pyTrainSeg/test_dask_framework.ipynb

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from filter_functions import image_filter

Dashboard at http://127.0.0.1:8787/status

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from training_functions import train_segmentation

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# import dask
# import xarray as xr
import numpy as np
import  matplotlib.pyplot as plt
import os
from ipywidgets import Image
from ipywidgets import ColorPicker, IntSlider, link, AppLayout, HBox
from ipycanvas import  hold_canvas,  MultiCanvas #RoughCanvas,Canvas,
import imageio

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path = '/home/fische_r/NAS/testing/Jeremy_tomo/tomodata.nc'
# path = r"C:\Zwischenlager\tomodata.nc"

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IF = image_filter(data_path=path)

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IF.open_raw_data()

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# IF.data

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IF.prepare()

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IF.stack_features()

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# IF.feature_stack

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IF.make_xarray_nc()

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# IF.result

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# IF.compute()

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# IF.feature_stack.persist()

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# test = IF.result['feature_stack'].sel(x=0,time=0).data

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# test

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# training_path = r"C:\Zwischenlager\Jeremy_tomo"
training_path = '/home/fische_r/NAS/testing/Jeremy_tomo/test2'
if not os.path.exists(training_path):
    os.mkdir(training_path)

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TS = train_segmentation(training_path=training_path)

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TS.import_lazy_feature_stack(IF.result)

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TS.suggest_training_set()

You could try  x = 153  and  z = 210

iterative training

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def on_mouse_down(x, y):
    global drawing
    global position
    global shape
    drawing = True
    position = (x, y)
    shape = [position]

def on_mouse_move(x, y):
    global drawing
    global position
    global shape
    if not drawing:
        return
    with hold_canvas():
        canvas.stroke_line(position[0], position[1], x, y)
        position = (x, y)
    shape.append(position)

def on_mouse_up(x, y):
    global drawing
    global positiondu
    global shape
    drawing = False
    with hold_canvas():
        canvas.stroke_line(position[0], position[1], x, y)
        canvas.fill_polygon(shape)
    shape = []

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c1 = 'y'
p1 = 10
c2 = 'time'
p2 = 0

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TS.load_training_set(c1, p1, c2, p2)

existing label set loaded

/mnt/nas_Uwrite/fische_r/lib/pytrainseg/training_functions.py:176: RuntimeWarning: invalid value encountered in divide
  diff = diff/diff.max()*255

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# TS.interface()

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# TS.current_im8 = TS.current_im8.compute()

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alpha = 0.15
im8 = TS.current_im8
resultim = TS.current_result
width = im8.shape[1]
height = im8.shape[0]
Mcanvas = MultiCanvas(4, width=width, height=height)
background = Mcanvas[0]
resultdisplay = Mcanvas[2]
truthdisplay = Mcanvas[1]
canvas = Mcanvas[3]
canvas.sync_image_data = True
drawing = False
position = None
shape = []
image_data = np.stack((im8, im8, im8), axis=2)
background.put_image_data(image_data, 0, 0)
resultdisplay.global_alpha = alpha
if np.any(resultim>0):
    result_data = np.stack((255*(resultim==0), 255*(resultim==1), 255*(resultim==2)), axis=2)
else:
    result_data = np.stack((0*resultim, 0*resultim, 0*resultim), axis=2)
resultdisplay.put_image_data(result_data, 0, 0)
canvas.on_mouse_down(on_mouse_down)
canvas.on_mouse_move(on_mouse_move)
canvas.on_mouse_up(on_mouse_up)
picker = ColorPicker(description="Color:", value="#ff0000")
slidealpha = IntSlider(description="Result overlay", value=0.15)
link((picker, "value"), (canvas, "stroke_style"))
link((picker, "value"), (canvas, "fill_style"))
HBox((Mcanvas, picker, slidealpha))

HBox(children=(MultiCanvas(height=200, width=233), ColorPicker(value='#ff0000', description='Color:'), IntSlid…

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label_set = canvas.get_image_data()

TS.current_truth[label_set[:,:,0]>0] = 1
TS.current_truth[label_set[:,:,1]>0] = 2
TS.current_truth[label_set[:,:,2]>0] = 4

imageio.imsave(TS.current_truthpath, TS.current_truth)

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TS.train_slice()

training and classifying

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