': float(args.focus_pv_min_period_ms),
+        'tracker': str(args.tracker),
+        'pt': str(args.pt),
+        'engine': str(args.engine),
+        'task': str(args.task),
+    }
+
+    # Normalize and validate.
+    runtime_cfg['skip'] = max(0, int(runtime_cfg['skip']))
+    runtime_cfg['focus_every'] = max(1, int(runtime_cfg['focus_every']))
+    if not (0.0 <= float(runtime_cfg['conf']) <= 1.0):
+        runtime_cfg['conf'] = parser_defaults['conf']
+    if not (0.0 < float(runtime_cfg['infer_scale']) <= 1.0):
+        runtime_cfg['infer_scale'] = parser_defaults['infer_scale']
+    if not (0.0 < float(runtime_cfg['focus_scale']) <= 1.0):
+        runtime_cfg['focus_scale'] = parser_defaults['focus_scale']
+    if float(runtime_cfg['max_fps']) < 0.0:
+        runtime_cfg['max_fps'] = parser_defaults['max_fps']
+    if float(runtime_cfg['focus_pv_min_period_ms']) < 0.0:
+        runtime_cfg['focus_pv_min_period_ms'] = parser_defaults['focus_pv_min_period_ms']
+    if runtime_cfg['tracker'] not in ('none', 'bytetrack', 'botsort'):
+        runtime_cfg['tracker'] = parser_defaults['tracker']
+    if runtime_cfg['task'] not in ('auto', 'detect', 'segment'):
+        runtime_cfg['task'] = parser_defaults['task']
+    try:
+        runtime_cfg['shard_count'], runtime_cfg['shard_index'] = _resolve_sharding(
+            device=str(runtime_cfg.get('device', 'cpu')),
+            shard_count=int(runtime_cfg.get('shard_count', parser_defaults['shard_count'])),
+            shard_index=int(runtime_cfg.get('shard_index', parser_defaults['shard_index'])),
+        )
+    except Exception as e:
+        print(f"[Config] invalid sharding config, using defaults: {e}", flush=True)
+        runtime_cfg['shard_count'], runtime_cfg['shard_index'] = _resolve_sharding(
+            device=str(runtime_cfg.get('device', 'cpu')),
+            shard_count=parser_defaults['shard_count'],
+            shard_index=parser_defaults['shard_index'],
+        )
+    if runtime_cfg['focus_epics_enabled'] and not runtime_cfg['focus_enabled']:
+        runtime_cfg['focus_enabled'] = True
+        print("focus_epics_enabled=true -> forcing focus_enabled=true", flush=True)
+
+    ingress_pull = None
+    dist_push_by_shard = {}
+    use_distributor_ingest = int(runtime_cfg.get('shard_count', 1)) > 1
+    if use_distributor_ingest:
+        ingest_id = hashlib.sha1(f"{args.zmq}|{args.topic}".encode('utf-8')).hexdigest()[:16]
+        shard_count = int(runtime_cfg['shard_count'])
+        shard_index = int(runtime_cfg['shard_index'])
+        endpoint_for = lambda idx: f"ipc:///tmp/zmq_inference_ingest_{ingest_id}_{idx}.sock"
+
+        if shard_index == 0:
+            sub = ctx.socket(zmq.SUB)
+            # Tuned for high-bandwidth 10G
+            sub.setsockopt(zmq.RCVBUF, 128 * 1024 * 1024)
+            sub.setsockopt(zmq.RCVHWM, 1000)
+            sub.setsockopt(zmq.TCP_KEEPALIVE, 1)
+            sub.setsockopt(zmq.IMMEDIATE, 1)
+            sub.connect(args.zmq)
+            sub.setsockopt_string(zmq.SUBSCRIBE, args.topic)
+            print(f"Connected to ZMQ {args.zmq} topic='{args.topic}' as distributor", flush=True)
+
+            for idx in range(1, shard_count):
+                s = ctx.socket(zmq.PUSH)
+                s.bind(endpoint_for(idx))
+                dist_push_by_shard[idx] = s
+            print(
+                f"Ingest fan-out enabled: shard_count={shard_count} "
+                f"(internal IPC id={ingest_id})",
+                flush=True,
+            )
+        else:
+            ingress_pull = ctx.socket(zmq.PULL)
+            ingress_pull.connect(endpoint_for(shard_index))
+            print(
+                f"Connected to internal ingest distributor shard={shard_index}/{shard_count - 1} "
+                f"(ipc id={ingest_id})",
+                flush=True,
+            )
+    else:
+        sub = ctx.socket(zmq.SUB)
+        # Tuned for high-bandwidth 10G
+        sub.setsockopt(zmq.RCVBUF, 128 * 1024 * 1024)
+        sub.setsockopt(zmq.RCVHWM, 1000)
+        sub.setsockopt(zmq.TCP_KEEPALIVE, 1)
+        sub.setsockopt(zmq.IMMEDIATE, 1)
+        sub.connect(args.zmq)
+        sub.setsockopt_string(zmq.SUBSCRIBE, args.topic)
+        print(f"Connected to ZMQ {args.zmq} topic='{args.topic}'")
+
+    base_persist_cfg = dict(file_cfg)
+
+    def _build_persisted_config() -> dict[str, Any]:
+        with runtime_lock:
+            cfg = dict(runtime_cfg)
+        out = dict(base_persist_cfg)
+        out.update({
+            'zmq': str(args.zmq),
+            'topic': str(args.topic),
+            'http_port': int(args.http_port),
+            'encode_stream': bool(args.encode_stream),
+            'show': bool(args.show),
+            'display_fps': bool(args.display_fps),
+            'jpg_quality': int(args.jpg_quality),
+            'scale': float(args.scale),
+            'publish': str(args.publish),
+            'publish_format': str(args.publish_format),
+            'publish_topic': str(args.publish_topic),
+            'control_host': str(args.control_host),
+            'control_port': int(args.control_port),
+            'config_autosave': bool(args.config_autosave),
+            'verbose': bool(args.verbose),
+        })
+        # Avoid forcing token persistence unless explicitly configured.
+        if args.control_token:
+            out['control_token'] = str(args.control_token)
+        out.update(cfg)
+        return out
+
+    fanin_pull = None
+    fanin_push = None
+    is_multi_worker_publish = bool(args.publish) and int(runtime_cfg.get('shard_count', 1)) > 1
+    is_publish_aggregator = is_multi_worker_publish and int(runtime_cfg.get('shard_index', 0)) == 0
+
+    if args.publish and runtime_cfg['publish_enabled']:
+        if is_multi_worker_publish:
+            fanin_id = hashlib.sha1(args.publish.encode('utf-8')).hexdigest()[:16]
+            fanin_endpoint = f"ipc:///tmp/zmq_inference_fanin_{fanin_id}.sock"
+            if is_publish_aggregator:
+                pub = ctx.socket(zmq.PUB)
+                pub.bind(args.publish)
+                fanin_pull = ctx.socket(zmq.PULL)
+                fanin_pull.bind(fanin_endpoint)
+                print(
+                    f"Publishing aggregated inference JSON on {args.publish} topic='{args.publish_topic}' "
+                    f"(fan-in: {fanin_endpoint})",
+                    flush=True,
+                )
+            else:
+                fanin_push = ctx.socket(zmq.PUSH)
+                fanin_push.connect(fanin_endpoint)
+                print(
+                    f"Worker shard {runtime_cfg.get('shard_index')}/{runtime_cfg.get('shard_count') - 1} "
+                    f"pushing publish payloads to aggregator via {fanin_endpoint}",
+                    flush=True,
+                )
+        else:
+            pub = ctx.socket(zmq.PUB)
+            try:
+                pub.bind(args.publish)
+                print(f"Publishing inference JSON on {args.publish} topic='{args.publish_topic}'")
+            except Exception:
+                pub.connect(args.publish)
+                print(f"Connected PUB to {args.publish} topic='{args.publish_topic}'")
+    elif args.publish and not runtime_cfg['publish_enabled']:
+        print(
+            f"Publish socket disabled (publish_enabled=false), "
+            f"not binding/connecting to {args.publish}",
+            flush=True
+        )
+
+    focus_writer = FocusPvWriter(
+        pv_name=runtime_cfg['focus_pv'],
+        enabled=bool(runtime_cfg['focus_epics_enabled']),
+        min_period_ms=float(runtime_cfg['focus_pv_min_period_ms']),
+    )
+    if args.config_autosave:
+        save_err = save_runtime_config(args.config, _build_persisted_config())
+        if save_err:
+            print(f"[Config] {save_err}", flush=True)
+        else:
+            print(f"[Config] loaded+saved runtime config at {args.config}", flush=True)
+    print(
+        "[Focus] startup "
+        f"focus_enabled={runtime_cfg['focus_enabled']} "
+        f"focus_epics_enabled={runtime_cfg['focus_epics_enabled']} "
+        f"pv='{runtime_cfg['focus_pv']}' "
+        f"focus_every={runtime_cfg['focus_every']} "
+        f"focus_scale={runtime_cfg['focus_scale']} "
+        f"min_period_ms={runtime_cfg['focus_pv_min_period_ms']}",
+        flush=True
+    )
+
+    model_lock = Lock()
+    model = load_model(
+        pt_path=runtime_cfg['pt'],
+        engine_path=runtime_cfg['engine'],
+        task=runtime_cfg['task']
+    )
+
+    def _snapshot_cfg() -> dict[str, Any]:
+        with runtime_lock:
+            return dict(runtime_cfg)
+
+    def _snapshot_merged_rates() -> dict[str, Any]:
+        now = time.time()
+        while merged_source_window and (now - merged_source_window[0]) > 1.0:
+            merged_source_window.popleft()
+        while merged_publish_window and (now - merged_publish_window[0]) > 1.0:
+            merged_publish_window.popleft()
+        while merged_prediction_window and (now - merged_prediction_window[0]) > 1.0:
+            merged_prediction_window.popleft()
+        return {
+            "source_fps_merged": float(len(merged_source_window)),
+            "publish_fps_merged": float(len(merged_publish_window)),
+            "prediction_fps_merged": float(len(merged_prediction_window)),
+            "source_total_merged": int(merged_source_total),
+            "publish_total_merged": int(merged_publish_total),
+            "prediction_total_merged": int(merged_prediction_total),
+        }
+
+    def _reload_model_from_cfg() -> None:
+        nonlocal model
+        cfg = _snapshot_cfg()
+        new_model = load_model(
+            pt_path=cfg['pt'],
+            engine_path=cfg['engine'],
+            task=cfg['task'],
+        )
+        with model_lock:
+            model = new_model
+        print(
+            f"Runtime model reloaded: task={cfg['task']} "
+            f"engine={cfg['engine']} pt={cfg['pt']}",
+            flush=True
+        )
+
+    def _run_control_api():
+        try:
+            from fastapi import FastAPI, Header, HTTPException
+            import uvicorn
+        except Exception as e:
+            print(f"Control API unavailable (missing dependency): {e}", flush=True)
+            return
+        app = FastAPI(title="ZMQ Inference Control API", version="1.0")
+
+        def _authorize(api_key: str | None):
+            if args.control_token and (api_key != args.control_token):
+                raise HTTPException(status_code=401, detail="Invalid API key")
+
+        @app.get("/health")
+        def health():
+            return {"ok": True}
+
+        @app.get("/config")
+        def get_config():
+            with model_lock:
+                model_id = str(getattr(model, 'model_name', 'loaded'))
+            return {
+                "config": _snapshot_cfg(),
+                "config_path": args.config,
+                "config_autosave": bool(args.config_autosave),
+                "model": model_id,
+                "publish_socket_ready": bool((pub is not None) or (fanin_push is not None)),
+                "focus_writer": focus_writer.status(),
+                "merged_rates": _snapshot_merged_rates(),
+                "capabilities": {
+                    "cuda_available": bool(cuda_available),
+                    "jetson": bool(is_jetson),
+                    "cuda_device_count": int(cuda_device_count),
+                },
+            }
+
+        @app.get("/models")
+        def get_models():
+            root = project_root()
+            models_dir = root / "models"
+            models = list_available_models()
+            return {
+                "models_dir": str(models_dir),
+                "models": models,
+                "count": len(models),
+                "capabilities": {
+                    "cuda_available": bool(cuda_available),
+                    "jetson": bool(is_jetson),
+                    "cuda_device_count": int(cuda_device_count),
+                },
+            }
+
+        @app.post("/config")
+        def set_config(update: dict[str, Any], x_api_key: str | None = Header(default=None, alias="X-API-Key")):
+            _authorize(x_api_key)
+            if not isinstance(update, dict):
+                raise HTTPException(status_code=400, detail="JSON object required")
+
+            allowed = {
+                'conf', 'infer_scale', 'skip', 'max_fps', 'device', 'shard_count', 'shard_index',
+                'bayer_flip', 'publish_enabled',
+                'compute_target_point',
+                'focus_enabled', 'focus_epics_enabled', 'focus_pv', 'focus_every',
+                'focus_scale', 'focus_pv_min_period_ms',
+                'tracker', 'pt', 'engine', 'task'
+            }
+            unknown = sorted(k for k in update.keys() if k not in allowed)
+            if unknown:
+                raise HTTPException(status_code=400, detail=f"Unknown fields: {unknown}")
+
+            reload_needed = False
+            changed_fields = []
+
+            with runtime_lock:
+                if 'conf' in update:
+                    new_conf = float(update['conf'])
+                    if not (0.0 <= new_conf <= 1.0):
+                        raise HTTPException(status_code=400, detail="conf must be in [0,1]")
+                    if new_conf != runtime_cfg['conf']:
+                        changed_fields.append(("conf", runtime_cfg['conf'], new_conf))
+                        runtime_cfg['conf'] = new_conf
+                if 'infer_scale' in update:
+                    new_infer_scale = float(update['infer_scale'])
+                    if not (0.0 < new_infer_scale <= 1.0):
+                        raise HTTPException(status_code=400, detail="infer_scale must be in (0,1]")
+                    if new_infer_scale != runtime_cfg['infer_scale']:
+                        changed_fields.append(("infer_scale", runtime_cfg['infer_scale'], new_infer_scale))
+                        runtime_cfg['infer_scale'] = new_infer_scale
+                if 'skip' in update:
+                    new_skip = int(update['skip'])
+                    if new_skip < 0:
+                        raise HTTPException(status_code=400, detail="skip must be >= 0")
+                    if new_skip != runtime_cfg['skip']:
+                        changed_fields.append(("skip", runtime_cfg['skip'], new_skip))
+                        runtime_cfg['skip'] = new_skip
+                if 'max_fps' in update:
+                    new_max_fps = float(update['max_fps'])
+                    if new_max_fps < 0:
+                        raise HTTPException(status_code=400, detail="max_fps must be >= 0")
+                    if new_max_fps != runtime_cfg['max_fps']:
+                        changed_fields.append(("max_fps", runtime_cfg['max_fps'], new_max_fps))
+                        runtime_cfg['max_fps'] = new_max_fps
+                if 'bayer_flip' in update:
+                    new_bayer_flip = bool(update['bayer_flip'])
+                    if new_bayer_flip != runtime_cfg['bayer_flip']:
+                        changed_fields.append(("bayer_flip", runtime_cfg['bayer_flip'], new_bayer_flip))
+                        runtime_cfg['bayer_flip'] = new_bayer_flip
+                if 'device' in update:
+                    try:
+                        new_device = _resolve_device(update['device'])
+                    except ValueError as e:
+                        raise HTTPException(status_code=400, detail=str(e))
+                    if new_device != runtime_cfg['device']:
+                        changed_fields.append(("device", runtime_cfg['device'], new_device))
+                        runtime_cfg['device'] = new_device
+                if 'shard_count' in update:
+                    requested_shard_count = int(update['shard_count'])
+                    if requested_shard_count != int(runtime_cfg['shard_count']):
+                        raise HTTPException(
+                            status_code=400,
+                            detail="shard_count change requires restart (ingest topology is fixed at startup)",
+                        )
+                if 'shard_index' in update:
+                    requested_shard_index = int(update['shard_index'])
+                    if requested_shard_index != int(runtime_cfg['shard_index']):
+                        raise HTTPException(
+                            status_code=400,
+                            detail="shard_index change requires restart (ingest topology is fixed at startup)",
+                        )
+                if 'publish_enabled' in update:
+                    new_publish_enabled = bool(update['publish_enabled'])
+                    if new_publish_enabled and (pub is None) and (fanin_push is None) and args.publish:
+                        raise HTTPException(
+                            status_code=400,
+                            detail=(
+                                "publish_enabled cannot be set to true at runtime because the "
+                                "publish socket was not created at startup. Restart with "
+                                "--publish-enabled true."
+                            ),
+                        )
+                    if new_publish_enabled != runtime_cfg['publish_enabled']:
+                        changed_fields.append(
+                            ("publish_enabled", runtime_cfg['publish_enabled'], new_publish_enabled)
+                        )
+                        runtime_cfg['publish_enabled'] = new_publish_enabled
+                if 'compute_target_point' in update:
+                    new_compute_target_point = bool(update['compute_target_point'])
+                    if new_compute_target_point != runtime_cfg['compute_target_point']:
+                        changed_fields.append(
+                            ("compute_target_point", runtime_cfg['compute_target_point'], new_compute_target_point)
+                        )
+                        runtime_cfg['compute_target_point'] = new_compute_target_point
+                if 'focus_enabled' in update:
+                    new_focus_enabled = bool(update['focus_enabled'])
+                    if new_focus_enabled != runtime_cfg['focus_enabled']:
+                        changed_fields.append(("focus_enabled", runtime_cfg['focus_enabled'], new_focus_enabled))
+                        runtime_cfg['focus_enabled'] = new_focus_enabled
+                if 'focus_epics_enabled' in update:
+                    new_focus_epics_enabled = bool(update['focus_epics_enabled'])
+                    if new_focus_epics_enabled and not bool(runtime_cfg.get('focus_enabled', False)):
+                        runtime_cfg['focus_enabled'] = True
+                        changed_fields.append(("focus_enabled", False, True))
+                    if new_focus_epics_enabled and not str(runtime_cfg.get('focus_pv', '')).strip():
+                        raise HTTPException(status_code=400, detail="focus_pv must be set before enabling EPICS writes")
+                    if new_focus_epics_enabled != runtime_cfg['focus_epics_enabled']:
+                        changed_fields.append(
+                            ("focus_epics_enabled", runtime_cfg['focus_epics_enabled'], new_focus_epics_enabled)
+                        )
+                        runtime_cfg['focus_epics_enabled'] = new_focus_epics_enabled
+                if 'focus_pv' in update:
+                    new_focus_pv = str(update['focus_pv']).strip()
+                    if not new_focus_pv:
+                        raise HTTPException(status_code=400, detail="focus_pv must be a non-empty string")
+                    if new_focus_pv != runtime_cfg['focus_pv']:
+                        changed_fields.append(("focus_pv", runtime_cfg['focus_pv'], new_focus_pv))
+                        runtime_cfg['focus_pv'] = new_focus_pv
+                if 'focus_every' in update:
+                    new_focus_every = int(update['focus_every'])
+                    if new_focus_every < 1:
+                        raise HTTPException(status_code=400, detail="focus_every must be >= 1")
+                    if new_focus_every != runtime_cfg['focus_every']:
+                        changed_fields.append(("focus_every", runtime_cfg['focus_every'], new_focus_every))
+                        runtime_cfg['focus_every'] = new_focus_every
+                if 'focus_scale' in update:
+                    new_focus_scale = float(update['focus_scale'])
+                    if not (0.0 < new_focus_scale <= 1.0):
+                        raise HTTPException(status_code=400, detail="focus_scale must be in (0,1]")
+                    if new_focus_scale != runtime_cfg['focus_scale']:
+                        changed_fields.append(("focus_scale", runtime_cfg['focus_scale'], new_focus_scale))
+                        runtime_cfg['focus_scale'] = new_focus_scale
+                if 'focus_pv_min_period_ms' in update:
+                    new_focus_pv_min_period_ms = float(update['focus_pv_min_period_ms'])
+                    if new_focus_pv_min_period_ms < 0.0:
+                        raise HTTPException(status_code=400, detail="focus_pv_min_period_ms must be >= 0")
+                    if new_focus_pv_min_period_ms != runtime_cfg['focus_pv_min_period_ms']:
+                        changed_fields.append(
+                            (
+                                "focus_pv_min_period_ms",
+                                runtime_cfg['focus_pv_min_period_ms'],
+                                new_focus_pv_min_period_ms,
+                            )
+                        )
+                        runtime_cfg['focus_pv_min_period_ms'] = new_focus_pv_min_period_ms
+                if 'tracker' in update:
+                    tracker = str(update['tracker']).strip().lower()
+                    if tracker not in ('none', 'bytetrack', 'botsort'):
+                        raise HTTPException(status_code=400, detail="tracker must be none/bytetrack/botsort")
+                    if tracker != runtime_cfg['tracker']:
+                        changed_fields.append(("tracker", runtime_cfg['tracker'], tracker))
+                        runtime_cfg['tracker'] = tracker
+                if 'pt' in update:
+                    pt_val = str(update['pt']).strip()
+                    if pt_val != runtime_cfg['pt']:
+                        changed_fields.append(("pt", runtime_cfg['pt'], pt_val))
+                        runtime_cfg['pt'] = pt_val
+                        reload_needed = True
+                if 'engine' in update:
+                    engine_val = str(update['engine']).strip()
+                    if engine_val != runtime_cfg['engine']:
+                        changed_fields.append(("engine", runtime_cfg['engine'], engine_val))
+                        runtime_cfg['engine'] = engine_val
+                        reload_needed = True
+                if 'task' in update:
+                    task = str(update['task']).strip()
+                    if task not in ("auto", "detect", "segment"):
+                        raise HTTPException(status_code=400, detail="task must be auto/detect/segment")
+                    if task != runtime_cfg['task']:
+                        changed_fields.append(("task", runtime_cfg['task'], task))
+                        runtime_cfg['task'] = task
+                        reload_needed = True
+
+            for name, old, new in changed_fields:
+                print(f"Runtime config change: {name} {old} -> {new}", flush=True)
+            if not changed_fields:
+                print("Runtime config update received: no effective changes", flush=True)
+
+            focus_writer.configure(
+                enabled=bool(runtime_cfg.get('focus_epics_enabled', False)),
+                pv_name=str(runtime_cfg.get('focus_pv', '')),
+                min_period_ms=float(runtime_cfg.get('focus_pv_min_period_ms', 100.0)),
+            )
+            config_save_error = None
+            if args.config_autosave:
+                config_save_error = save_runtime_config(args.config, _build_persisted_config())
+                if config_save_error:
+                    print(f"[Config] {config_save_error}", flush=True)
+            if changed_fields:
+                print(
+                    "[Focus] runtime "
+                    f"focus_enabled={runtime_cfg.get('focus_enabled')} "
+                    f"focus_epics_enabled={runtime_cfg.get('focus_epics_enabled')} "
+                    f"pv='{runtime_cfg.get('focus_pv')}' "
+                    f"focus_every={runtime_cfg.get('focus_every')} "
+                    f"focus_scale={runtime_cfg.get('focus_scale')} "
+                    f"min_period_ms={runtime_cfg.get('focus_pv_min_period_ms')}",
+                    flush=True,
+                )
+
+            if reload_needed:
+                try:
+                    print("Runtime config change requires model reload", flush=True)
+                    _reload_model_from_cfg()
+                except Exception as e:
+                    raise HTTPException(status_code=500, detail=f"Model reload failed: {e}") from e
+
+            return {
+                "ok": True,
+                "model_reloaded": reload_needed,
+                "config": _snapshot_cfg(),
+                "config_saved": bool(args.config_autosave and config_save_error is None),
+                "config_save_error": config_save_error,
+                "config_path": args.config,
+            }
+
+        print(
+            f"Control API started at http://{args.control_host}:{args.control_port} "
+            f"(token={'enabled' if args.control_token else 'disabled'})",
+            flush=True
+        )
+        uvicorn.run(app, host=args.control_host, port=args.control_port, log_level="warning")
+
+    if args.control_port > 0:
+        Thread(target=_run_control_api, daemon=True).start()
+
+    startup_device = str(runtime_cfg.get('device', 'cpu'))
+    if startup_device == 'cpu':
+        print("Using inference device=cpu", flush=True)
+    else:
+        print(f"CUDA available -> using configured device={startup_device}", flush=True)
+    print(
+        f"Sharding enabled: shard_count={runtime_cfg.get('shard_count', 1)} "
+        f"shard_index={runtime_cfg.get('shard_index', 0)}",
+        flush=True,
+    )
+
+    # main loop
+    last_time = time.time()
+    fps = 0.0
+    frame_count = 0
+
+    # Source FPS tracking
+    source_last_time = time.time()
+    source_frame_count = 0
+    source_fps = 0.0
+    distributor_seq = 0
+    merged_source_window = deque(maxlen=5000)
+    merged_publish_window = deque(maxlen=5000)
+    merged_prediction_window = deque(maxlen=5000)
+    merged_source_total = 0
+    merged_publish_total = 0
+    merged_prediction_total = 0
+
+    recv_count = 0
+    last_processed_time = 0.0
+    latest_dets = []
+    track_poly_cache = {}
+    track_last_seen = {}
+    infer_timestamps = deque(maxlen=240)
+    prediction_fps = 0.0
+    latest_focus_score = None
+    focus_counter = 0
+
+    try:
+        while True:
+            parts = None
+            if use_distributor_ingest:
+                if sub is not None:
+                    # Leader ingests once from pserv and routes frames round-robin to worker shards.
+                    while True:
+                        parts_candidate = sub.recv_multipart()
+                        source_frame_count += 1
+                        merged_source_total += 1
+                        _tick_fps_window(merged_source_window)
+                        target_shard = distributor_seq % max(1, int(runtime_cfg.get('shard_count', 1)))
+                        distributor_seq += 1
+                        if target_shard == 0:
+                            parts = parts_candidate
+                            break
+                        target_push = dist_push_by_shard.get(target_shard)
+                        if target_push is not None:
+                            try:
+                                target_push.send_multipart(parts_candidate, copy=False)
+                            except Exception as e:
+                                if args.verbose:
+                                    print(f"[VERBOSE] failed routing frame to shard {target_shard}: {e}")
+                elif ingress_pull is not None:
+                    parts = ingress_pull.recv_multipart()
+                    source_frame_count += 1
+                else:
+                    raise RuntimeError("Invalid ingest configuration: no source socket available")
+            else:
+                # DRAIN: keep only the latest source frame to minimize latency.
+                while True:
+                    try:
+                        latest_parts = sub.recv_multipart(zmq.NOBLOCK)
+                        parts = latest_parts
+                        source_frame_count += 1
+                        merged_source_total += 1
+                        _tick_fps_window(merged_source_window)
+                    except zmq.Again:
+                        break
+
+                if parts is None:
+                    parts = sub.recv_multipart()
+                    source_frame_count += 1
+                    merged_source_total += 1
+                    _tick_fps_window(merged_source_window)
+
+            # Calculate Source FPS every 25 frames
+            if source_frame_count >= 25:
+                now = time.time()
+                source_fps = source_frame_count / (now - source_last_time)
+                source_last_time = now
+                source_frame_count = 0
+
+            if len(parts) == 0:
+                continue
+
+            # Snapshot runtime config for this frame; updates are lock-free after this point.
+            cfg = _snapshot_cfg()
+            conf = float(cfg['conf'])
+            infer_scale_cfg = float(cfg['infer_scale'])
+            skip_cfg = int(cfg['skip'])
+            max_fps_cfg = float(cfg['max_fps'])
+            device_cfg = str(cfg.get('device', 'cpu'))
+            predict_device = 'cpu' if device_cfg == 'cpu' else int(device_cfg)
+            shard_count_cfg = max(1, int(cfg.get('shard_count', 1)))
+            shard_index_cfg = int(cfg.get('shard_index', 0))
+            bayer_flip_cfg = bool(cfg['bayer_flip'])
+            publish_enabled_cfg = bool(cfg.get('publish_enabled', True))
+            compute_target_point_cfg = bool(cfg.get('compute_target_point', False))
+            focus_enabled_cfg = bool(cfg.get('focus_enabled', False))
+            focus_epics_enabled_cfg = bool(cfg.get('focus_epics_enabled', False))
+            focus_every_cfg = max(1, int(cfg.get('focus_every', 3)))
+            focus_scale_cfg = float(cfg.get('focus_scale', 0.5))
+            tracker_cfg = str(cfg.get('tracker', 'none')).lower()
+
+            img = None
+            outgoing_image_buf = None
+            img_already_flipped = False
+            publish_is_bayer = False
+            publish_bayer_pattern = None
+            # If publisher sends [meta,json, data], try to parse header
+            if len(parts) >= 2:
+                # try interpret first part as JSON header
+                try:
+                    first = parts[0]
+                    if isinstance(first, (bytes, bytearray)):
+                        meta_text = first.decode('utf-8')
+                    else:
+                        meta_text = str(first)
+                    header = json.loads(meta_text)
+                    if args.verbose:
+                        print(f"[VERBOSE] received multipart with header keys: {list(header.keys())} data_len={len(parts[-1])}")
+                    # handle raw uint8 Bayer frame with shape
+                    if header.get('type') == 'uint8' and isinstance(header.get('shape'), (list, tuple)):
+                        shape = header.get('shape')
+                        data = parts[-1]
+                        arr = np.frombuffer(data, dtype=np.uint8)
+                        if len(shape) == 2:
+                            h, w = shape
+                            if arr.size != h * w:
+                                if args.verbose:
+                                    print(f"[VERBOSE] warning: data size {arr.size} != h*w {h*w}")
+                            try:
+                                bayer = arr.reshape((h, w))
+                                # Apply orientation correction on server so downstream clients consume as-is.
+                                if bayer_flip_cfg:
+                                    bayer = cv2.flip(bayer, 0)
+                                    # Vertical flip changes RGGB phase to GBRG for even-height sensors.
+                                    demosaic_code = cv2.COLOR_BAYER_GB2BGR
+                                    publish_bayer_pattern = 'GBRG'
+                                    img_already_flipped = True
+                                else:
+                                    demosaic_code = cv2.COLOR_BAYER_RG2BGR
+                                    publish_bayer_pattern = 'RGGB'
+                                # Convert Bayer to BGR for YOLO/OpenCV
+                                img = cv2.cvtColor(bayer, demosaic_code)
+                                if (pub is not None or fanin_push is not None) and publish_enabled_cfg and args.publish_format == 'bayer':
+                                    if not bayer.flags['C_CONTIGUOUS']:
+                                        bayer = np.ascontiguousarray(bayer)
+                                    outgoing_image_buf = memoryview(bayer)
+                                    publish_is_bayer = True
+                            except Exception as e:
+                                if args.verbose:
+                                    print(f"[VERBOSE] bayer->bgr conversion failed: {e}")
+                        elif len(shape) == 3:
+                            # e.g., [h, w, c]
+                            h, w, c = shape
+                            if arr.size != h * w * c:
+                                if args.verbose:
+                                    print(f"[VERBOSE] warning: data size {arr.size} != h*w*c {h*w*c}")
+                            try:
+                                img = arr.reshape((h, w, c)).copy()
+                                if bayer_flip_cfg:
+                                    img = cv2.flip(img, 0)
+                                    img_already_flipped = True
+                            except Exception as e:
+                                if args.verbose:
+                                    print(f"[VERBOSE] raw reshape failed: {e}")
+                except Exception:
+                    # not JSON or unexpected header; fall back
+                    if args.verbose:
+                        print("[VERBOSE] first multipart part not JSON header, falling back to raw decode")
+
+            # fallback: if we still don't have img, try to decode last part as common image (jpeg/png)
+            recv_count += 1
+
+            # Publish aggregator forwards worker payloads from fan-in channel.
+            if fanin_pull is not None and pub is not None and publish_enabled_cfg:
+                while True:
+                    try:
+                        forwarded = fanin_pull.recv_multipart(zmq.NOBLOCK)
+                    except zmq.Again:
+                        break
+                    try:
+                        merged_publish_total += 1
+                        _tick_fps_window(merged_publish_window)
+                        try:
+                            forwarded_meta = json.loads(forwarded[1].decode('utf-8'))
+                            if bool(forwarded_meta.get('predicted_this_frame', False)):
+                                merged_prediction_total += 1
+                                _tick_fps_window(merged_prediction_window)
+                        except Exception:
+                            pass
+                        pub.send_multipart(forwarded, copy=False)
+                    except Exception as e:
+                        if args.verbose:
+                            print(f"[VERBOSE] failed forwarding worker publish payload: {e}")
+            if (not use_distributor_ingest) and shard_count_cfg > 1 and (recv_count % shard_count_cfg) != shard_index_cfg:
+                # This frame belongs to another worker shard.
+                continue
+
+            # decide whether to run inference on this frame (skip + max_fps)
+            process_frame = True
+            if skip_cfg > 0:
+                # skip N frames between processed frames: process every (skip+1)th
+                if (recv_count % (skip_cfg + 1)) != 0:
+                    process_frame = False
+            if max_fps_cfg > 0.0:
+                min_interval = 1.0 / max_fps_cfg
+                if (time.time() - last_processed_time) < min_interval:
+                    process_frame = False
+
+            if img is None:
+                img_bytes = parts[-1]
+                if args.verbose:
+                    try:
+                        print(f"[VERBOSE] received bytes: {len(img_bytes)}")
+                    except Exception:
+                        print("[VERBOSE] received bytes: <unknown>")
+                img = decode_image(img_bytes)
+
+            if img is None:
+                if args.verbose:
+                    print("[VERBOSE] failed to decode image")
+                # skip if cannot decode
+                continue
+            else:
+                if args.verbose:
+                    h, w = img.shape[:2]
+                    print(f"[VERBOSE] decoded image {w}x{h}")
+            if img.ndim == 2:
+                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+            elif img.ndim == 3 and img.shape[2] == 4:
+                img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
+            # Keep server-side orientation correction authoritative for all incoming image types.
+            if bayer_flip_cfg and not img_already_flipped:
+                img = cv2.flip(img, 0)
+
+            # Unified stream payload is always raw BGR bytes for fastest client decode.
+            # For publish mode, avoid a full-frame tobytes() copy by sending a buffer view.
+            if (pub is not None or fanin_push is not None) and publish_enabled_cfg:
+                if outgoing_image_buf is None:
+                    if not img.flags['C_CONTIGUOUS']:
+                        img = np.ascontiguousarray(img)
+                    outgoing_image_buf = memoryview(img)
+
+            # Optional server-side focus score (throttled for performance).
+            if focus_enabled_cfg:
+                focus_counter += 1
+                if (focus_counter % focus_every_cfg) == 0:
+                    try:
+                        latest_focus_score = compute_focus_score_bgr(img, scale=focus_scale_cfg)
+                        if focus_epics_enabled_cfg:
+                            focus_writer.submit(latest_focus_score)
+                    except Exception as e:
+                        if args.verbose:
+                            print(f"[VERBOSE] focus computation/write failed: {e}")
+            # Decide whether we need an annotated copy (showing or streaming MJPEG)
+            need_annotate = False
+            if args.show:
+                need_annotate = True
+            if args.http_port and args.http_port > 0 and args.encode_stream:
+                need_annotate = True
+            # If we don't need to annotate, avoid copying to reduce memory churn
+            annotated = img.copy() if need_annotate else img
+            result0 = None
+            predicted_this_frame = False
+            if process_frame:
+                # run inference
+                t0 = time.time()
+                # Optionally downscale input for faster inference
+                infer_img = img
+                infer_scale = infer_scale_cfg if infer_scale_cfg > 0.0 else 1.0
+                orig_h, orig_w = img.shape[:2]
+                if infer_scale < 1.0:
+                    new_w = max(1, int(orig_w * infer_scale))
+                    new_h = max(1, int(orig_h * infer_scale))
+                    infer_img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+
+                try:
+                    with model_lock:
+                        model_ref = model
+                    if tracker_cfg == 'none':
+                        results = model_ref.predict(infer_img, conf=conf, device=predict_device, verbose=False)
+                    else:
+                        tracker_yaml = 'bytetrack.yaml' if tracker_cfg == 'bytetrack' else 'botsort.yaml'
+                        results = model_ref.track(
+                            infer_img,
+                            conf=conf,
+                            device=predict_device,
+                            verbose=False,
+                            persist=True,
+                            tracker=tracker_yaml
+                        )
+                    result0 = results[0] if isinstance(results, (list, tuple)) else results
+                    predicted_this_frame = True
+                except Exception:
+                    # fallback: call model(infer_img)
+                    with model_lock:
+                        model_ref = model
+                    res_tuple = model_ref(infer_img)
+                    result0 = res_tuple[0]
+                    predicted_this_frame = True
+                t_inf = time.time() - t0
+                last_processed_time = time.time()
+                infer_timestamps.append(last_processed_time)
+                while infer_timestamps and (last_processed_time - infer_timestamps[0]) > 1.0:
+                    infer_timestamps.popleft()
+                prediction_fps = float(len(infer_timestamps))
+                if args.verbose:
+                    try:
+                        nboxes = len(result0.boxes) if getattr(result0, 'boxes', None) is not None else 0
+                    except Exception:
+                        # try numpy fallback
+                        try:
+                            nboxes = result0.boxes.numpy().shape[0]
+                        except Exception:
+                            nboxes = -1
+                    print(f"[VERBOSE] inference time: {t_inf:.3f}s, boxes: {nboxes}")
+
+                # Draw on the infer-sized image if we ran inference on a smaller one,
+                # then scale annotated back to original size for display/streaming.
+                try:
+                    if infer_scale < 1.0 and need_annotate:
+                        # draw on the smaller infer_img copy
+                        small_annot = infer_img.copy()
+                        small_annot = draw_boxes(small_annot, result0, conf_threshold=conf)
+                        # upscale annotated back to original size
+                        annotated = cv2.resize(small_annot, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR)
+                    else:
+                        if need_annotate:
+                            annotated = draw_boxes(annotated, result0, conf_threshold=conf)
+
+                    # Overlay inference time on the annotated frame
+                    if need_annotate:
+                        inf_text = f"Infer: {t_inf * 1000:.1f}ms"
+                        cv2.putText(annotated, inf_text, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+                except Exception:
+                        # drawing shouldn't crash the loop
+                        if args.verbose:
+                            print('[VERBOSE] draw_boxes failed, continuing')
+                if result0 is not None:
+                    try:
+                        dets = []
+                        names = result0.names if hasattr(result0, 'names') else {}
+                        boxes = getattr(result0, 'boxes', None)
+                        if boxes is not None and len(boxes) > 0:
+                            try:
+                                xyxy = boxes.xyxy.cpu().numpy()
+                                confs = boxes.conf.cpu().numpy()
+                                classes = boxes.cls.cpu().numpy().astype(int)
+                                track_ids = None
+                                if getattr(boxes, 'id', None) is not None:
+                                    try:
+                                        track_ids = boxes.id.cpu().numpy().astype(int)
+                                    except Exception:
+                                        track_ids = None
+                                masks_xy = None
+                                masks = getattr(result0, 'masks', None)
+                                if masks is not None and hasattr(masks, 'xy'):
+                                    masks_xy = masks.xy
+                                # If we inferred on a smaller image, scale boxes to original image size
+                                if infer_scale < 1.0:
+                                    scale_x = float(orig_w) / float(infer_img.shape[1])
+                                    scale_y = float(orig_h) / float(infer_img.shape[0])
+                                else:
+                                    scale_x = scale_y = 1.0
+
+                                for i, ((x1, y1, x2, y2), conf, cls) in enumerate(zip(xyxy, confs, classes)):
+                                    box_x1 = float(x1) * scale_x
+                                    box_y1 = float(y1) * scale_y
+                                    box_x2 = float(x2) * scale_x
+                                    box_y2 = float(y2) * scale_y
+                                    dets.append({
+                                        'x1': box_x1, 'y1': box_y1,
+                                        'x2': box_x2, 'y2': box_y2,
+                                        'conf': float(conf), 'cls': int(cls), 'label': names.get(int(cls), str(int(cls)))
+                                    })
+                                    if track_ids is not None and i < len(track_ids):
+                                        dets[-1]['track_id'] = int(track_ids[i])
+                                    if masks_xy is not None and i < len(masks_xy):
+                                        try:
+                                            poly = np.asarray(masks_xy[i], dtype=np.float32)
+                                            if poly.ndim == 2 and poly.shape[0] >= 3:
+                                                poly_abs = np.stack(
+                                                    [poly[:, 0] * scale_x, poly[:, 1] * scale_y],
+                                                    axis=1
+                                                )
+                                                poly_rel = np.stack(
+                                                    [poly_abs[:, 0] - box_x1, poly_abs[:, 1] - box_y1],
+                                                    axis=1
+                                                )
+                                                dets[-1]['poly'] = poly_rel.tolist()
+                                        except Exception:
+                                            pass
+                            except Exception:
+                                try:
+                                    arr = result0.boxes.numpy()
+                                    # numpy fallback: arr columns: x1,y1,x2,y2,score,class
+                                    for r in arr:
+                                        x1, y1, x2, y2, conf, cls = r[:6]
+                                        s_x = float(orig_w) / float(infer_img.shape[1])
+                                        s_y = float(orig_h) / float(infer_img.shape[0])
+
+                                        dets.append({
+                                            'x1': float(x1) * s_x, 'y1': float(y1) * s_y,
+                                            'x2': float(x2) * s_x, 'y2': float(y2) * s_y,
+                                            'conf': float(conf), 'cls': int(cls), 'label': names.get(int(cls), str(int(cls)))
+                                        })
+                                except Exception:
+                                    dets = []
+
+                        # Temporal mask carry-over by track id reduces segmentation flicker when a mask drops
+                        if dets and tracker_cfg != 'none':
+                            for d in dets:
+                                tid = d.get('track_id')
+                                if tid is None:
+                                    continue
+                                tid = int(tid)
+                                if 'poly' in d and d['poly']:
+                                    track_poly_cache[tid] = d['poly']
+                                    track_last_seen[tid] = recv_count
+                                else:
+                                    prev_poly = track_poly_cache.get(tid)
+                                    if prev_poly is not None:
+                                        d['poly'] = prev_poly
+                                    track_last_seen[tid] = recv_count
+
+                            # prune stale tracks
+                            stale = [tid for tid, last in track_last_seen.items() if (recv_count - last) > 120]
+                            for tid in stale:
+                                track_last_seen.pop(tid, None)
+                                track_poly_cache.pop(tid, None)
+
+                        latest_dets = dets
+                    except Exception as e:
+                        if args.verbose:
+                            print(f"[VERBOSE] failed to convert inference boxes: {e}")
+            if not process_frame:
+                # not processing this frame: reuse previous annotated frame if available
+                if args.verbose:
+                    print("[VERBOSE] skipping inference for this frame to reduce load")
+
+            # publish unified stream every received frame using latest detections
+            if (pub is not None or fanin_push is not None) and publish_enabled_cfg:
+                try:
+                    if publish_is_bayer:
+                        payload_shape = [int(img.shape[0]), int(img.shape[1])]
+                        payload_pixel_format = 'BAYER'
+                    else:
+                        payload_shape = [int(img.shape[0]), int(img.shape[1]), int(img.shape[2])]
+                        payload_pixel_format = 'BGR'
+                    payload = {
+                        'time': time.time(),
+                        'frame_id': recv_count,
+                        'shape': payload_shape,
+                        'pixel_format': payload_pixel_format,
+                        'type': 'uint8',
+                        'boxes': latest_dets,
+                        'source_fps': float(source_fps),
+                        'prediction_fps': float(prediction_fps),
+                        'conf': float(conf),
+                        'infer_scale': float(infer_scale_cfg),
+                        'skip': int(skip_cfg),
+                        'max_fps': float(max_fps_cfg),
+                        'tracker': tracker_cfg,
+                        'publish_enabled': bool(publish_enabled_cfg),
+                        'worker_shard_index': int(shard_index_cfg),
+                        'worker_shard_count': int(shard_count_cfg),
+                        'predicted_this_frame': bool(predicted_this_frame),
+                        'focus_score': (float(latest_focus_score) if latest_focus_score is not None else None),
+                    }
+                    if compute_target_point_cfg:
+                        payload['target_point'] = compute_target_point(latest_dets)
+                    if publish_is_bayer and publish_bayer_pattern:
+                        payload['bayer_pattern'] = publish_bayer_pattern
+                    j = json.dumps(payload).encode('utf-8')
+                    topic = args.publish_topic.encode('utf-8') if args.publish_topic else b''
+                    if fanin_push is not None:
+                        fanin_push.send_multipart([topic, j, outgoing_image_buf], copy=False)
+                    elif pub is not None:
+                        pub.send_multipart([topic, j, outgoing_image_buf], copy=False)
+                        merged_publish_total += 1
+                        _tick_fps_window(merged_publish_window)
+                        if predicted_this_frame:
+                            merged_prediction_total += 1
+                            _tick_fps_window(merged_prediction_window)
+                except Exception as e:
+                    if args.verbose:
+                        print(f"[VERBOSE] failed to publish unified stream: {e}")
+
+            # Calculate and Draw FPS for all outputs
+            if args.display_fps:
+                frame_count += 1
+                if frame_count >= 5:
+                    now = time.time()
+                    fps = frame_count / (now - last_time)
+                    last_time = now
+                    frame_count = 0
+                
+                # Draw both Source and Processing FPS
+                fps_text = f"Source: {source_fps:.1f} | Proc: {fps:.1f}"
+                cv2.putText(annotated, fps_text, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
+
+            # push latest annotated frame to HTTP MJPEG encoder queue
+            if args.http_port and args.http_port > 0 and args.encode_stream:
+                submit_stream_frame(annotated)
+
+            # no publishing; just display locally
+            # display
+            if args.show:
+                cv2.imshow('annotated', annotated)
+
+    except KeyboardInterrupt:
+        print('Interrupted')
+    finally:
+        if args.config_autosave:
+            save_err = save_runtime_config(args.config, _build_persisted_config())
+            if save_err:
+                print(f"[Config] {save_err}", flush=True)
+        focus_writer.close()
+        if fanin_pull is not None:
+            fanin_pull.close()
+        if fanin_push is not None:
+            fanin_push.close()
+        if ingress_pull is not None:
+            ingress_pull.close()
+        for s in dist_push_by_shard.values():
+            try:
+                s.close()
+            except Exception:
+                pass
+        if sub is not None:
+            sub.close()
+        ctx.term()
+        if http_server:
+            http_server.shutdown()
+        cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/src/server/focus_epics.py b/src/server/focus_epics.py
new file mode 100644
index 0000000..9a584c6
--- /dev/null
+++ b/src/server/focus_epics.py
@@ -0,0 +1,196 @@
+import queue
+import time
+from threading import Event, Lock, Thread
+from typing import Any
+
+import cv2
+import numpy as np
+
+
+def compute_focus_score_bgr(img_bgr: np.ndarray, scale: float = 0.5) -> float:
+    """
+    Fast focus proxy using variance of Laplacian.
+    Lower scale reduces CPU cost substantially with modest sensitivity tradeoff.
+    """
+    if img_bgr is None or img_bgr.size == 0:
+        return 0.0
+    work = img_bgr
+    s = float(scale)
+    if 0.0 < s < 1.0:
+        h, w = img_bgr.shape[:2]
+        nw = max(1, int(w * s))
+        nh = max(1, int(h * s))
+        work = cv2.resize(work, (nw, nh), interpolation=cv2.INTER_AREA)
+    gray = cv2.cvtColor(work, cv2.COLOR_BGR2GRAY)
+    return float(cv2.Laplacian(gray, cv2.CV_64F).var())
+
+
+class FocusPvWriter:
+    """Non-blocking EPICS writer for focus scores."""
+
+    def __init__(self, pv_name: str, enabled: bool, min_period_ms: float = 100.0):
+        self._lock = Lock()
+        self._queue = queue.Queue(maxsize=1)
+        self._stop = Event()
+        self._thread = Thread(target=self._worker, daemon=True)
+        self._enabled = bool(enabled)
+        self._pv_name = str(pv_name or "").strip()
+        self._min_period_s = max(0.0, float(min_period_ms) / 1000.0)
+        self._last_write_ts = 0.0
+        self._last_write_value = None
+        self._last_error = ""
+        self._submit_count = 0
+        self._drop_count = 0
+        self._write_count = 0
+        self._last_log_ts = 0.0
+        self._backend = "none"
+        self._epics = None
+        self._pv = None
+        self._caproto_write = None
+        self._thread.start()
+
+    def _log(self, msg: str, min_interval_s: float = 5.0) -> None:
+        now = time.time()
+        if (now - self._last_log_ts) >= min_interval_s:
+            print(f"[FocusPvWriter] {msg}", flush=True)
+            self._last_log_ts = now
+
+    def _connect_if_needed(self) -> bool:
+        if self._backend == "caproto" and self._caproto_write is not None:
+            return True
+        if self._pv is not None:
+            return True
+        if not self._pv_name:
+            self._last_error = "focus_pv is empty"
+            self._log("focus_pv is empty; EPICS writes disabled", min_interval_s=10.0)
+            return False
+        try:
+            if self._epics is None:
+                import epics  # type: ignore
+                self._epics = epics
+            self._pv = self._epics.PV(self._pv_name, auto_monitor=False)
+            self._backend = "pyepics"
+            self._log(f"connected to PV '{self._pv_name}'", min_interval_s=0.0)
+            return True
+        except Exception as e:
+            self._last_error = str(e)
+            self._pv = None
+            self._log(
+                f"pyepics failed for '{self._pv_name}': {e}. Trying caproto fallback.",
+                min_interval_s=5.0,
+            )
+            try:
+                from caproto.sync.client import write as caproto_write  # type: ignore
+
+                self._caproto_write = caproto_write
+                self._backend = "caproto"
+                self._log(
+                    f"using caproto fallback backend for PV '{self._pv_name}'",
+                    min_interval_s=0.0,
+                )
+                return True
+            except Exception as caproto_exc:
+                self._last_error = f"pyepics: {e}; caproto: {caproto_exc}"
+                self._backend = "none"
+                self._log(
+                    f"caproto fallback failed for '{self._pv_name}': {caproto_exc}",
+                    min_interval_s=5.0,
+                )
+                return False
+
+    def configure(
+        self,
+        *,
+        enabled: bool | None = None,
+        pv_name: str | None = None,
+        min_period_ms: float | None = None,
+    ) -> None:
+        with self._lock:
+            if enabled is not None:
+                self._enabled = bool(enabled)
+            if pv_name is not None:
+                new_name = str(pv_name or "").strip()
+                if new_name != self._pv_name:
+                    self._pv_name = new_name
+                    self._pv = None
+                    self._caproto_write = None
+                    self._backend = "none"
+            if min_period_ms is not None:
+                self._min_period_s = max(0.0, float(min_period_ms) / 1000.0)
+
+    def submit(self, value: float) -> None:
+        with self._lock:
+            enabled = self._enabled
+        if not enabled:
+            return
+        self._submit_count += 1
+        try:
+            self._queue.put_nowait(float(value))
+        except queue.Full:
+            self._drop_count += 1
+            try:
+                _ = self._queue.get_nowait()
+            except Exception:
+                pass
+            try:
+                self._queue.put_nowait(float(value))
+            except Exception:
+                pass
+
+    def status(self) -> dict[str, Any]:
+        with self._lock:
+            enabled = self._enabled
+            pv_name = self._pv_name
+            min_period_ms = self._min_period_s * 1000.0
+        return {
+            "enabled": bool(enabled),
+            "pv_name": pv_name,
+            "min_period_ms": float(min_period_ms),
+            "backend": self._backend,
+            "connected": bool(self._pv is not None) or bool(self._backend == "caproto"),
+            "submit_count": int(self._submit_count),
+            "drop_count": int(self._drop_count),
+            "write_count": int(self._write_count),
+            "last_write_ts": float(self._last_write_ts),
+            "last_write_value": self._last_write_value,
+            "last_error": self._last_error,
+        }
+
+    def _worker(self) -> None:
+        while not self._stop.is_set():
+            try:
+                val = float(self._queue.get(timeout=0.2))
+            except queue.Empty:
+                continue
+            with self._lock:
+                enabled = self._enabled
+                min_period_s = self._min_period_s
+            if not enabled:
+                continue
+            now = time.time()
+            if min_period_s > 0.0 and (now - self._last_write_ts) < min_period_s:
+                continue
+            if not self._connect_if_needed():
+                continue
+            try:
+                if self._backend == "caproto" and self._caproto_write is not None:
+                    self._caproto_write(self._pv_name, val, notify=False, timeout=0.5)
+                else:
+                    self._pv.put(val, wait=False)
+                self._last_write_ts = now
+                self._last_write_value = val
+                self._write_count += 1
+            except Exception as e:
+                self._last_error = str(e)
+                self._pv = None
+                if self._backend == "caproto":
+                    self._caproto_write = None
+                self._backend = "none"
+                self._log(f"write failed for '{self._pv_name}': {e}", min_interval_s=5.0)
+
+    def close(self) -> None:
+        self._stop.set()
+        try:
+            self._thread.join(timeout=1.0)
+        except Exception:
+            pass
diff --git a/src/server/image_utils.py b/src/server/image_utils.py
new file mode 100644
index 0000000..2900fe7
--- /dev/null
+++ b/src/server/image_utils.py
@@ -0,0 +1,89 @@
+import cv2
+import numpy as np
+
+
+# per-class color mapping (BGR tuples for OpenCV)
+# keep colors consistent with your labeling tool:
+# class 0 = GREEN, 1 = RED, 2 = BLUE, 3 = YELLOW
+CLASS_COLORS = {
+    0: (0, 255, 0),   # green
+    1: (0, 0, 255),   # red
+    2: (255, 0, 0),   # blue
+    3: (0, 255, 255), # yellow (cyan in BGR)
+}
+
+
+def decode_image(msg_bytes):
+    # try to decode raw JPEG/PNG bytes robustly
+    try:
+        # handle memoryview/bytes/bytearray
+        if isinstance(msg_bytes, memoryview):
+            buf = msg_bytes.tobytes()
+        else:
+            buf = bytes(msg_bytes)
+        arr = np.frombuffer(buf, dtype=np.uint8)
+        # ensure contiguous writable array for OpenCV
+        arr = arr.copy()
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        if img is not None:
+            return img
+    except Exception:
+        pass
+
+    # fallback: if the payload is base64-encoded text, try to decode
+    try:
+        import base64
+
+        b = base64.b64decode(msg_bytes)
+        arr = np.frombuffer(b, dtype=np.uint8).copy()
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        return img
+    except Exception:
+        return None
+
+
+def draw_boxes(img, results, conf_threshold=0.25):
+    # results: ultralytics.Results for single image
+    if results is None:
+        return img
+    boxes = getattr(results, 'boxes', None)
+    if boxes is None or len(boxes) == 0:
+        return img
+
+    names = results.names if hasattr(results, 'names') else {}
+    try:
+        xyxy = boxes.xyxy.cpu().numpy()
+        confs = boxes.conf.cpu().numpy()
+        classes = boxes.cls.cpu().numpy().astype(int)
+    except Exception:
+        # fallback to converting to numpy via .numpy()
+        arr = results.boxes.numpy()
+        # arr columns: x1,y1,x2,y2,score,class
+        xyxy = arr[:, :4]
+        confs = arr[:, 4]
+        classes = arr[:, 5].astype(int)
+
+    for (x1, y1, x2, y2), conf, cls in zip(xyxy, confs, classes):
+        if conf < conf_threshold:
+            continue
+        x1i, y1i, x2i, y2i = int(x1), int(y1), int(x2), int(y2)
+        label = names.get(cls, str(cls))
+        # pick color from mapping, fallback to a deterministic hashed color
+        try:
+            color = CLASS_COLORS.get(int(cls))
+            if color is None:
+                raise KeyError()
+        except Exception:
+            # deterministic fallback: create color from class id
+            v = int(cls)
+            # simple hashing to BGR range
+            b = (37 * v) % 256
+            g = (17 * v) % 256
+            r = (97 * v) % 256
+            color = (b, g, r)
+        cv2.rectangle(img, (x1i, y1i), (x2i, y2i), color, 2)
+        text = f"{label} {conf:.2f}"
+        ((tw, _th), _) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+        cv2.rectangle(img, (x1i, y1i - 20), (x1i + tw, y1i), color, -1)
+        cv2.putText(img, text, (x1i, y1i - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
+    return img
diff --git a/src/server/model_utils.py b/src/server/model_utils.py
new file mode 100644
index 0000000..4b9d588
--- /dev/null
+++ b/src/server/model_utils.py
@@ -0,0 +1,128 @@
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+from ultralytics import YOLO
+
+
+def resolve_task(task: str, pt_path: str, engine_path: str) -> str:
+    """Resolve model task from explicit arg or filename hint."""
+    if task in ("detect", "segment"):
+        return task
+    probe = f"{engine_path or ''} {pt_path or ''}".lower()
+    if "seg" in probe:
+        return "segment"
+    return "detect"
+
+
+def project_root() -> Path:
+    """
+    Resolve repository root robustly even if this module is moved.
+    Prefer the closest ancestor containing a 'models' directory.
+    """
+    here = Path(__file__).resolve()
+    for candidate in here.parents:
+        if (candidate / "models").exists():
+            return candidate
+    # Fallback keeps previous behavior if the models directory does not exist yet.
+    return here.parent.parent
+
+
+def resolve_model_path(path_value: str) -> tuple[str, list[str]]:
+    """
+    Resolve a model path robustly across differing working directories.
+    Returns: (resolved_path_or_raw, tried_paths)
+    """
+    raw = (path_value or "").strip()
+    if not raw:
+        return "", []
+
+    p = Path(raw).expanduser()
+    tried: list[Path] = [p]
+
+    if p.exists():
+        return str(p), [str(x) for x in tried]
+
+    root = project_root()
+    models_dir = root / "models"
+
+    root_candidate = (root / p)
+    tried.append(root_candidate)
+    if root_candidate.exists():
+        return str(root_candidate.resolve()), [str(x) for x in tried]
+
+    raw_norm = raw.replace("\\", "/")
+    if raw_norm.startswith("models/"):
+        rel_in_models = raw_norm[len("models/"):]
+        model_candidate = models_dir / rel_in_models
+        tried.append(model_candidate)
+        if model_candidate.exists():
+            return str(model_candidate.resolve()), [str(x) for x in tried]
+
+    basename = Path(raw_norm).name
+    if basename and models_dir.exists():
+        matches = sorted(x for x in models_dir.rglob(basename) if x.is_file())
+        for m in matches:
+            tried.append(m)
+        if len(matches) == 1:
+            return str(matches[0].resolve()), [str(x) for x in tried]
+
+    return raw, [str(x) for x in tried]
+
+
+def is_segmentation_capable_name(name: str) -> bool:
+    lowered = (name or "").lower()
+    return any(token in lowered for token in ("seg", "segment", "mask"))
+
+
+def list_available_models() -> list[dict[str, Any]]:
+    root = project_root()
+    models_dir = root / "models"
+    if not models_dir.exists():
+        return []
+
+    valid_suffixes = {".pt", ".engine", ".engines", ".yaml", ".yml", ".onnx"}
+    items: list[dict[str, Any]] = []
+    for p in sorted(models_dir.rglob("*")):
+        if not p.is_file() or p.suffix.lower() not in valid_suffixes:
+            continue
+        rel_from_models = str(p.relative_to(models_dir))
+        rel_from_root = str(p.relative_to(root))
+        segmentation_capable = is_segmentation_capable_name(p.name)
+        items.append(
+            {
+                "name": rel_from_models,
+                "path": str(p),
+                "project_path": rel_from_root,
+                "kind": p.suffix.lower().lstrip("."),
+                "segmentation_capable": segmentation_capable,
+                "task_hint": "segment" if segmentation_capable else "detect",
+            }
+        )
+    return items
+
+
+def load_model(pt_path: str, engine_path: str, task: str):
+    resolved_pt, pt_tried = resolve_model_path(pt_path)
+    resolved_engine, engine_tried = resolve_model_path(engine_path)
+    pt_path = resolved_pt
+    engine_path = resolved_engine
+
+    resolved_task = resolve_task(task, pt_path, engine_path)
+    if engine_path and os.path.exists(engine_path):
+        print(f"Loading TensorRT engine: {engine_path}")
+        print(f"Model task: {resolved_task}")
+        return YOLO(engine_path, task=resolved_task)
+    if pt_path and os.path.exists(pt_path):
+        print(f"Loading PyTorch model: {pt_path}")
+        print(f"Model task: {resolved_task}")
+        return YOLO(pt_path, task=resolved_task)
+    debug = {
+        "engine": engine_path,
+        "pt": pt_path,
+        "engine_tried": engine_tried,
+        "pt_tried": pt_tried,
+        "project_root": str(project_root()),
+    }
+    raise FileNotFoundError(f"No valid model found. Resolution details: {json.dumps(debug)}")
diff --git a/src/server/runtime_config.py b/src/server/runtime_config.py
new file mode 100644
index 0000000..36c6aeb
--- /dev/null
+++ b/src/server/runtime_config.py
@@ -0,0 +1,42 @@
+import json
+from pathlib import Path
+from typing import Any
+
+
+def load_runtime_config(path: str) -> tuple[dict[str, Any], str | None]:
+    p = Path(path).expanduser()
+    if not p.exists():
+        return {}, None
+    try:
+        data = json.loads(p.read_text(encoding="utf-8"))
+    except Exception as e:
+        return {}, f"Failed to parse config '{p}': {e}"
+    if not isinstance(data, dict):
+        return {}, f"Config '{p}' must be a JSON object"
+    return data, None
+
+
+def save_runtime_config(path: str, config: dict[str, Any]) -> str | None:
+    p = Path(path).expanduser()
+    try:
+        p.parent.mkdir(parents=True, exist_ok=True)
+        p.write_text(json.dumps(config, indent=2, sort_keys=True), encoding="utf-8")
+        return None
+    except Exception as e:
+        return f"Failed to save config '{p}': {e}"
+
+
+def collect_cli_overrides(argv: list[str]) -> set[str]:
+    """
+    Collect option names explicitly passed on CLI, normalized to argparse dest form.
+    Example: --focus-epics-enabled -> focus_epics_enabled
+    """
+    out: set[str] = set()
+    for token in argv[1:]:
+        if not token.startswith("--"):
+            continue
+        key = token[2:]
+        if "=" in key:
+            key = key.split("=", 1)[0]
+        out.add(key.replace("-", "_"))
+    return out
diff --git a/src/server/stream_utils.py b/src/server/stream_utils.py
new file mode 100644
index 0000000..26393f4
--- /dev/null
+++ b/src/server/stream_utils.py
@@ -0,0 +1,70 @@
+import queue
+from http.server import BaseHTTPRequestHandler, HTTPServer
+from threading import Lock, Thread
+
+import cv2
+
+
+_latest_frame = None
+_frame_lock = Lock()
+_encode_queue = queue.Queue(maxsize=2)
+
+
+class MJPEGHandler(BaseHTTPRequestHandler):
+    def do_GET(self):
+        if self.path != '/stream' and self.path != '/':
+            self.send_response(200)
+            self.send_header('Content-type', 'text/html')
+            self.end_headers()
+            self.wfile.write(b"<html><body><img src='/stream' /></body></html>")
+            return
+        self.send_response(200)
+        self.send_header('Content-type', 'multipart/x-mixed-replace; boundary=FRAME')
+        self.end_headers()
+        try:
+            while True:
+                with _frame_lock:
+                    frame = _latest_frame
+                if frame:
+                    self.wfile.write(b'--FRAME\r\n')
+                    self.wfile.write(b'Content-Type: image/jpeg\r\n')
+                    self.wfile.write(f'Content-Length: {len(frame)}\r\n\r\n'.encode())
+                    self.wfile.write(frame)
+                    self.wfile.write(b'\r\n')
+                    try:
+                        self.wfile.flush()
+                    except Exception:
+                        pass
+        except Exception:
+            return
+
+
+def _encoding_worker(quality: int, scale_factor: float):
+    """Background thread to handle JPEG compression without blocking the GPU loop."""
+    global _latest_frame
+    while True:
+        try:
+            frame_to_encode = _encode_queue.get(timeout=1)
+            if scale_factor < 1.0:
+                h, w = frame_to_encode.shape[:2]
+                frame_to_encode = cv2.resize(frame_to_encode, (int(w * scale_factor), int(h * scale_factor)))
+            _, enc = cv2.imencode('.jpg', frame_to_encode, [int(cv2.IMWRITE_JPEG_QUALITY), quality])
+            with _frame_lock:
+                _latest_frame = enc.tobytes()
+        except queue.Empty:
+            continue
+
+
+def start_http_server(port: int, quality: int, scale_factor: float):
+    server = HTTPServer(('', port), MJPEGHandler)
+    Thread(target=server.serve_forever, daemon=True).start()
+    Thread(target=_encoding_worker, args=(quality, scale_factor), daemon=True).start()
+    return server
+
+
+def submit_stream_frame(frame) -> None:
+    """Non-blocking frame submission for HTTP MJPEG stream."""
+    try:
+        _encode_queue.put_nowait(frame)
+    except queue.Full:
+        pass
diff --git a/src/server/target_point.py b/src/server/target_point.py
new file mode 100644
index 0000000..c4e3519
--- /dev/null
+++ b/src/server/target_point.py
@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+from typing import Any
+
+import cv2
+import numpy as np
+
+
+def _poly_to_abs(poly: Any, x1: float, y1: float) -> np.ndarray | None:
+    """Convert det['poly'] (relative-to-box) into absolute image points."""
+    if poly is None:
+        return None
+    try:
+        pts = np.asarray(poly, dtype=np.float32)
+    except Exception:
+        return None
+    if pts.ndim != 2 or pts.shape[1] != 2 or pts.shape[0] < 3:
+        return None
+    pts_abs = np.empty_like(pts)
+    pts_abs[:, 0] = pts[:, 0] + float(x1)
+    pts_abs[:, 1] = pts[:, 1] + float(y1)
+    return pts_abs
+
+
+def _shape_center(det: dict[str, Any]) -> tuple[float, float] | None:
+    """Center of mass of polygon shape if present, otherwise bbox center."""
+    try:
+        x1 = float(det.get("x1", 0.0))
+        y1 = float(det.get("y1", 0.0))
+        x2 = float(det.get("x2", 0.0))
+        y2 = float(det.get("y2", 0.0))
+    except Exception:
+        return None
+
+    pts_abs = _poly_to_abs(det.get("poly"), x1, y1)
+    if pts_abs is not None:
+        try:
+            cnt = pts_abs.reshape((-1, 1, 2))
+            m = cv2.moments(cnt)
+            if abs(m["m00"]) > 1e-6:
+                return float(m["m10"] / m["m00"]), float(m["m01"] / m["m00"])
+        except Exception:
+            pass
+
+    return 0.5 * (x1 + x2), 0.5 * (y1 + y2)
+
+
+def _loop_left_area_center(det: dict[str, Any]) -> tuple[float, float] | None:
+    """
+    Target point for loop_all:
+    center of the left-side loop area instead of the top-most apex.
+    """
+    try:
+        x1 = float(det.get("x1", 0.0))
+        y1 = float(det.get("y1", 0.0))
+        x2 = float(det.get("x2", 0.0))
+        y2 = float(det.get("y2", 0.0))
+    except Exception:
+        return None
+
+    pts_abs = _poly_to_abs(det.get("poly"), x1, y1)
+    if pts_abs is not None and len(pts_abs) > 0:
+        min_x = float(np.min(pts_abs[:, 0]))
+        max_x = float(np.max(pts_abs[:, 0]))
+        min_y = float(np.min(pts_abs[:, 1]))
+        max_y = float(np.max(pts_abs[:, 1]))
+
+        width = max_x - min_x
+        height = max_y - min_y
+        if width <= 1e-6 or height <= 1e-6:
+            return 0.5 * (x1 + x2), 0.5 * (y1 + y2)
+
+        y_mid = 0.5 * (min_y + max_y)
+
+        # Keep only points near the left edge of the shape.
+        # This tends to land inside the left "loop area".
+        left_band = pts_abs[pts_abs[:, 0] <= (min_x + 0.35 * width)]
+        if len(left_band) == 0:
+            left_band = pts_abs
+
+        # Prefer points closest to the vertical middle of that left band.
+        idx = int(np.argmin(np.abs(left_band[:, 1] - y_mid)))
+        candidate = left_band[idx]
+
+        # Blend with the left-band centroid for stability.
+        centroid = np.mean(left_band, axis=0)
+        return float(0.5 * (candidate[0] + centroid[0])), float(0.5 * (candidate[1] + centroid[1]))
+
+    return 0.5 * (x1 + x2), 0.5 * (y1 + y2)
+
+
+def _pin_left_midpoint(det: dict[str, Any]) -> tuple[float, float] | None:
+    """Left-middle point of the pin bounding box."""
+    try:
+        x1 = float(det.get("x1", 0.0))
+        y1 = float(det.get("y1", 0.0))
+        y2 = float(det.get("y2", 0.0))
+    except Exception:
+        return None
+    return x1, 0.5 * (y1 + y2)
+
+
+def _pick_best(dets: list[dict[str, Any]], label: str) -> dict[str, Any] | None:
+    class_hits = [d for d in dets if str(d.get("label", "")) == label]
+    if not class_hits:
+        return None
+    return max(class_hits, key=lambda d: float(d.get("conf", 0.0)))
+
+
+def compute_target_point(dets: list[dict[str, Any]]) -> dict[str, Any] | None:
+    """
+    Target priority:
+    1) crystal   -> center of mass of crystal shape
+    2) loop_face -> center of mass of loop_face shape
+    3) loop_all  -> center of left-side loop area
+    4) pin       -> left middle side of pin box
+    """
+    if not dets:
+        return None
+
+    crystal = _pick_best(dets, "crystal")
+    if crystal is not None:
+        pt = _shape_center(crystal)
+        if pt is not None:
+            return {"x": int(round(pt[0])), "y": int(round(pt[1])), "source": "crystal_center"}
+
+    loop_face = _pick_best(dets, "loop_face")
+    if loop_face is not None:
+        pt = _shape_center(loop_face)
+        if pt is not None:
+            return {"x": int(round(pt[0])), "y": int(round(pt[1])), "source": "loop_face_center"}
+
+    loop_all = _pick_best(dets, "loop_all")
+    if loop_all is not None:
+        pt = _loop_left_area_center(loop_all)
+        if pt is not None:
+            return {"x": int(round(pt[0])), "y": int(round(pt[1])), "source": "loop_all_left_area_center"}
+
+    pin = _pick_best(dets, "pin")
+    if pin is not None:
+        pt = _pin_left_midpoint(pin)
+        if pt is not None:
+            return {"x": int(round(pt[0])), "y": int(round(pt[1])), "source": "pin_left_mid"}
+
+    return None
diff --git a/src/tools/__init__.py b/src/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/tools/alc_integration_yolo.py b/src/tools/alc_integration_yolo.py
new file mode 100644
index 0000000..a4b1716
--- /dev/null
+++ b/src/tools/alc_integration_yolo.py
@@ -0,0 +1,42 @@
+from ultralytics import YOLO
+import cv2
+
+def get_feature_0_bounding_boxes(image, model_path):
+    """
+    Processes a given image through the YOLO model and extracts the bounding boxes for feature/class 0.
+
+    Args:
+        image: The image object (e.g., an array or PIL.Image supported by YOLO).
+        model_path: Path to the YOLO model file (e.g., best.pt).
+
+    Returns:
+        List of bounding boxes for feature/class 0. Each bounding box is in the format [x1, y1, x2, y2].
+    """
+    # Load the YOLO model
+    model = YOLO(model_path)
+    # Perform prediction on the image
+    results = model.predict(source=image, conf=0.5, save=True, show=False)
+
+    # Extract the first bounding box for class 0
+    for r in results:
+        for box in r.boxes.data.tolist():  # Each box: [x1, y1, x2, y2, confidence, class_id]
+            x1, y1, x2, y2, conf, cls = box
+            if int(cls) == 0:  # Check if class_id is 0 (feature 0)
+                return [x1, y1, x2, y2]  # Return the first box for class 0
+    return None  # If no box is found, return None
+
+
+# Example usage
+if __name__ == "__main__":
+
+    # Provide your YOLO model path and an image
+    yolo_model_path = "/Users/duan_j/Applications/alc/tests/yolo/runs/aare-test/train8/weights/best.pt"  # Replace with your YOLO model's actual path
+    image_path = "/Users/duan_j/Applications/alc/tests/yolo/Snipaste_2025-02-25_14-43-11.jpg"  # Replace with your image's path
+
+    # Open image with PIL (YOLO supports this format)
+    image = cv2.imread(image_path)
+
+    # Get bounding boxes for class 0
+    feature_0_boxes = get_feature_0_bounding_boxes(image, yolo_model_path) #x1, y1, x2, y2
+
+    print("Bounding Boxes for Feature 0:", feature_0_boxes)
diff --git a/src/tools/annotating_imgs.py b/src/tools/annotating_imgs.py
new file mode 100644
index 0000000..0ecd808
--- /dev/null
+++ b/src/tools/annotating_imgs.py
@@ -0,0 +1,188 @@
+import cv2
+import numpy as np
+import os
+import glob
+import yaml
+from pathlib import Path
+
+def resize_to_screen(img, screen_width=1920, screen_height=1080):
+    """Resize the image to fit the screen while maintaining the aspect ratio."""
+    height, width, _ = img.shape
+    scaling_factor = min(screen_width / width, screen_height / height)
+    new_width = int(width * scaling_factor)
+    new_height = int(height * scaling_factor)
+    resized_img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)
+    return resized_img, scaling_factor
+
+def draw_and_record_boxes(image_path, screen_width=1920, screen_height=1080):
+    """Draw four boxes and record normalized YOLO box data."""
+    img = cv2.imread(image_path)
+
+    # Resize image to fit the screen
+    resized_img, scale = resize_to_screen(img, screen_width/2, screen_height/2)
+    height, width, _ = img.shape  # Original dimensions (for normalization)
+
+    while True:
+
+        boxes_data = []  # List to store box info
+        preview_img = resized_img.copy()
+
+        # Helper for selecting and recording a box
+        def select_and_record_box(box_name, color, class_id):
+            box = cv2.selectROI(box_name, resized_img, fromCenter=False, showCrosshair=True)
+            if box == (0, 0, 0, 0):  # no selection
+                return None
+            x, y, w, h = [int(coord / scale) for coord in box]  # Scale back to original size
+            rx, ry, rw, rh = [int(coord) for coord in box]  # use resized coords directly
+            cv2.rectangle(preview_img, (rx, ry), (rx + rw, ry + rh), color, 2)
+            #cv2.rectangle(resized_img, (x, y), (x + w, y + h), color, 2)
+            center_x = (x + w / 2) / width
+            center_y = (y + h / 2) / height
+            norm_w = w / width
+            norm_h = h / height
+            return [class_id, center_x, center_y, norm_w, norm_h]
+
+            # Boxes
+
+        box = select_and_record_box(f"Select Class 0: {class_dict[0].upper()} GREEN Box", (0, 255, 0), 0)
+        if box: boxes_data.append(box)
+
+        box = select_and_record_box(f"Select Class 1: {class_dict[1].upper()} RED Box", (0, 0, 255), 1)
+        if box: boxes_data.append(box)
+
+        box = select_and_record_box(f"Select Class 2: {class_dict[2].upper()} BLUE Box", (255, 0, 0), 2)
+        if box: boxes_data.append(box)
+
+        box = select_and_record_box(f"Select Class 3: {class_dict[3].upper()} YELLOW Box", (0, 255, 255), 3)
+        if box: boxes_data.append(box)
+
+        # Show annotated image
+        cv2.imshow("Annotated Image (Press: [a]=accept, [r]=restart, [q/ESC]=quit)", preview_img)
+        key = cv2.waitKey(0) & 0xFF  # Mask to 8-bit
+        cv2.destroyAllWindows()
+
+        if key in [ord('a'), 13]:  # 'a' or ENTER = accept
+            return boxes_data, False, preview_img
+        elif key == ord('r'):  # restart selection
+            print("Restarting annotation for this image...")
+            continue
+        elif key in [27, ord('q')]:  # ESC or 'q' = quit all
+            return boxes_data, True, preview_img
+        elif key == ord('s'):  # s = skip current image
+            print("Skipping this image...")
+            return [], False, preview_img
+        else:
+            print("Unrecognized key. Press [a]=accept, [r]=restart, [q]=quit.")
+
+        return boxes_data, False, preview_img
+
+
+def process_images_for_yolo(input_folder, labels_folder, screen_width=1920, screen_height=1080, overwrite_existing=None):
+    """Process images for YOLO by saving labeled bounding boxes for each image."""
+    # Supported image extensions
+    image_extensions = ('*.png', '*.jpg', '*.jpeg', '*.bmp', '*.tif', '*.tiff')
+
+    # Collect all image file paths from the input folder
+    image_paths = []
+    for ext in image_extensions:
+        image_paths.extend(glob.glob(os.path.join(input_folder, ext)))
+
+    if not image_paths:
+        print(f"No images found in the input folder: {input_folder}")
+        return
+
+    # Create the labels folder if it doesn't exist
+    if not os.path.exists(labels_folder):
+        os.makedirs(labels_folder)
+
+    print("\n--- YOLO Image Annotation Controls ---")
+    print("[ESC] or [q]  → quit annotation completely")
+    print("[c] - cancel process")
+    print("[r]            → restart current image")
+    print("[s]           → skip current image")
+    print("[any other]   → save boxes & move to next image")
+    print("---------------------------------------\n")
+
+    # Iterate over each image
+    for image_path in image_paths:
+        print(f"Processing: {image_path}")
+        image_name = os.path.splitext(os.path.basename(image_path))[0]
+        output_file_path = os.path.join(labels_folder, f"{image_name}.txt")
+
+        if os.path.exists(output_file_path):
+            if overwrite_existing is True:
+                print(f"overwriteing exisiting label for {image_name}.txt")
+            elif overwrite_existing is False:
+                print(f"Skipping {image_name} as already labeled")
+                continue
+            else:
+                print(f"⚠️ Label already exists for {image_name}.txt")
+                choice = input("[o] → overwrite, [s] → skip, [q/ESC] → quit: "). strip().lower()
+
+                if choice == "s":
+                    print(f"Skipping {image_name} (already labeled).")
+                    continue
+                elif choice == "q":
+                    print("Exiting early...")
+                    return
+                elif choice == "o":
+                    print(f"Overwriting label for {image_name}...")
+                else:
+                    print("Unknown choice, skipping this image.")
+                    continue
+
+        print(f"\nProcessing: {image_path}")
+
+        while True:
+            # Get normalized box data from user interaction
+            boxes_data, exit_flag, preview_img = draw_and_record_boxes(image_path, screen_width, screen_height)
+
+            if exit_flag:  # Exit the annotation process if Ctrl+W is pressed
+                print("Exiting image annotation early...")
+                return
+
+            if boxes_data:  # Write the annotations for this image file if boxes are drawn
+                image_name = os.path.splitext(os.path.basename(image_path))[0]  # File name without extension
+                output_file_path = os.path.join(labels_folder, f"{image_name}.txt")
+
+                # Write each box to its corresponding text file in YOLO format
+                with open(output_file_path, 'w') as f:
+                    for box in boxes_data:
+                        class_id, center_x, center_y, norm_w, norm_h = box
+                        f.write(f"{class_id} {center_x:.6f} {center_y:.6f} {norm_w:.6f} {norm_h:.6f}\n")
+
+                #cv2.imshow(f"Saved Annotation: {image_name}", preview_img)
+                #cv2.waitKey(1500)  # wait 1.5 seconds or until key is pressed
+                #cv2.destroyAllWindows()
+
+                break
+
+            else:
+                print("skipping this image...")
+                break
+
+    print(f"Annotations saved to {labels_folder}")
+
+
+
+if __name__ == "__main__":
+    # Define input folder containing images
+    input_folder = r"/Users/gotthardg/Volumes/ra_work/Sept/annotations" #"/Users/duan_j/Applications/alc/tests/yolo/nodetec/pin2"  # Change to your folder path
+    #overwrite flag to say whether to overwrite existing labels, skip all labeled or enable user choice.
+    overwrite_flag = True
+    # Define the labels output folder for YOLO format
+    labels_folder = f"{input_folder}labels"  # Change to your desired folder
+
+    # Path to your yaml file
+    yaml_file = str(Path(__file__).resolve().parents[2] / "config" / "dataset.yaml")
+
+    with open(yaml_file, "r") as f:
+        data = yaml.safe_load(f)
+
+    # Extract names dictionary
+    class_dict = data.get("names", {})
+
+    print(class_dict[0])
+
+    # Process images and save YOLO-compatible annotations
+    process_images_for_yolo(input_folder, labels_folder, overwrite_existing=overwrite_flag)
diff --git a/src/tools/autofocus_tools.py b/src/tools/autofocus_tools.py
new file mode 100644
index 0000000..6674702
--- /dev/null
+++ b/src/tools/autofocus_tools.py
@@ -0,0 +1,59 @@
+import cv2
+import numpy as np
+
+def focus_measure_edges(gray: np.ndarray, mask: np.ndarray | None = None) -> float:
+    # mild denoise (optional but usually stabilizes the curve)
+    gray = cv2.GaussianBlur(gray, (3, 3), 0)
+
+    gx = cv2.Scharr(gray, cv2.CV_64F, 1, 0)
+    gy = cv2.Scharr(gray, cv2.CV_64F, 0, 1)
+    g2 = gx * gx + gy * gy
+
+    roi = g2[mask] if mask is not None else g2.reshape(-1)
+    if roi.size == 0:
+        return 0.0
+
+    # threshold relative to median -> knocks out noise floor
+    t = float(np.median(roi) * 3.0)
+    strong = roi[roi > t]
+
+    if strong.size == 0:
+        return 0.0
+
+    print(f"mask pixels: {mask.sum()}, "
+          f"focus={strong.mean():.2f}"
+          f"strong_size={strong.size}")
+
+    return float(strong.mean())  # higher = sharper
+
+def focus_measure_blob_size(gray: np.ndarray, mask: np.ndarray | None = None) -> float:
+    """
+    Measures sharpness for a single bright blob.
+    Higher = sharper (smaller blob).
+    """
+    g = gray.astype(np.float64)
+
+    if mask is not None:
+        g = np.where(mask, g, 0.0)
+
+    # Background subtraction is crucial for blob metrics
+    # Use a large-ish blur as background estimate (tune ksize to your scale)
+    bg = cv2.GaussianBlur(g, (0, 0), sigmaX=10.0, sigmaY=10.0)
+    s = g - bg
+    s[s < 0] = 0.0
+
+    total = float(s.sum())
+    if total <= 0:
+        return 0.0
+
+    h, w = s.shape
+    y, x = np.mgrid[0:h, 0:w]
+
+    cx = float((s * x).sum() / total)
+    cy = float((s * y).sum() / total)
+
+    # intensity-weighted second central moment (variance)
+    var = float((s * ((x - cx) ** 2 + (y - cy) ** 2)).sum() / total)
+
+    # smaller var => sharper, so invert
+    return float(1.0 / (var + 1e-9))
\ No newline at end of file
diff --git a/src/tools/file_names_record.py b/src/tools/file_names_record.py
new file mode 100644
index 0000000..f7c0f01
--- /dev/null
+++ b/src/tools/file_names_record.py
@@ -0,0 +1,53 @@
+import os
+
+
+def save_file_names_to_record(training_folder, validation_folder, test_folder, output_file):
+    """
+    Reads file names from training, validation, and test folders and saves them to a text file.
+
+    Args:
+        training_folder (str): Path to the training folder.
+        validation_folder (str): Path to the validation folder.
+        test_folder (str): Path to the test folder.
+        output_file (str): Path to the output text file.
+    """
+    try:
+        # Collect file names from the training folder
+        training_files = os.listdir(training_folder)
+        training_files.sort()  # Sort the files alphabetically for consistency
+
+        # Collect file names from the validation folder
+        validation_files = os.listdir(validation_folder)
+        validation_files.sort()
+
+        # Collect file names from the test folder
+        test_files = os.listdir(test_folder)
+        test_files.sort()
+
+        # Save the file names to the output file
+        with open(output_file, 'w') as file:
+            file.write("Training Files:\n")
+            for name in training_files:
+                file.write(name + '\n')
+
+            file.write("\nValidation Files:\n")
+            for name in validation_files:
+                file.write(name + '\n')
+
+            file.write("\nTest Files:\n")
+            for name in test_files:
+                file.write(name + '\n')
+
+        print(f"File names have been successfully saved to {output_file}.")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+# Specify the paths to the folders and output file
+training_folder_path = "/Users/duan_j/Applications/alc/tests/yolo/train/labels"
+validation_folder_path = "/Users/duan_j/Applications/alc/tests/yolo/val/labels"
+test_folder_path = "/Users/duan_j/Applications/alc/tests/yolo/test/labels"
+output_file_path = "/Users/duan_j/Applications/alc/tests/yolo/runs/aare-test/train/img_record.txt"
+
+# Save the file names to the record
+save_file_names_to_record(training_folder_path, validation_folder_path, test_folder_path, output_file_path)
\ No newline at end of file
diff --git a/src/tools/generate_splits.py b/src/tools/generate_splits.py
new file mode 100644
index 0000000..b35e19e
--- /dev/null
+++ b/src/tools/generate_splits.py
@@ -0,0 +1,79 @@
+"""
+generate_splits_txt.py
+
+Generates a splits.txt file listing all label filenames across train/test/val
+splits in the format:
+
+    split/labels/<filename>.txt
+
+One entry per line, ordered train → test → val. This file can be consumed by a
+shell script to recreate the directory structure and create symlinks, e.g.:
+
+    while IFS= read -r entry; do
+      split=$(echo "$entry" | cut -d/ -f1)
+      fname=$(basename "$entry" .txt)
+      ln -s "$SOURCE/labels/$fname.txt" "splits/$split/labels/$fname.txt"
+      ln -s "$SOURCE/images/$fname.jpg" "splits/$split/images/$fname.jpg"
+    done < splits.txt
+"""
+
+import argparse
+from pathlib import Path
+
+
+SPLITS = ["train", "test", "val"]
+
+
+def generate_splits_txt(splits_dir: Path, output_file: Path) -> None:
+    entries = []
+    counts = {}
+
+    for split in SPLITS:
+        labels_dir = splits_dir / split / "labels"
+        if not labels_dir.is_dir():
+            print(f"Warning: {labels_dir} not found, skipping.")
+            counts[split] = 0
+            continue
+
+        files = sorted(labels_dir.glob("*.txt"))
+        for f in files:
+            entries.append(f"{split}/labels/{f.name}")
+        counts[split] = len(files)
+
+    output_file.write_text("\n".join(entries) + "\n")
+
+    total = sum(counts.values())
+    print(f"Written {total} entries to {output_file}")
+    for split in SPLITS:
+        print(f"  {split}: {counts.get(split, 0)}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate splits.txt from a splits directory.")
+    parser.add_argument(
+        "--input_dir",
+        type=Path,
+        help="Path to the input folder containing train/val/test splits",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=Path,
+        default=Path(__file__).resolve().parents[2] / "data" / "splits",
+        help="Path to the output folder (default: <project_root>/data/splits)",
+    )
+    args = parser.parse_args()
+
+    splits_dir = args.input_dir
+    output_dir = args.output_dir
+
+    if not splits_dir.is_dir():
+        raise SystemExit(f"Error: input folder not found: {splits_dir}")
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_file = output_dir / f"{splits_dir.name}_split.txt"
+
+    generate_splits_txt(splits_dir, output_file)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tools/movetext_images.py b/src/tools/movetext_images.py
new file mode 100644
index 0000000..a6fcd63
--- /dev/null
+++ b/src/tools/movetext_images.py
@@ -0,0 +1,29 @@
+import os
+import shutil
+
+
+def get_filename_without_ext(filepath):
+    return os.path.splitext(os.path.basename(filepath))[0]
+
+text_source = "/Users/duan_j/Applications/alc/tests/sample_img/20240109/only_images/C1/labels"
+image_source = "/Users/duan_j/Applications/alc/tests/sample_img/20240109/only_images/C1"
+
+# Get all text files and their base names
+text_files = {get_filename_without_ext(f): f
+              for f in os.listdir(text_source)
+              if f.endswith('.txt')}
+
+# Move matching pairs
+for file in os.listdir(image_source):
+    if file.lower().endswith(('.jpg', '.jpeg')):
+        base_name = get_filename_without_ext(file)
+        if base_name in text_files:
+            # Move image
+            image_source_path = os.path.join(image_source, file)
+            image_dest_path = os.path.join("/Users/duan_j/Applications/alc/tests/yolo/train/images", file)
+            shutil.move(image_source_path, image_dest_path)
+
+            # Move corresponding text file
+            text_source_path = os.path.join(text_source, text_files[base_name])
+            text_dest_path = os.path.join("/Users/duan_j/Applications/alc/tests/yolo/train/labels", text_files[base_name])
+            shutil.move(text_source_path, text_dest_path)
diff --git a/src/tools/pt2engine.py b/src/tools/pt2engine.py
new file mode 100644
index 0000000..9c88fbc
--- /dev/null
+++ b/src/tools/pt2engine.py
@@ -0,0 +1,25 @@
+from ultralytics import YOLO
+import torch
+import os
+from ultralytics import settings
+settings.update({"hub": False})
+print("CUDA available:", torch.cuda.is_available())
+if torch.cuda.is_available():
+    print("GPU:", torch.cuda.get_device_name(0))
+
+try:
+    import onnx
+    print("ONNX:", onnx.__version__)
+except Exception as e:
+    print("ONNX import failed:", e)
+
+try:
+    import tensorrt as trt
+    print("TensorRT:", trt.__version__)
+except Exception as e:
+    print("TensorRT import failed:", e)
+# Load model
+model = YOLO(f"/opt/ml_dir/AareLC/models/best_yolo26n-seg-overlap-false_2026-04-12.pt")
+
+# Export the model to TensorRT engine format
+model.export(format="engine", imgsz=640, simplify=False, device=0, conf=0.25, iou=0.45)  # Adjust imgsz if needed
\ No newline at end of file
diff --git a/src/training/prepare_yolo_seg_dataset_20260306_095355.py b/src/training/prepare_yolo_seg_dataset_20260306_095355.py
new file mode 100755
index 0000000..ee3c988
--- /dev/null
+++ b/src/training/prepare_yolo_seg_dataset_20260306_095355.py
@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""
+Auto-generated by AareLC ML Studio on 2026-03-06T09:54:02.
+Prepare YOLO segmentation train/val folders from source images/labels.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import random
+import shutil
+from collections import Counter
+from datetime import datetime, timezone
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent
+DEFAULT_SOURCE = 'data/img_sets/seg_training'
+DEFAULT_DESTINATION = 'data/img_sets/2026-03-06_seg_training_yolo26n-seg'
+DEFAULT_TRAIN_RATIO = 0.85
+DEFAULT_SEED = 42
+DEFAULT_MODEL_SPEC = 'yolo26n-seg.pt'
+CLASS_NAMES = ["loop_all", "pin", "crystal", "loop_face", "ice", "needle"]
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff", ".webp"}
+
+
+def resolve_user_path(path_value: str) -> Path:
+    p = Path(path_value).expanduser()
+    if not p.is_absolute():
+        p = PROJECT_ROOT / p
+    return p.resolve()
+
+
+def resolve_layout(source_root: Path) -> tuple[Path, Path]:
+    images_dir = source_root / "images"
+    labels_dir = source_root / "labels"
+    if images_dir.is_dir() and labels_dir.is_dir():
+        return images_dir, labels_dir
+    return source_root, source_root
+
+
+def collect_pairs(source_root: Path):
+    images_dir, labels_dir = resolve_layout(source_root)
+    image_files = []
+    for ext in IMAGE_EXTENSIONS:
+        image_files.extend(images_dir.rglob(f"*{ext}"))
+        image_files.extend(images_dir.rglob(f"*{ext.upper()}"))
+    image_files = sorted(set(image_files))
+
+    pairs = []
+    missing_labels = []
+    used_labels = set()
+
+    for img_path in image_files:
+        rel_image = img_path.relative_to(images_dir)
+        label_candidate = (labels_dir / rel_image).with_suffix(".txt")
+        if not label_candidate.exists():
+            flat_fallback = labels_dir / f"{img_path.stem}.txt"
+            if flat_fallback.exists():
+                label_candidate = flat_fallback
+
+        if label_candidate.exists():
+            pairs.append((img_path, label_candidate, rel_image))
+            used_labels.add(label_candidate.resolve())
+        else:
+            missing_labels.append(str(img_path))
+
+    all_labels = {p.resolve() for p in labels_dir.rglob("*.txt")}
+    orphan_labels = sorted(str(p) for p in (all_labels - used_labels))
+    return pairs, missing_labels, orphan_labels, images_dir, labels_dir
+
+
+def split_pairs(pairs, train_ratio: float, seed: int):
+    ordered = list(pairs)
+    rng = random.Random(seed)
+    rng.shuffle(ordered)
+    total = len(ordered)
+    if total == 0:
+        return [], []
+
+    train_count = int(total * train_ratio)
+    if total > 1:
+        train_count = max(1, min(total - 1, train_count))
+    else:
+        train_count = 1
+    return ordered[:train_count], ordered[train_count:]
+
+
+def parse_class_ids(label_path: Path):
+    class_ids = []
+    for line in label_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        head = line.split()[0]
+        try:
+            class_ids.append(int(float(head)))
+        except ValueError:
+            continue
+    return class_ids
+
+
+def write_dataset_yaml(destination_root: Path):
+    lines = [
+        f"path: {destination_root.resolve()}",
+        "train: train/images",
+        "val: val/images",
+        "",
+        "names:",
+    ]
+    for class_id, class_name in enumerate(CLASS_NAMES):
+        lines.append(f"  {class_id}: {class_name}")
+    dataset_yaml = destination_root / "dataset.yaml"
+    dataset_yaml.write_text("\n".join(lines) + "\n", encoding="utf-8")
+    return dataset_yaml
+
+
+def write_train_script(destination_root: Path, dataset_yaml: Path, model_spec: str):
+    train_script = destination_root / "train_yolo_seg.py"
+    script_text = f"""#!/usr/bin/env python3
+from pathlib import Path
+from ultralytics import YOLO
+import argparse
+
+
+def resolve_model(model_value: str) -> str:
+    p = Path(model_value).expanduser()
+    if p.is_absolute():
+        return str(p)
+    local_candidate = Path(__file__).resolve().parent.parent / p
+    if local_candidate.exists():
+        return str(local_candidate.resolve())
+    return model_value
+
+
+def main():
+    parser = argparse.ArgumentParser(description=\"Train YOLO segmentation model\")
+    parser.add_argument(\"--model\", default='yolo26n-seg.pt', help=\"Local model path or Ultralytics model name\")
+    parser.add_argument(\"--data\", default=None, help=\"Dataset YAML path (default: ./dataset.yaml next to this script)\")
+    parser.add_argument(\"--epochs\", type=int, default=100)
+    parser.add_argument(\"--imgsz\", type=int, default=640)
+    parser.add_argument(\"--batch\", type=int, default=16)
+    parser.add_argument(\"--device\", default=\"0\")
+    parser.add_argument(\"--project\", default=\"runs/segment\")
+    parser.add_argument(\"--name\", default=\"aarelc_seg_train\")
+    args = parser.parse_args()
+
+    model_value = resolve_model(args.model)
+    if args.data:
+        data_yaml = Path(args.data).expanduser().resolve()
+    else:
+        data_yaml = Path(__file__).resolve().parent / "dataset.yaml"
+    if not data_yaml.exists():
+        raise FileNotFoundError(
+            f"Dataset YAML not found: {data_yaml}. "
+            "Run prepare_yolo_seg_dataset first or pass --data /path/to/dataset.yaml."
+        )
+
+    model = YOLO(model_value)
+    model.train(
+        data=str(data_yaml),
+        epochs=args.epochs,
+        imgsz=args.imgsz,
+        batch=args.batch,
+        device=args.device,
+        project=args.project,
+        name=args.name,
+    )
+
+
+if __name__ == \"__main__\":
+    main()
+"""
+    train_script.write_text(script_text, encoding="utf-8")
+    train_script.chmod(0o755)
+    return train_script
+
+
+def copy_pairs(split_name: str, rows, destination_root: Path, dry_run: bool):
+    log_rows = []
+    for image_path, label_path, rel_image in rows:
+        dst_image = destination_root / split_name / "images" / rel_image
+        dst_label = destination_root / split_name / "labels" / rel_image.with_suffix(".txt")
+        log_rows.append(
+            {
+                "source_image": str(image_path),
+                "source_label": str(label_path),
+                "dest_image": str(dst_image),
+                "dest_label": str(dst_label),
+            }
+        )
+        if dry_run:
+            continue
+        dst_image.parent.mkdir(parents=True, exist_ok=True)
+        dst_label.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(image_path, dst_image)
+        shutil.copy2(label_path, dst_label)
+    return log_rows
+
+
+def build_text_log(metadata: dict) -> str:
+    lines = [
+        "YOLO Dataset Preparation Log",
+        "=" * 32,
+        f"Prepared at (UTC): {metadata['prepared_at_utc']}",
+        f"Source root: {metadata['source_root']}",
+        f"Destination root: {metadata['destination_root']}",
+        f"Model spec: {metadata['model_spec']}",
+        f"Training script: {metadata['train_script']}",
+        f"Dry run: {metadata['dry_run']}",
+        f"Train ratio: {metadata['train_ratio']}",
+        f"Seed: {metadata['seed']}",
+        "",
+        f"Total image+label pairs: {metadata['totals']['pairs']}",
+        f"Train pairs: {metadata['totals']['train_pairs']}",
+        f"Validation pairs: {metadata['totals']['val_pairs']}",
+        f"Missing labels: {metadata['totals']['missing_labels']}",
+        f"Orphan labels: {metadata['totals']['orphan_labels']}",
+        "",
+        "Class counts:",
+    ]
+    for class_id, count in sorted(metadata["class_counts"].items(), key=lambda x: int(x[0])):
+        class_name = metadata["class_names"].get(class_id, f"class_{class_id}")
+        lines.append(f"  class {class_id} ({class_name}): {count}")
+
+    lines.append("")
+    lines.append("Train files:")
+    for row in metadata["train_files"]:
+        lines.append(f"  IMG {row['source_image']} -> {row['dest_image']}")
+        lines.append(f"  LBL {row['source_label']} -> {row['dest_label']}")
+
+    lines.append("")
+    lines.append("Validation files:")
+    for row in metadata["val_files"]:
+        lines.append(f"  IMG {row['source_image']} -> {row['dest_image']}")
+        lines.append(f"  LBL {row['source_label']} -> {row['dest_label']}")
+
+    if metadata["missing_labels"]:
+        lines.append("")
+        lines.append("Images missing labels:")
+        for path in metadata["missing_labels"]:
+            lines.append(f"  {path}")
+
+    if metadata["orphan_labels"]:
+        lines.append("")
+        lines.append("Labels without matching images:")
+        for path in metadata["orphan_labels"]:
+            lines.append(f"  {path}")
+
+    return "\n".join(lines) + "\n"
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Prepare YOLO segmentation train/val dataset.")
+    parser.add_argument("--source", default=DEFAULT_SOURCE, help="Source folder with images/labels")
+    parser.add_argument("--destination", default=DEFAULT_DESTINATION, help="Destination dataset root")
+    parser.add_argument("--train-ratio", type=float, default=DEFAULT_TRAIN_RATIO, help="Train split ratio")
+    parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="Random seed")
+    parser.add_argument("--dry-run", action="store_true", help="Preview only, do not copy files")
+    args = parser.parse_args()
+
+    if not (0.0 < args.train_ratio < 1.0):
+        raise ValueError("train-ratio must be between 0 and 1")
+
+    source_root = resolve_user_path(args.source)
+    destination_root = resolve_user_path(args.destination)
+    if not source_root.exists():
+        raise FileNotFoundError(f"Source folder not found: {source_root}")
+
+    pairs, missing_labels, orphan_labels, images_dir, labels_dir = collect_pairs(source_root)
+    train_pairs, val_pairs = split_pairs(pairs, args.train_ratio, args.seed)
+
+    class_counts = Counter()
+    for _, label_path, _ in pairs:
+        class_counts.update(parse_class_ids(label_path))
+
+    if not args.dry_run:
+        for split in ("train", "val"):
+            (destination_root / split / "images").mkdir(parents=True, exist_ok=True)
+            (destination_root / split / "labels").mkdir(parents=True, exist_ok=True)
+
+    train_rows = copy_pairs("train", train_pairs, destination_root, args.dry_run)
+    val_rows = copy_pairs("val", val_pairs, destination_root, args.dry_run)
+
+    dataset_yaml = None
+    train_script_path = None
+    if not args.dry_run:
+        dataset_yaml = write_dataset_yaml(destination_root)
+        train_script_path = write_train_script(destination_root, dataset_yaml, DEFAULT_MODEL_SPEC)
+
+    now_utc = datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+    class_name_map = {str(i): name for i, name in enumerate(CLASS_NAMES)}
+    for class_id in class_counts:
+        class_name_map.setdefault(str(class_id), f"class_{class_id}")
+
+    metadata = {
+        "prepared_at_utc": now_utc,
+        "source_root": str(source_root),
+        "destination_root": str(destination_root),
+        "images_dir_resolved": str(images_dir),
+        "labels_dir_resolved": str(labels_dir),
+        "dry_run": args.dry_run,
+        "train_ratio": args.train_ratio,
+        "seed": args.seed,
+        "dataset_yaml": str(dataset_yaml) if dataset_yaml else None,
+        "train_script": str(train_script_path) if train_script_path else None,
+        "model_spec": DEFAULT_MODEL_SPEC,
+        "class_names": class_name_map,
+        "class_counts": {str(k): int(v) for k, v in sorted(class_counts.items())},
+        "totals": {
+            "pairs": len(pairs),
+            "train_pairs": len(train_pairs),
+            "val_pairs": len(val_pairs),
+            "missing_labels": len(missing_labels),
+            "orphan_labels": len(orphan_labels),
+        },
+        "train_files": train_rows,
+        "val_files": val_rows,
+        "missing_labels": missing_labels,
+        "orphan_labels": orphan_labels,
+    }
+
+    logs_root = destination_root / "logs"
+    if not args.dry_run:
+        logs_root.mkdir(parents=True, exist_ok=True)
+        stamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+        json_log = logs_root / f"dataset_preparation_{stamp}.json"
+        txt_log = logs_root / f"dataset_preparation_{stamp}.txt"
+        json_log.write_text(json.dumps(metadata, indent=2), encoding="utf-8")
+        txt_log.write_text(build_text_log(metadata), encoding="utf-8")
+        print(f"Wrote logs: {json_log} and {txt_log}")
+        print(f"Wrote training script: {train_script_path}")
+    else:
+        print("Dry run enabled: files were not copied and logs were not written.")
+        print("Summary:")
+        print(json.dumps(metadata["totals"], indent=2))
+
+    print(
+        f"Prepared dataset split from {source_root} -> {destination_root} "
+        f"(train={len(train_pairs)}, val={len(val_pairs)}, total={len(pairs)})."
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/training/prepare_yolo_seg_dataset_20260316_120428.py b/src/training/prepare_yolo_seg_dataset_20260316_120428.py
new file mode 100755
index 0000000..c7658f2
--- /dev/null
+++ b/src/training/prepare_yolo_seg_dataset_20260316_120428.py
@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""
+Auto-generated by AareLC ML Studio on 2026-03-16T12:04:37.
+Prepare YOLO segmentation train/val folders from source images/labels.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import random
+import shutil
+from collections import Counter
+from datetime import datetime, timezone
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent
+DEFAULT_SOURCE = ''
+DEFAULT_DESTINATION = 'AareLC/data/img_sets/seg_training_yolo26_20260316'
+DEFAULT_TRAIN_RATIO = 0.85
+DEFAULT_SEED = 42
+DEFAULT_MODEL_SPEC = 'yolo26l-seg.pt'
+CLASS_NAMES = ["loop_all", "pin", "crystal", "loop_face", "ice", "needle"]
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff", ".webp"}
+
+
+def resolve_user_path(path_value: str) -> Path:
+    p = Path(path_value).expanduser()
+    if not p.is_absolute():
+        p = PROJECT_ROOT / p
+    return p.resolve()
+
+
+def resolve_layout(source_root: Path) -> tuple[Path, Path]:
+    images_dir = source_root / "images"
+    labels_dir = source_root / "labels"
+    if images_dir.is_dir() and labels_dir.is_dir():
+        return images_dir, labels_dir
+    return source_root, source_root
+
+
+def collect_pairs(source_root: Path):
+    images_dir, labels_dir = resolve_layout(source_root)
+    image_files = []
+    for ext in IMAGE_EXTENSIONS:
+        image_files.extend(images_dir.rglob(f"*{ext}"))
+        image_files.extend(images_dir.rglob(f"*{ext.upper()}"))
+    image_files = sorted(set(image_files))
+
+    pairs = []
+    missing_labels = []
+    used_labels = set()
+
+    for img_path in image_files:
+        rel_image = img_path.relative_to(images_dir)
+        label_candidate = (labels_dir / rel_image).with_suffix(".txt")
+        if not label_candidate.exists():
+            flat_fallback = labels_dir / f"{img_path.stem}.txt"
+            if flat_fallback.exists():
+                label_candidate = flat_fallback
+
+        if label_candidate.exists():
+            pairs.append((img_path, label_candidate, rel_image))
+            used_labels.add(label_candidate.resolve())
+        else:
+            missing_labels.append(str(img_path))
+
+    all_labels = {p.resolve() for p in labels_dir.rglob("*.txt")}
+    orphan_labels = sorted(str(p) for p in (all_labels - used_labels))
+    return pairs, missing_labels, orphan_labels, images_dir, labels_dir
+
+
+def split_pairs(pairs, train_ratio: float, seed: int):
+    ordered = list(pairs)
+    rng = random.Random(seed)
+    rng.shuffle(ordered)
+    total = len(ordered)
+    if total == 0:
+        return [], []
+
+    train_count = int(total * train_ratio)
+    if total > 1:
+        train_count = max(1, min(total - 1, train_count))
+    else:
+        train_count = 1
+    return ordered[:train_count], ordered[train_count:]
+
+
+def parse_class_ids(label_path: Path):
+    class_ids = []
+    for line in label_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        head = line.split()[0]
+        try:
+            class_ids.append(int(float(head)))
+        except ValueError:
+            continue
+    return class_ids
+
+
+def write_dataset_yaml(destination_root: Path):
+    lines = [
+        f"path: {destination_root.resolve()}",
+        "train: train/images",
+        "val: val/images",
+        "",
+        "names:",
+    ]
+    for class_id, class_name in enumerate(CLASS_NAMES):
+        lines.append(f"  {class_id}: {class_name}")
+    dataset_yaml = destination_root / "dataset.yaml"
+    dataset_yaml.write_text("\n".join(lines) + "\n", encoding="utf-8")
+    return dataset_yaml
+
+
+def write_train_script(destination_root: Path, dataset_yaml: Path, model_spec: str):
+    train_script = destination_root / "train_yolo_seg.py"
+    script_text = f"""#!/usr/bin/env python3
+from pathlib import Path
+from ultralytics import YOLO
+import argparse
+
+
+def resolve_model(model_value: str) -> str:
+    p = Path(model_value).expanduser()
+    if p.is_absolute():
+        return str(p)
+    local_candidate = Path(__file__).resolve().parent.parent / p
+    if local_candidate.exists():
+        return str(local_candidate.resolve())
+    return model_value
+
+
+def main():
+    parser = argparse.ArgumentParser(description=\"Train YOLO segmentation model\")
+    parser.add_argument(\"--model\", default='yolo26l-seg.pt', help=\"Local model path or Ultralytics model name\")
+    parser.add_argument(\"--data\", default=None, help=\"Dataset YAML path (default: ./dataset.yaml next to this script)\")
+    parser.add_argument(\"--epochs\", type=int, default=100)
+    parser.add_argument(\"--imgsz\", type=int, default=640)
+    parser.add_argument(\"--batch\", type=int, default=16)
+    parser.add_argument(\"--device\", default=\"0\")
+    parser.add_argument(\"--project\", default=\"runs/segment\")
+    parser.add_argument(\"--name\", default=\"aarelc_seg_train\")
+    args = parser.parse_args()
+
+    model_value = resolve_model(args.model)
+    if args.data:
+        dataset_yaml = Path(args.data).expanduser().resolve()
+    else:
+        dataset_yaml = Path(__file__).resolve().parent / "dataset.yaml"
+    if not dataset_yaml.exists():
+        raise FileNotFoundError(
+            f"Dataset YAML not found: {dataset_yaml}. "
+            "Run prepare_yolo_seg_dataset first or pass --data /path/to/dataset.yaml."
+        )
+
+    model = YOLO(model_value)
+    model.train(
+        data=str(dataset_yaml),
+        epochs=args.epochs,
+        imgsz=args.imgsz,
+        batch=args.batch,
+        device=args.device,
+        project=args.project,
+        name=args.name,
+    )
+
+
+if __name__ == \"__main__\":
+    main()
+"""
+    train_script.write_text(script_text, encoding="utf-8")
+    train_script.chmod(0o755)
+    return train_script
+
+
+def copy_pairs(split_name: str, rows, destination_root: Path, dry_run: bool):
+    log_rows = []
+    for image_path, label_path, rel_image in rows:
+        dst_image = destination_root / split_name / "images" / rel_image
+        dst_label = destination_root / split_name / "labels" / rel_image.with_suffix(".txt")
+        log_rows.append(
+            {
+                "source_image": str(image_path),
+                "source_label": str(label_path),
+                "dest_image": str(dst_image),
+                "dest_label": str(dst_label),
+            }
+        )
+        if dry_run:
+            continue
+        dst_image.parent.mkdir(parents=True, exist_ok=True)
+        dst_label.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(image_path, dst_image)
+        shutil.copy2(label_path, dst_label)
+    return log_rows
+
+
+def build_text_log(metadata: dict) -> str:
+    lines = [
+        "YOLO Dataset Preparation Log",
+        "=" * 32,
+        f"Prepared at (UTC): {metadata['prepared_at_utc']}",
+        f"Source root: {metadata['source_root']}",
+        f"Destination root: {metadata['destination_root']}",
+        f"Model spec: {metadata['model_spec']}",
+        f"Training script: {metadata['train_script']}",
+        f"Dry run: {metadata['dry_run']}",
+        f"Train ratio: {metadata['train_ratio']}",
+        f"Seed: {metadata['seed']}",
+        "",
+        f"Total image+label pairs: {metadata['totals']['pairs']}",
+        f"Train pairs: {metadata['totals']['train_pairs']}",
+        f"Validation pairs: {metadata['totals']['val_pairs']}",
+        f"Missing labels: {metadata['totals']['missing_labels']}",
+        f"Orphan labels: {metadata['totals']['orphan_labels']}",
+        "",
+        "Class counts:",
+    ]
+    for class_id, count in sorted(metadata["class_counts"].items(), key=lambda x: int(x[0])):
+        class_name = metadata["class_names"].get(class_id, f"class_{class_id}")
+        lines.append(f"  class {class_id} ({class_name}): {count}")
+
+    lines.append("")
+    lines.append("Train files:")
+    for row in metadata["train_files"]:
+        lines.append(f"  IMG {row['source_image']} -> {row['dest_image']}")
+        lines.append(f"  LBL {row['source_label']} -> {row['dest_label']}")
+
+    lines.append("")
+    lines.append("Validation files:")
+    for row in metadata["val_files"]:
+        lines.append(f"  IMG {row['source_image']} -> {row['dest_image']}")
+        lines.append(f"  LBL {row['source_label']} -> {row['dest_label']}")
+
+    if metadata["missing_labels"]:
+        lines.append("")
+        lines.append("Images missing labels:")
+        for path in metadata["missing_labels"]:
+            lines.append(f"  {path}")
+
+    if metadata["orphan_labels"]:
+        lines.append("")
+        lines.append("Labels without matching images:")
+        for path in metadata["orphan_labels"]:
+            lines.append(f"  {path}")
+
+    return "\n".join(lines) + "\n"
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Prepare YOLO segmentation train/val dataset.")
+    parser.add_argument("--source", default=DEFAULT_SOURCE, help="Source folder with images/labels")
+    parser.add_argument("--destination", default=DEFAULT_DESTINATION, help="Destination dataset root")
+    parser.add_argument("--train-ratio", type=float, default=DEFAULT_TRAIN_RATIO, help="Train split ratio")
+    parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="Random seed")
+    parser.add_argument("--dry-run", action="store_true", help="Preview only, do not copy files")
+    args = parser.parse_args()
+
+    if not (0.0 < args.train_ratio < 1.0):
+        raise ValueError("train-ratio must be between 0 and 1")
+
+    source_root = resolve_user_path(args.source)
+    destination_root = resolve_user_path(args.destination)
+    if not source_root.exists():
+        raise FileNotFoundError(f"Source folder not found: {source_root}")
+
+    pairs, missing_labels, orphan_labels, images_dir, labels_dir = collect_pairs(source_root)
+    train_pairs, val_pairs = split_pairs(pairs, args.train_ratio, args.seed)
+
+    class_counts = Counter()
+    for _, label_path, _ in pairs:
+        class_counts.update(parse_class_ids(label_path))
+
+    if not args.dry_run:
+        for split in ("train", "val"):
+            (destination_root / split / "images").mkdir(parents=True, exist_ok=True)
+            (destination_root / split / "labels").mkdir(parents=True, exist_ok=True)
+
+    train_rows = copy_pairs("train", train_pairs, destination_root, args.dry_run)
+    val_rows = copy_pairs("val", val_pairs, destination_root, args.dry_run)
+
+    dataset_yaml = None
+    train_script_path = None
+    if not args.dry_run:
+        dataset_yaml = write_dataset_yaml(destination_root)
+        train_script_path = write_train_script(destination_root, dataset_yaml, DEFAULT_MODEL_SPEC)
+
+    now_utc = datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+    class_name_map = {str(i): name for i, name in enumerate(CLASS_NAMES)}
+    for class_id in class_counts:
+        class_name_map.setdefault(str(class_id), f"class_{class_id}")
+
+    metadata = {
+        "prepared_at_utc": now_utc,
+        "source_root": str(source_root),
+        "destination_root": str(destination_root),
+        "images_dir_resolved": str(images_dir),
+        "labels_dir_resolved": str(labels_dir),
+        "dry_run": args.dry_run,
+        "train_ratio": args.train_ratio,
+        "seed": args.seed,
+        "dataset_yaml": str(dataset_yaml) if dataset_yaml else None,
+        "train_script": str(train_script_path) if train_script_path else None,
+        "model_spec": DEFAULT_MODEL_SPEC,
+        "class_names": class_name_map,
+        "class_counts": {str(k): int(v) for k, v in sorted(class_counts.items())},
+        "totals": {
+            "pairs": len(pairs),
+            "train_pairs": len(train_pairs),
+            "val_pairs": len(val_pairs),
+            "missing_labels": len(missing_labels),
+            "orphan_labels": len(orphan_labels),
+        },
+        "train_files": train_rows,
+        "val_files": val_rows,
+        "missing_labels": missing_labels,
+        "orphan_labels": orphan_labels,
+    }
+
+    logs_root = destination_root / "logs"
+    if not args.dry_run:
+        logs_root.mkdir(parents=True, exist_ok=True)
+        stamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+        json_log = logs_root / f"dataset_preparation_{stamp}.json"
+        txt_log = logs_root / f"dataset_preparation_{stamp}.txt"
+        json_log.write_text(json.dumps(metadata, indent=2), encoding="utf-8")
+        txt_log.write_text(build_text_log(metadata), encoding="utf-8")
+        print(f"Wrote logs: {json_log} and {txt_log}")
+        print(f"Wrote training script: {train_script_path}")
+    else:
+        print("Dry run enabled: files were not copied and logs were not written.")
+        print("Summary:")
+        print(json.dumps(metadata["totals"], indent=2))
+
+    print(
+        f"Prepared dataset split from {source_root} -> {destination_root} "
+        f"(train={len(train_pairs)}, val={len(val_pairs)}, total={len(pairs)})."
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/training/train_yolo_seg.py b/src/training/train_yolo_seg.py
new file mode 100755
index 0000000..bd13efb
--- /dev/null
+++ b/src/training/train_yolo_seg.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+from pathlib import Path
+from ultralytics import YOLO
+import argparse
+
+
+def resolve_model(model_value: str) -> str:
+    p = Path(model_value).expanduser()
+    if p.is_absolute():
+        return str(p)
+    local_candidate = Path(__file__).resolve().parent.parent / p
+    if local_candidate.exists():
+        return str(local_candidate.resolve())
+    return model_value
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Train YOLO segmentation model")
+    parser.add_argument("--model", default='/opt/ml_dir/AareLC/models/yolo26n-seg.pt', help="Local model path or Ultralytics model name")
+    parser.add_argument("--data", default=None, help="Dataset YAML path (default: ./dataset.yaml next to this script)")
+    parser.add_argument("--epochs", type=int, default=400)
+    parser.add_argument("--imgsz", type=int, default=640)
+    parser.add_argument("--batch", type=int, default=16)
+    parser.add_argument("--device", default="0")
+    parser.add_argument("--project", default="runs/segment_yolo26n-seg_20260401")
+    parser.add_argument("--name", default="yolo26n-seg_20260401")
+    args = parser.parse_args()
+
+    model_value = resolve_model(args.model)
+    if args.data:
+        dataset_yaml = Path(args.data).expanduser().resolve()
+    else:
+        dataset_yaml = Path(__file__).resolve().parent / "dataset.yaml"
+    if not dataset_yaml.exists():
+        raise FileNotFoundError(
+            f"Dataset YAML not found: /opt/ml_dir/AareLC/data/img_sets/seg_training/AareLC/data/img_sets/seg_training_yolo26_20260316/dataset.yaml. "
+            "Run prepare_yolo_seg_dataset first or pass --data /path/to/dataset.yaml."
+        )
+
+    model = YOLO(model_value)
+    model.train(
+        data=str(dataset_yaml),
+        patience=50,
+        epochs=args.epochs,
+        imgsz=args.imgsz,
+        overlap_mask=False,
+        batch=args.batch,
+        # zoom/size style augmentation
+        scale=0.7,        # stronger zoom in/out effect (try 0.5~0.9)
+        translate=0.15,   # shift object position
+        mosaic=0.5,       # combines scenes at different effective scales
+        close_mosaic=10,  # disable mosaic near end for stability
+        # optional multi-scale training (image size jitter)
+        #multi_scale=True,
+        # keep your flips/geometry
+        fliplr=0.5,
+        flipud=0.3,
+        shear=5.0,
+        perspective=0.0,
+        #workers=0,
+        #device=0,
+        device=(0,1,2,3),
+        project=args.project,
+        name=args.name,
+        amp=False,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_generate_splits.py b/tests/test_generate_splits.py
new file mode 100644
index 0000000..2dd77c2
--- /dev/null
+++ b/tests/test_generate_splits.py
@@ -0,0 +1,65 @@
+"""Unit tests for src/tools/generate_splits.py"""
+import pytest
+from pathlib import Path
+
+from src.tools.generate_splits import generate_splits_txt, SPLITS
+
+
+@pytest.fixture
+def splits_dir(tmp_path):
+    """Create a minimal splits directory with label files."""
+    for split in ["train", "val"]:
+        label_dir = tmp_path / split / "labels"
+        label_dir.mkdir(parents=True)
+        for i in range(3):
+            (label_dir / f"img_{split}_{i:03d}.txt").write_text("0 0.5 0.5 0.1 0.1\n")
+    # leave "test" absent intentionally
+    return tmp_path
+
+
+class TestGenerateSplitsTxt:
+    def test_creates_output_file(self, splits_dir, tmp_path):
+        out = tmp_path / "output" / "split.txt"
+        out.parent.mkdir()
+        generate_splits_txt(splits_dir, out)
+        assert out.exists()
+
+    def test_entries_have_correct_format(self, splits_dir, tmp_path):
+        out = tmp_path / "split.txt"
+        generate_splits_txt(splits_dir, out)
+        lines = [l for l in out.read_text().splitlines() if l]
+        for line in lines:
+            parts = line.split("/")
+            assert parts[0] in SPLITS, f"unexpected split name in: {line}"
+            assert parts[1] == "labels"
+            assert parts[2].endswith(".txt")
+
+    def test_entry_count_matches_label_files(self, splits_dir, tmp_path):
+        out = tmp_path / "split.txt"
+        generate_splits_txt(splits_dir, out)
+        lines = [l for l in out.read_text().splitlines() if l]
+        # 3 train + 3 val = 6; test was absent
+        assert len(lines) == 6
+
+    def test_train_entries_come_before_val(self, splits_dir, tmp_path):
+        out = tmp_path / "split.txt"
+        generate_splits_txt(splits_dir, out)
+        lines = [l for l in out.read_text().splitlines() if l]
+        splits_seen = [l.split("/")[0] for l in lines]
+        train_indices = [i for i, s in enumerate(splits_seen) if s == "train"]
+        val_indices   = [i for i, s in enumerate(splits_seen) if s == "val"]
+        assert max(train_indices) < min(val_indices)
+
+    def test_missing_split_dir_is_skipped_gracefully(self, tmp_path):
+        # empty directory — no split sub-dirs at all
+        out = tmp_path / "split.txt"
+        generate_splits_txt(tmp_path, out)
+        lines = [l for l in out.read_text().splitlines() if l]
+        assert lines == []
+
+    def test_entries_are_sorted_within_split(self, splits_dir, tmp_path):
+        out = tmp_path / "split.txt"
+        generate_splits_txt(splits_dir, out)
+        lines = [l for l in out.read_text().splitlines() if l]
+        train_lines = [l for l in lines if l.startswith("train/")]
+        assert train_lines == sorted(train_lines)
diff --git a/tests/test_image_utils.py b/tests/test_image_utils.py
new file mode 100644
index 0000000..0239322
--- /dev/null
+++ b/tests/test_image_utils.py
@@ -0,0 +1,63 @@
+"""Unit tests for src/server/image_utils.py"""
+import base64
+import cv2
+import numpy as np
+import pytest
+
+from src.server.image_utils import decode_image, CLASS_COLORS
+
+
+def _make_jpeg_bytes(width=64, height=64) -> bytes:
+    """Create a minimal valid JPEG bytestring."""
+    img = np.zeros((height, width, 3), dtype=np.uint8)
+    img[10:50, 10:50] = (128, 64, 200)  # a coloured rectangle
+    success, buf = cv2.imencode(".jpg", img)
+    assert success
+    return buf.tobytes()
+
+
+class TestDecodeImage:
+    def test_decodes_valid_jpeg_bytes(self):
+        raw = _make_jpeg_bytes()
+        img = decode_image(raw)
+        assert img is not None
+        assert img.ndim == 3
+
+    def test_decodes_from_memoryview(self):
+        raw = _make_jpeg_bytes()
+        img = decode_image(memoryview(raw))
+        assert img is not None
+
+    def test_decodes_base64_encoded_jpeg(self):
+        raw = _make_jpeg_bytes()
+        encoded = base64.b64encode(raw)
+        img = decode_image(encoded)
+        assert img is not None
+
+    def test_returns_none_for_garbage_bytes(self):
+        img = decode_image(b"\x00\x01\x02\x03garbage")
+        assert img is None
+
+    def test_returns_none_for_empty_bytes(self):
+        img = decode_image(b"")
+        assert img is None
+
+    def test_decoded_shape_matches_source(self):
+        raw = _make_jpeg_bytes(width=80, height=60)
+        img = decode_image(raw)
+        assert img is not None
+        # JPEG encoding may have minor dimension rounding; check order
+        assert img.shape[0] == 60   # height
+        assert img.shape[1] == 80   # width
+
+
+class TestClassColors:
+    def test_class_colors_are_bgr_tuples(self):
+        for cls_id, color in CLASS_COLORS.items():
+            assert isinstance(color, tuple)
+            assert len(color) == 3
+            assert all(0 <= c <= 255 for c in color)
+
+    def test_known_class_ids_present(self):
+        assert 0 in CLASS_COLORS
+        assert 1 in CLASS_COLORS
diff --git a/tests/test_inference.py b/tests/test_inference.py
new file mode 100644
index 0000000..959ef5a
--- /dev/null
+++ b/tests/test_inference.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import requests
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Send a test request to inferenceserver /predict/")
+    parser.add_argument("--url", default="http://127.0.0.1:8003/predict/", help="Predict endpoint URL")
+    parser.add_argument("--image", default="image.jpg", help="Path to image file")
+    parser.add_argument("--timeout", type=float, default=10.0, help="HTTP timeout in seconds")
+    parser.add_argument("--conf", type=float, default=None, help="Optional confidence override")
+    parser.add_argument("--scale", type=float, default=None, help="Optional scale override")
+    parser.add_argument("--infer-scale", type=float, default=None, help="Optional infer_scale override")
+    parser.add_argument("--task", choices=["auto", "detect", "segment"], default=None, help="Optional task override")
+    args = parser.parse_args()
+
+    image_path = Path(args.image)
+    if not image_path.exists():
+        print(f"ERROR: image not found: {image_path.resolve()}")
+        return 2
+
+    params: dict[str, float] = {}
+    if args.conf is not None:
+        params["conf"] = float(args.conf)
+    if args.scale is not None:
+        params["scale"] = float(args.scale)
+    if args.infer_scale is not None:
+        params["infer_scale"] = float(args.infer_scale)
+    if args.task is not None:
+        params["task"] = args.task
+
+    print(f"POST {args.url}")
+    print(f"image={image_path.resolve()} timeout={args.timeout}s params={params}")
+
+    try:
+        with image_path.open("rb") as f:
+            response = requests.post(
+                args.url,
+                files={"file": (image_path.name, f, "application/octet-stream")},
+                params=params,
+                timeout=args.timeout,
+            )
+    except requests.exceptions.RequestException as e:
+        print(f"REQUEST FAILED: {e}")
+        print("Hint: keep SSH tunnel open in a separate terminal:")
+        print("  ssh -N -L 8002:x10sa-spark-01:8002 gotthard_g@x10sa-gw")
+        return 1
+
+    print(f"status={response.status_code}")
+    print("response headers:", dict(response.headers))
+
+    try:
+        print("json:", response.json())
+    except ValueError:
+        print("non-json body:")
+        print(response.text[:2000])
+
+    return 0 if response.ok else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tests/test_target_point.py b/tests/test_target_point.py
new file mode 100644
index 0000000..1bb3707
--- /dev/null
+++ b/tests/test_target_point.py
@@ -0,0 +1,139 @@
+"""Unit tests for src/server/target_point.py"""
+import pytest
+from src.server.target_point import (
+    _poly_to_abs,
+    _shape_center,
+    _pin_left_midpoint,
+    _pick_best,
+    compute_target_point,
+)
+
+
+# ---------------------------------------------------------------------------
+# _poly_to_abs
+# ---------------------------------------------------------------------------
+
+class TestPolyToAbs:
+    def test_none_poly_returns_none(self):
+        assert _poly_to_abs(None, 10, 20) is None
+
+    def test_translates_relative_to_absolute(self):
+        poly = [[1, 2], [3, 4], [5, 6]]
+        result = _poly_to_abs(poly, 10.0, 20.0)
+        assert result is not None
+        assert result[0, 0] == pytest.approx(11.0)
+        assert result[0, 1] == pytest.approx(22.0)
+        assert result[2, 0] == pytest.approx(15.0)
+
+    def test_rejects_fewer_than_3_points(self):
+        poly = [[1, 2], [3, 4]]
+        assert _poly_to_abs(poly, 0, 0) is None
+
+    def test_rejects_wrong_shape(self):
+        poly = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+        assert _poly_to_abs(poly, 0, 0) is None
+
+
+# ---------------------------------------------------------------------------
+# _shape_center
+# ---------------------------------------------------------------------------
+
+class TestShapeCenter:
+    def test_falls_back_to_bbox_center_with_no_poly(self):
+        det = {"x1": 0.0, "y1": 0.0, "x2": 100.0, "y2": 100.0}
+        cx, cy = _shape_center(det)
+        assert cx == pytest.approx(50.0)
+        assert cy == pytest.approx(50.0)
+
+    def test_uses_polygon_centroid_when_poly_present(self):
+        # Unit square at origin: centroid == (0.5, 0.5) + offset (10, 20) = (10.5, 20.5)
+        poly = [[0, 0], [1, 0], [1, 1], [0, 1]]
+        det = {"x1": 10.0, "y1": 20.0, "x2": 11.0, "y2": 21.0, "poly": poly}
+        cx, cy = _shape_center(det)
+        assert cx == pytest.approx(10.5, abs=0.1)
+        assert cy == pytest.approx(20.5, abs=0.1)
+
+    def test_returns_none_on_missing_coords(self):
+        assert _shape_center({}) == (0.0, 0.0)
+
+
+# ---------------------------------------------------------------------------
+# _pin_left_midpoint
+# ---------------------------------------------------------------------------
+
+class TestPinLeftMidpoint:
+    def test_returns_x1_and_vertical_midpoint(self):
+        det = {"x1": 5.0, "y1": 10.0, "x2": 50.0, "y2": 30.0}
+        x, y = _pin_left_midpoint(det)
+        assert x == pytest.approx(5.0)
+        assert y == pytest.approx(20.0)
+
+    def test_returns_none_on_bad_coords(self):
+        assert _pin_left_midpoint({"x1": "bad", "y1": 0, "y2": 10}) is None
+
+
+# ---------------------------------------------------------------------------
+# _pick_best
+# ---------------------------------------------------------------------------
+
+class TestPickBest:
+    def test_returns_none_when_no_matching_label(self):
+        dets = [{"label": "pin", "conf": 0.9}]
+        assert _pick_best(dets, "crystal") is None
+
+    def test_returns_highest_confidence_detection(self):
+        dets = [
+            {"label": "crystal", "conf": 0.5},
+            {"label": "crystal", "conf": 0.9},
+            {"label": "crystal", "conf": 0.3},
+        ]
+        best = _pick_best(dets, "crystal")
+        assert best["conf"] == pytest.approx(0.9)
+
+    def test_returns_none_for_empty_list(self):
+        assert _pick_best([], "crystal") is None
+
+
+# ---------------------------------------------------------------------------
+# compute_target_point — priority logic
+# ---------------------------------------------------------------------------
+
+class TestComputeTargetPoint:
+    def test_returns_none_for_empty_dets(self):
+        assert compute_target_point([]) is None
+
+    def test_crystal_takes_priority_over_loop_face(self):
+        dets = [
+            {"label": "loop_face", "conf": 0.99, "x1": 0, "y1": 0, "x2": 200, "y2": 200},
+            {"label": "crystal",   "conf": 0.5,  "x1": 10, "y1": 10, "x2": 50, "y2": 50},
+        ]
+        result = compute_target_point(dets)
+        assert result is not None
+        assert result["source"] == "crystal_center"
+
+    def test_loop_face_takes_priority_over_loop_all(self):
+        dets = [
+            {"label": "loop_all",  "conf": 0.99, "x1": 0,  "y1": 0,  "x2": 200, "y2": 200},
+            {"label": "loop_face", "conf": 0.5,  "x1": 10, "y1": 10, "x2": 50,  "y2": 50},
+        ]
+        result = compute_target_point(dets)
+        assert result is not None
+        assert result["source"] == "loop_face_center"
+
+    def test_pin_used_as_last_resort(self):
+        dets = [{"label": "pin", "conf": 0.8, "x1": 20.0, "y1": 10.0, "x2": 80.0, "y2": 30.0}]
+        result = compute_target_point(dets)
+        assert result is not None
+        assert result["source"] == "pin_left_mid"
+        assert result["x"] == 20
+        assert result["y"] == 20
+
+    def test_output_contains_integer_coordinates(self):
+        dets = [{"label": "pin", "conf": 0.8, "x1": 10.7, "y1": 5.3, "x2": 60.0, "y2": 25.1}]
+        result = compute_target_point(dets)
+        assert isinstance(result["x"], int)
+        assert isinstance(result["y"], int)
+
+    def test_unknown_labels_return_none(self):
+        dets = [{"label": "unknown_object", "conf": 0.99, "x1": 0, "y1": 0, "x2": 100, "y2": 100}]
+        assert compute_target_point(dets) is None