leonarski_f
f199f23c54
Wherever the correction is set for the whole run its curve is the frame's peak-excluded ring background (the adaptive spot finder's, whole frame) and the ring's clip follows it - as stills have done since integ-pal - now rotation too. One empty-aperture measurement (BackgroundAperture) decides it for both workflows: - The apertures' corrected difference is now taken as the integrator applies the correction: the clip follows the curve (each ring pixel's threshold raised by the curve's rise from the centre, in whole counts) as well as the kernel correction. Before, the tally clipped flat, so it measured a correction the engine never applies. - Rotation measures the stencils the spot width can give it (r1 = 4, 5, 6 with the ring-area rule, RotationStencil) and decides on the one it gets, and separately on the pre-adaptive stencil the geometry pre-pass and the starvation guard return to. - Stills: on wherever the corrected ring predicts the empty disk better (unchanged rule). Rotation: the frame's curve on every image only where it takes >= 10% of the apertures' mean square error away (ROTATION_RADIAL_MIN_GAIN); otherwise the existing per-image ice gate with the reflections' curve is left exactly as it was. Why a margin on rotation only: a still has no other correction, a sweep has one. Measured over 18 rotation sets (forced on every image): photon-counting sweeps gain 0.1-5% on the apertures (z 3-4) and lose ISa/anomalous signal when corrected (insulin, lysozyme, cytochrome c; 9yzk R-free +0.009 at 1.2%), as the earlier stills-rule trial found; the home-source CCD and smooth-ice runs gain 13-31%. Result (CPU A/B vs 87684012f, 18 rotation sets): 12 .hkl byte-identical (every photon-counting control without ice), 5uth R-free 0.210 -> 0.201 / ISa 10.4 -> 10.8, the ice set neutral; stills: lcls242_s turns the correction on (the clip-following tally), R-free 0.2399 -> 0.2406. No integration kernel changed; the tally is host code. Tests: BackgroundAperture_CorrectedClipFollowsTheCurve, RadialHelps margins. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu (cherry picked from commit dd3f3f2da39f5338e7dda1874bbdd535229bce98)
573 lines
32 KiB
C++
573 lines
32 KiB
C++
// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#include "MXAnalysisWithoutFPGA.h"
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#include <algorithm>
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#include <cmath>
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#include <spdlog/spdlog.h>
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#include "spot_finding/StrongPixelSet.h"
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#include "../compression/JFJochDecompress.h"
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#include "../common/CUDAWrapper.h"
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#include "spot_finding/SpotUtils.h"
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#include "bragg_prediction/BraggPredictionFactory.h"
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#include "image_preprocessing/ImagePreprocessorCPU.h"
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#include "azint/AzIntEngineCPU.h"
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#include "roi/ROIIntegrationCPU.h"
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#include "spot_finding/ImageSpotFinderCPU.h"
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#include "spot_finding/AdaptiveSpotFinderCPU.h"
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#include "bragg_integration/BraggIntegrationEngineCPU.h"
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#ifdef __APPLE__
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#include "bragg_integration/BraggIntegrationEngineMetal.h"
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#endif
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#ifdef JFJOCH_USE_CUDA
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#include "azint/AzIntEngineGPU.h"
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#include "roi/ROIIntegrationGPU.h"
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#include "spot_finding/ImageSpotFinderGPU.h"
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#include "spot_finding/AdaptiveSpotFinderGPU.h"
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#include "image_preprocessing/ImagePreprocessorGPU.h"
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#include "image_preprocessing/ImagePreprocessorBufferGPU.h"
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#include "bragg_integration/BraggIntegrationEngineGPU.h"
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#include "../common/CUDAWrapper.h"
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#endif
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MXAnalysisWithoutFPGA::MXAnalysisWithoutFPGA(const DiffractionExperiment &in_experiment,
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const AzimuthalIntegrationMapping &in_integration,
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const PixelMask &in_mask,
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IndexAndRefine &in_indexer,
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bool in_enable_fused_adaptive_gpu)
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: experiment(in_experiment),
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integration(in_integration),
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enable_fused_adaptive_gpu(in_enable_fused_adaptive_gpu),
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npixels(experiment.GetPixelsNum()),
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xpixels(experiment.GetXPixelsNum()),
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indexer(in_indexer),
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prediction(CreateBraggPrediction(experiment.IsRotationIndexing())),
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mask(in_mask),
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mask_high_res(-1),
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mask_low_res(-1) {
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#ifdef JFJOCH_USE_CUDA
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if (get_gpu_count() == 0) {
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#endif
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preprocessor_buffer = std::make_unique<ImagePreprocessorBuffer>(experiment.GetPixelsNum());
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auto cpu_preprocessor = std::make_unique<ImagePreprocessorCPU>(in_experiment, in_mask);
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preprocessor_cpu = cpu_preprocessor.get();
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preprocessor = std::move(cpu_preprocessor);
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#ifdef __APPLE__
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// The CPU engine with its integer pixel work on the Apple GPU: the same result, bit for bit.
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if (BraggIntegrationEngineMetal::Available(in_experiment))
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bragg_engine = std::make_unique<BraggIntegrationEngineMetal>(in_experiment, in_mask);
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else
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#endif
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bragg_engine = std::make_unique<BraggIntegrationEngineCPU>(in_experiment, in_mask);
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if (experiment.ROI().size() >= 1)
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roi = std::make_unique<ROIIntegrationCPU>(experiment);
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#ifdef JFJOCH_USE_CUDA
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} else {
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stream = std::make_shared<CudaStream>();
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// The host copy of the preprocessed image is only read when a CPU engine wants it, which is
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// the same condition that drives copy_image_to_host below. Skipping it also skips page-locking
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// 4 bytes per pixel per worker.
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preprocessor_buffer = std::make_unique<ImagePreprocessorBufferGPU>(
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experiment.GetPixelsNum(), /*host_mirror=*/!enable_fused_adaptive_gpu);
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// The preprocessed image only has to come back to the host if a CPU engine reads it. Every
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// engine built below runs on the GPU, except the CPU adaptive finder that is kept when the fused
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// GPU engine is off - so that is the one case that needs the copy. Every caller currently passes
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// enable_fused_adaptive_gpu = true, so on the GPU path the copy is off in practice.
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preprocessor = std::make_unique<ImagePreprocessorGPU>(in_experiment, in_mask, stream,
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/*copy_image_to_host=*/!enable_fused_adaptive_gpu);
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bragg_engine = std::make_unique<BraggIntegrationEngineGPU>(in_experiment, stream, in_mask);
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if (experiment.ROI().size() >= 1)
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roi = std::make_unique<ROIIntegrationGPU>(experiment, stream);
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if (enable_fused_adaptive_gpu) {
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// One GPU engine that computes the azimuthal profile and the adaptive spot mask in a single
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// image pass. fused_adaptive aliases it so Analyze() can lift the profile out of it.
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auto fused = std::make_unique<AdaptiveSpotFinderGPU>(integration, stream);
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fused_adaptive = fused.get();
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adaptiveSpotFinder = std::move(fused);
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}
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}
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#endif
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if (!adaptiveSpotFinder) {
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auto cpu = std::make_unique<AdaptiveSpotFinderCPU>(integration);
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// Where the azimuthal integration is the CPU engine too, the finder's first ring pass is the
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// same pass over the same pixels, and gives the profile (see AdaptiveSpotFinderCPU).
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bool cpu_azint = true;
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#ifdef JFJOCH_USE_CUDA
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cpu_azint = !stream;
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#endif
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if (cpu_azint) {
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cpu->FuseAzimuthalIntegration(true);
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fused_adaptive_cpu = cpu.get();
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}
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adaptiveSpotFinder = std::move(cpu);
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}
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// The panels the image is made of: the spot finders' windows and components, and the squares read
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// about a spot, stay inside one (one, the whole image, without a module table).
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adaptiveSpotFinder->SetPanels(experiment.GetModules());
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preprocessor_buffer->SetPanels(PanelBoxes(experiment.GetModules(), experiment.GetXPixelsNum(),
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experiment.GetYPixelsNum()));
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}
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void MXAnalysisWithoutFPGA::Analyze(DataMessage &output,
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AzimuthalIntegrationProfile &profile,
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const SpotFindingSettings &spot_finding_settings) {
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if ((output.image.GetWidth() != xpixels)
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|| (output.image.GetWidth() * output.image.GetHeight() != npixels))
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
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"Mismatch in pixel size");
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// Decompress on the device where the preprocessor can, so only the compressed chunk crosses PCIe
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// and the host does no decompression at all. AnalyzeCompressed says whether it took the image;
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// when it declines (a CPU preprocessor, or an algorithm with no device decoder) fall through to
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// the host route unchanged. The two produce the same preprocessed image.
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const auto compression_start_time = std::chrono::steady_clock::now();
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ImageStatistics ret{};
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bool decoded_on_device = false;
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try {
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decoded_on_device = preprocessor->AnalyzeCompressed(*preprocessor_buffer, output.image, ret);
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} catch (const JFJochException &e) {
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// The device route must never be the reason a frame fails: whatever it could not handle, the
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// host decoder gets its turn. If the data really is bad the host throws too and the caller
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// sees the same error it saw before any of this existed - but a GPU-side problem costs speed
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// rather than the acquisition. Unless the context is lost, which the host route cannot survive.
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cuda_throw_if_context_lost();
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// The miniCBF and marCCD encodings are decoded on the device whenever there is one; the host
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// decoder is the CPU path, not a slower second try. A stream the device refuses, the host
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// refuses as well.
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if (output.image.GetCompressionAlgorithm() == CompressionAlgorithm::CBF_BYTE_OFFSET
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|| output.image.GetCompressionAlgorithm() == CompressionAlgorithm::UINT16_WIDENED)
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throw;
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spdlog::warn("Device decoding failed ({}), falling back to host decompression", e.what());
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decoded_on_device = false;
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// Handled here, so it must not stay behind as the thread's last error: the host route below
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// runs kernels of its own and checks cudaGetLastError() after them, which would report this
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// failure again - fatally - over an image that decoded perfectly well on the host.
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cuda_clear_error();
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}
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const auto compression_end_time = std::chrono::steady_clock::now();
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// The fused GPU engine (rugnux offline, GPU, adaptive detection) produces the azimuthal profile as
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// a byproduct of spot finding, so the separate azint pass is skipped in that case and the profile is
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// lifted out of the finder below.
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const bool fused = enable_fused_adaptive_gpu && spot_finding_settings.enable
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&& spot_finding_settings.adaptive_threshold && fused_adaptive != nullptr;
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const bool fused_cpu = spot_finding_settings.enable && spot_finding_settings.adaptive_threshold
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&& fused_adaptive_cpu != nullptr;
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if (preprocessor_cpu) {
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const auto preprocessing_start_time = std::chrono::steady_clock::now();
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ret = PreprocessCPU(output.image, fused_cpu);
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const auto preprocessing_end_time = std::chrono::steady_clock::now();
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output.preprocessing_time_s = std::chrono::duration<float>(preprocessing_end_time - preprocessing_start_time).count();
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// Decoded inside the preprocessing pass, whose time includes it.
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if (output.image.GetCompressionAlgorithm() != CompressionAlgorithm::NO_COMPRESSION)
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output.compression_time_s = 0.0f;
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} else if (!decoded_on_device) {
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const uint8_t *image_ptr = Decompress(output.image);
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const auto decompressed_time = std::chrono::steady_clock::now();
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if (output.image.GetCompressionAlgorithm() != CompressionAlgorithm::NO_COMPRESSION)
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output.compression_time_s = std::chrono::duration<float>(decompressed_time - compression_start_time).count();
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const auto preprocessing_start_time = std::chrono::steady_clock::now();
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ret = preprocessor->Analyze(*preprocessor_buffer, image_ptr, output.image.GetMode());
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const auto preprocessing_end_time = std::chrono::steady_clock::now();
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output.preprocessing_time_s = std::chrono::duration<float>(preprocessing_end_time - preprocessing_start_time).count();
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} else {
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// Decode and preprocess are one device operation here, but the decompression is still a real,
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// separately measurable cost - the decoder brackets it with CUDA events - so it is still
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// reported as one. Leaving compression_time_s unset instead would blank the broker's
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// "compression" plot trace and fill /entry/profiling/compressionTime with NaN.
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const float total_s = std::chrono::duration<float>(compression_end_time - compression_start_time).count();
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const float decompress_s = std::min(preprocessor->GetLastDecompressionTime_s(), total_s);
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output.compression_time_s = decompress_s;
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output.preprocessing_time_s = total_s - decompress_s;
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}
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if (!fused && !fused_cpu) {
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const auto azint_start_time = std::chrono::steady_clock::now();
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AzInt().Run(*preprocessor_buffer, profile);
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const auto azint_end_time = std::chrono::steady_clock::now();
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output.azint_time_s = std::chrono::duration<float>(azint_end_time - azint_start_time).count();
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}
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if (roi)
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roi->Run(*preprocessor_buffer, output.roi);
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if (spot_finding_settings.enable) {
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// Update resolution mask
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if (mask_high_res != spot_finding_settings.high_resolution_limit
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|| mask_low_res != spot_finding_settings.low_resolution_limit)
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UpdateMaskResolution(spot_finding_settings);
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ImageSpotFinder &finder = spot_finding_settings.adaptive_threshold
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? static_cast<ImageSpotFinder &>(*adaptiveSpotFinder)
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: FixedThresholdFinder();
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const auto integrate_fn = [this](const std::vector<Reflection> &predicted, size_t npredicted,
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int64_t image_number) {
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if (count_crowding)
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crowding_engine->CountNeighbourCrowding(predicted, npredicted);
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return IntegrateOnImage(predicted, npredicted, image_number);
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};
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// The supercell probe integrates on the same engine, but what the engine counts is the run's
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// measurement of its own stencil, and the probe's reflections are not the run's: keep them out.
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const auto probe_integrate_fn = [this](const std::vector<Reflection> &predicted, size_t npredicted,
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int64_t image_number) {
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const BraggIntegrationCounts before = bragg_engine->Counts();
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auto ret = IntegrateOnImage(predicted, npredicted, image_number);
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const BraggIntegrationCounts after = bragg_engine->Counts();
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probe_counts.predicted += after.predicted - before.predicted;
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probe_counts.bkg_starved += after.bkg_starved - before.bkg_starved;
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probe_counts.bkg_starved_by_neighbour += after.bkg_starved_by_neighbour - before.bkg_starved_by_neighbour;
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probe_counts.profile_fallback += after.profile_fallback - before.profile_fallback;
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probe_counts.overlapped += after.overlapped - before.overlapped;
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probe_counts.overlapped_major += after.overlapped_major - before.overlapped_major;
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probe_counts.overloads_kept += after.overloads_kept - before.overloads_kept;
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return ret;
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};
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// The radial background correction has to be decided BEFORE this image is integrated, so the
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// ice score is taken here rather than with the other per-image quantities at the end of the
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// function. It needs the peak-excluded per-ring background, which the adaptive finder has as
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// soon as it has detected - so this must be called after detection and before integration.
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// Where no such background exists (no adaptive finder), auto leaves the correction off: the
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// plain profile carries the Bragg peaks and cannot support an absolute threshold.
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//
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// On stills - and wherever the correction is set for the whole run rather than left to auto, as
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// rugnux's pre-scan sets it on a rotation run whose empty apertures ask for it - the correction
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// takes its CURVE from that background, and on stills auto applies it to every image that has one
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// (the pre-scan turns it off for a run whose empty apertures say the radial model does not hold
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// there). A serial frame has a few dozen predicted reflections, so their own
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// ring pixels - where the curve comes from otherwise - sample the radial background sparsely,
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// while the finder's rings hold every pixel of the frame. And the correction is the measured
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// curvature itself: where the background has none it is zero, so there is nothing for a gate
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// to decide. The ice score is the wrong gate there: a smooth hump that is not ice - the
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// solvent or a carrier medium - curves the background as much as an ice ring does, and the
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// score does not see it.
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const auto decide_radial_background = [this, &spot_finding_settings, &output]() {
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// Same condition as the score at the end of this function: the adaptive finder holds its
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// ring background from whenever it last ran, so requiring that it ran for THIS image is
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// what keeps a stale curve out.
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const std::vector<float> no_rings;
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const std::vector<float> &ring_bkg = spot_finding_settings.adaptive_threshold
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? adaptiveSpotFinder->GetRingBackground() : no_rings;
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const bool stills = !experiment.IsRotationIndexing();
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if (stills || !bragg_engine->IsBackgroundRadialAuto())
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bragg_engine->RadialBackgroundFromRings(ring_bkg, integration.GetQBinCount(),
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integration.Settings().GetLowQ_recipA(),
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integration.Settings().GetQSpacing_recipA());
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if (!bragg_engine->IsBackgroundRadialAuto() || ring_bkg.empty())
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return;
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output.ice_ring_score = AzimuthalIntegrationProfile::IceRingScore(
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ring_bkg, integration.GetQBinCount(), integration.Settings(),
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spot_finding_settings.ice_ring_width_Q_recipA);
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if (stills)
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bragg_engine->BackgroundRadial(!bragg_engine->GetRadialBackground().empty());
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else
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bragg_engine->BackgroundRadial(*output.ice_ring_score
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>= experiment.GetScalingSettings().GetIceMinScore());
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};
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// A missing min-pix (std::nullopt) means "choose it per image". This applies only to the stills
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// indexing path (each frame is indexed independently); rotation indexing builds one lattice from
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// all frames, so it keeps the fixed min-pix and the single-pass finder.
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const bool adaptive_min_pix = !spot_finding_settings.min_pix_per_spot.has_value()
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&& spot_finding_settings.indexing
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&& !experiment.IsRotationIndexing();
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if (adaptive_min_pix) {
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// Choose the per-image min-pix adaptively instead of a fixed one. min-pix filters connected
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// components AFTER detection, so BOTH the detection (the expensive per-pixel pass) and the
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// connected-component search run ONCE and only the filter is repeated; the azimuthal
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// profile is the one Detect() computed.
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// Score at 2/3/1 (index-only, no integration/accumulation), keep whichever maximises
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// n_indexed^2 / n_total (indexed count weighted by indexed fraction) together with its spot
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// list, and integrate that one. The lattice is searched for once per frame: the first list
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// it is found on hands it to the others, which only refine and score it (FrameIndexerCalls).
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// De novo, the min-pix 1 list is not searched where the min-pix 2 list holds fewer spots than
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// a lattice needs: such a frame has (almost) no spot of two pixels, and the cells a free search
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// fits to its single pixels were measured to be unrelated to the crystal's. Against a known
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// cell it is searched: on sparse serial data those frames are real hits (measured - leaving
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// them out halved the observations of a weakly diffracting run).
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const auto detect_start_time = std::chrono::steady_clock::now();
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finder.Detect(*preprocessor_buffer, spot_finding_settings);
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const auto &components = finder.ExtractComponents(*preprocessor_buffer, spot_finding_settings);
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float spot_finding_time_s =
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std::chrono::duration<float>(std::chrono::steady_clock::now() - detect_start_time).count();
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float indexing_time_s = 0.0f;
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SpotFindingSettings s = spot_finding_settings;
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FrameIndexerCalls frame_calls;
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std::vector<DiffractionSpot> best_spots;
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int best_mp = 0;
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double best_score = -1.0;
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size_t mp2_spot_count = 0;
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for (int mp : {2, 3, 1}) {
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s.min_pix_per_spot = mp;
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const auto extract_start_time = std::chrono::steady_clock::now();
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std::vector<DiffractionSpot> spots = ImageSpotFinder::Filter(components, s);
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spot_finding_time_s +=
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std::chrono::duration<float>(std::chrono::steady_clock::now() - extract_start_time).count();
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SpotAnalyze(experiment, s, spots, output);
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// Counted as the indexer counts them: ice-ring spots only where indexing uses them.
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if (mp == 2)
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mp2_spot_count = std::count_if(output.spots.begin(), output.spots.end(), [&](const auto &spot) {
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return experiment.GetIndexingSettings().GetIndexIceRings() || !spot.ice_ring;
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});
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if (mp == 1 && !frame_calls.lattice_found && !experiment.GetUnitCell().has_value()
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&& mp2_spot_count < experiment.GetIndexingSettings().GetViableCellMinSpots())
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continue;
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const bool indexed = indexer.IndexFrameOnly(output, s, &frame_calls);
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indexing_time_s += output.indexing_time_s.value_or(0.0f);
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if (indexed) {
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const double n_idx = static_cast<double>(output.spot_count_indexed.value_or(0));
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const double n_tot = static_cast<double>(std::max<int64_t>(1, output.spot_count.value_or(1)));
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const double score = n_idx * n_idx / n_tot;
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// On a tie the larger min-pix is kept.
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if (score > best_score || (score == best_score && mp > best_mp)) {
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best_score = score;
|
|
best_mp = mp;
|
|
best_spots = std::move(spots);
|
|
}
|
|
}
|
|
}
|
|
if (best_mp != 0) {
|
|
// Index and integrate the winning spot list; no spot finding left to do.
|
|
s.min_pix_per_spot = best_mp;
|
|
SpotAnalyze(experiment, s, best_spots, output);
|
|
decide_radial_background();
|
|
indexer.ProcessImage(output, s, *prediction, integrate_fn, probe_integrate_fn, &frame_calls);
|
|
indexing_time_s += output.indexing_time_s.value_or(0.0f);
|
|
}
|
|
// Each indexer call reports only its own time, so the escalation's total is summed here.
|
|
output.spot_finding_time_s = spot_finding_time_s;
|
|
output.indexing_time_s = indexing_time_s;
|
|
} else {
|
|
const auto spot_finding_start_time = std::chrono::steady_clock::now();
|
|
const std::vector<DiffractionSpot> spots = finder.Run(*preprocessor_buffer, spot_finding_settings);
|
|
SpotAnalyze(experiment, spot_finding_settings, spots, output);
|
|
output.spot_finding_time_s = std::chrono::duration<float>(std::chrono::steady_clock::now() - spot_finding_start_time).count();
|
|
decide_radial_background();
|
|
if (spot_finding_settings.indexing)
|
|
indexer.ProcessImage(output, spot_finding_settings, *prediction, integrate_fn, probe_integrate_fn);
|
|
}
|
|
|
|
// Recorded whichever way the frame went. A frame holding StrongPixelLimit of them is given up
|
|
// on and reports no spots at all, and this is the only thing that tells such a frame from one
|
|
// that did not diffract.
|
|
output.strong_pixel_count = finder.StrongPixelCount();
|
|
|
|
#ifdef JFJOCH_USE_CUDA
|
|
if (fused) {
|
|
// Lift the azimuthal profile the fused engine computed in the same detection pass; its azint
|
|
// cost is folded into spot_finding_time_s above.
|
|
profile.Clear(integration);
|
|
profile += fused_adaptive->GetProfile();
|
|
output.azint_time_s = 0.0f;
|
|
}
|
|
#endif
|
|
if (fused_cpu) {
|
|
fused_adaptive_cpu->GetProfile(profile);
|
|
output.azint_time_s = 0.0f;
|
|
}
|
|
}
|
|
|
|
output.max_viable_pixel_value = ret.max_value;
|
|
output.min_viable_pixel_value = ret.min_value;
|
|
output.error_pixel_count = ret.error_pixel_count;
|
|
output.saturated_pixel_count = ret.saturated_pixel_count;
|
|
output.az_int_profile = profile.GetResult();
|
|
output.az_int_profile_count = profile.GetPixelCount();
|
|
output.az_int_profile_std = profile.GetStd();
|
|
|
|
output.bkg_estimate = profile.GetBkgEstimate(integration.Settings());
|
|
|
|
// The ice score wants a radial profile with the Bragg peaks taken OUT of it. The azimuthal profile
|
|
// is a plain per-ring mean, so a strong low-resolution reflection landing in a ring's bin is
|
|
// indistinguishable from ice sitting there - measured, that alone lifts clean crystals to a score
|
|
// of 1.5-4.2, right into the range real ice occupies. The adaptive spot finder already computes
|
|
// exactly what is wanted: a sigma-clipped per-ring background, in the same bins, from which the
|
|
// peaks have been removed (an ice ring is azimuthally smooth, so it survives the clip). It is in
|
|
// raw counts rather than corrected ones, which the score does not care about - it is a ratio to the
|
|
// background interpolated under the ring, and the corrections are smooth in radius.
|
|
const std::vector<float> &ring_bkg = adaptiveSpotFinder->GetRingBackground();
|
|
const bool have_ring_bkg = spot_finding_settings.enable && spot_finding_settings.adaptive_threshold
|
|
&& !ring_bkg.empty();
|
|
output.ice_ring_score = AzimuthalIntegrationProfile::IceRingScore(
|
|
have_ring_bkg ? ring_bkg : profile.GetResult1D(), integration.GetQBinCount(),
|
|
integration.Settings(), spot_finding_settings.ice_ring_width_Q_recipA);
|
|
}
|
|
|
|
ImageSpotFinder &MXAnalysisWithoutFPGA::FixedThresholdFinder() {
|
|
if (!spotFinder) {
|
|
#ifdef JFJOCH_USE_CUDA
|
|
if (stream)
|
|
spotFinder = std::make_unique<ImageSpotFinderGPU>(experiment.GetXPixelsNum(),
|
|
experiment.GetYPixelsNum(), stream);
|
|
else
|
|
#endif
|
|
spotFinder = std::make_unique<ImageSpotFinderCPU>(experiment.GetXPixelsNum(),
|
|
experiment.GetYPixelsNum());
|
|
spotFinder->SetPanels(experiment.GetModules());
|
|
// It missed every mask update that happened before it existed, so it takes the current one
|
|
// now. Without this it would find spots outside the resolution limits.
|
|
if (mask_resolution)
|
|
spotFinder->SetResolutionMaskBits(*mask_resolution);
|
|
}
|
|
return *spotFinder;
|
|
}
|
|
|
|
AzIntEngine &MXAnalysisWithoutFPGA::AzInt() {
|
|
if (!azint) {
|
|
#ifdef JFJOCH_USE_CUDA
|
|
if (stream)
|
|
azint = std::make_unique<AzIntEngineGPU>(integration, stream);
|
|
else
|
|
#endif
|
|
azint = std::make_unique<AzIntEngineCPU>(integration);
|
|
}
|
|
return *azint;
|
|
}
|
|
|
|
void MXAnalysisWithoutFPGA::RebuildROI() {
|
|
if (experiment.ROI().empty()) {
|
|
roi.reset();
|
|
return;
|
|
}
|
|
#ifdef JFJOCH_USE_CUDA
|
|
if (stream) {
|
|
roi = std::make_unique<ROIIntegrationGPU>(experiment, stream);
|
|
return;
|
|
}
|
|
#endif
|
|
roi = std::make_unique<ROIIntegrationCPU>(experiment);
|
|
}
|
|
|
|
void MXAnalysisWithoutFPGA::AnalyzeROIOnly(DataMessage &output) {
|
|
if ((output.image.GetWidth() != xpixels)
|
|
|| (output.image.GetWidth() * output.image.GetHeight() != npixels))
|
|
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
|
|
"Mismatch in pixel size");
|
|
|
|
const uint8_t *image_ptr = Decompress(output.image);
|
|
preprocessor->Analyze(*preprocessor_buffer, image_ptr, output.image.GetMode());
|
|
RunROIOnly(output);
|
|
}
|
|
|
|
const uint8_t *MXAnalysisWithoutFPGA::Decompress(const CompressedImage &image) {
|
|
// An uncompressed image is read straight out of the message and never touches decompression_buffer,
|
|
// so what the upload reads is the reader's own bytes: page-lock those instead. Pageable memory is
|
|
// staged by the driver a chunk at a time, which on a 4-byte 16 Mpx frame is 12.9 ms against 4.0 ms
|
|
// pinned - and a miniCBF sweep takes this path for every image.
|
|
if (image.GetCompressionAlgorithm() == CompressionAlgorithm::NO_COMPRESSION)
|
|
preprocessor->PinInputRegion(image.GetCompressed(), image.GetUncompressedSize());
|
|
else
|
|
preprocessor->PinInputBuffer(decompression_buffer, image.GetUncompressedSize());
|
|
return image.GetUncompressedPtr(decompression_buffer);
|
|
}
|
|
|
|
ImageStatistics MXAnalysisWithoutFPGA::PreprocessCPU(const CompressedImage &image, bool ring_pass) {
|
|
ImageStatistics ret{};
|
|
if (ring_pass)
|
|
fused_adaptive_cpu->BeginRings();
|
|
const auto process_block = [&](size_t first, const uint8_t *data, size_t n) {
|
|
preprocessor_cpu->AnalyzeBlock(*preprocessor_buffer, first, data, n, image.GetMode(), ret);
|
|
if (ring_pass)
|
|
fused_adaptive_cpu->AccumulateRingsBlock(*preprocessor_buffer, first, n);
|
|
};
|
|
if (JFJochIsBitshuffle(image.GetCompressionAlgorithm()))
|
|
JFJochDecompressBlocks(image.GetCompressionAlgorithm(), image.GetCompressed(), image.GetCompressedSize(),
|
|
image.GetWidth() * image.GetHeight() * image.GetNumChannels(), image.GetByteDepth(),
|
|
process_block);
|
|
else
|
|
process_block(0, Decompress(image), npixels);
|
|
return ret;
|
|
}
|
|
|
|
void MXAnalysisWithoutFPGA::RunROIOnly(DataMessage &output) {
|
|
output.roi.clear();
|
|
if (roi)
|
|
roi->Run(*preprocessor_buffer, output.roi);
|
|
}
|
|
|
|
BraggIntegrationCounts MXAnalysisWithoutFPGA::BraggCounts() const {
|
|
if (!bragg_engine)
|
|
return {};
|
|
BraggIntegrationCounts c = bragg_engine->Counts();
|
|
c.predicted -= probe_counts.predicted;
|
|
c.bkg_starved -= probe_counts.bkg_starved;
|
|
c.bkg_starved_by_neighbour -= probe_counts.bkg_starved_by_neighbour;
|
|
c.profile_fallback -= probe_counts.profile_fallback;
|
|
c.overlapped -= probe_counts.overlapped;
|
|
c.overlapped_major -= probe_counts.overlapped_major;
|
|
c.overloads_kept -= probe_counts.overloads_kept;
|
|
return c;
|
|
}
|
|
|
|
void MXAnalysisWithoutFPGA::CountCrowdingAt(const BraggIntegrationSettings &settings) {
|
|
DiffractionExperiment x = experiment;
|
|
x.ImportBraggIntegrationSettings(settings);
|
|
crowding_engine = std::make_unique<BraggIntegrationEngineCPU>(x, mask);
|
|
}
|
|
|
|
BraggIntegrationCounts MXAnalysisWithoutFPGA::CrowdingCounts() const {
|
|
return crowding_engine ? crowding_engine->Counts() : BraggIntegrationCounts{};
|
|
}
|
|
|
|
void MXAnalysisWithoutFPGA::UpdateMaskResolution(const SpotFindingSettings &settings) {
|
|
mask_low_res = settings.low_resolution_limit;
|
|
mask_high_res = settings.high_resolution_limit;
|
|
// The mask is a pure function of the resolution map and the two limits, so the mapping builds it -
|
|
// once for all the workers, which otherwise each walked every pixel of the detector to arrive at
|
|
// the same bits.
|
|
mask_resolution = integration.ResolutionMaskBits(mask_high_res, mask_low_res);
|
|
|
|
// The finders keep their own copy (the GPU ones a bit-packed device copy), so the mask is handed
|
|
// over here - when the limits change - rather than with every image.
|
|
if (spotFinder)
|
|
spotFinder->SetResolutionMaskBits(*mask_resolution);
|
|
adaptiveSpotFinder->SetResolutionMaskBits(*mask_resolution);
|
|
}
|
|
|
|
std::vector<Reflection> MXAnalysisWithoutFPGA::IntegrateOnImage(const std::vector<Reflection> &predicted,
|
|
size_t npredicted, int64_t image_number) {
|
|
const auto &modules = experiment.GetModules();
|
|
if (modules.empty())
|
|
return bragg_engine->Run(*preprocessor_buffer, predicted, npredicted, image_number);
|
|
|
|
// Predictions are where the reflections fall on the detector plane; the engine reads the image, so
|
|
// each goes to where its panel put those pixels, and comes back to the plane once integrated. One in
|
|
// a gap, or off every panel, has no pixels and is not integrated. The engine finds a reflection's
|
|
// panel from its image position, as the way back below does; a position that rounding to float put
|
|
// on another panel's edge is dropped, so that the two agree.
|
|
std::vector<Reflection> on_image;
|
|
on_image.reserve(npredicted);
|
|
for (size_t i = 0; i < npredicted; i++) {
|
|
const auto p = modules.PlaneToPanel(predicted[i].predicted_x, predicted[i].predicted_y);
|
|
if (!p)
|
|
continue;
|
|
Reflection r = predicted[i];
|
|
r.predicted_x = static_cast<float>(p->x);
|
|
r.predicted_y = static_cast<float>(p->y);
|
|
if (modules.ModuleOfImagePixel(r.predicted_x, r.predicted_y) != p->panel)
|
|
continue;
|
|
on_image.push_back(r);
|
|
}
|
|
auto ret = bragg_engine->Run(*preprocessor_buffer, on_image, on_image.size(), image_number);
|
|
for (auto &r: ret) {
|
|
const int64_t panel = modules.ModuleOfImagePixel(r.predicted_x, r.predicted_y);
|
|
const auto [px, py] = modules.ImageToPlane(panel, r.predicted_x, r.predicted_y);
|
|
r.predicted_x = static_cast<float>(px);
|
|
r.predicted_y = static_cast<float>(py);
|
|
if (std::isfinite(r.observed_x) && std::isfinite(r.observed_y)) {
|
|
const auto [ox, oy] = modules.ImageToPlane(panel, r.observed_x, r.observed_y);
|
|
r.observed_x = static_cast<float>(ox);
|
|
r.observed_y = static_cast<float>(oy);
|
|
}
|
|
}
|
|
return ret;
|
|
}
|