Files
Jungfraujoch/image_analysis/spot_finding/AdaptiveSpotFinderCPU.h
T
leonarski_f a395f358ef
Build Packages / Create release (push) Successful in 17s
Build Packages / build:viewer:macos-arm64:nocuda (push) Successful in 3m22s
Build Packages / build:rugnux:macos-arm64:nocuda (push) Successful in 2m37s
Build Packages / build:rugnux:linux-aarch64:cuda (push) Successful in 9m33s
Build Packages / build:rugnux:linux-x86_64:cuda (push) Successful in 10m39s
Build Packages / build:viewer:linux-x86_64:nocuda (push) Successful in 11m4s
Build Packages / build:viewer:linux-x86_64:cuda (push) Successful in 13m19s
Build Packages / build:jfjoch:rocky8:nocuda (push) Successful in 17m37s
Build Packages / build:jfjoch:rocky9:nocuda (push) Successful in 18m49s
Build Packages / build:viewer:windows-x86_64:nocuda (push) Successful in 19m10s
Build Packages / build:viewer:windows-x86_64:cuda (push) Successful in 24m26s
Build Packages / HDF5 consumer tests (DIALS, XDS) (push) Successful in 25m31s
Build Packages / build:jfjoch:ubuntu2404:nocuda (push) Successful in 18m54s
Build Packages / build:jfjoch:ubuntu2204:nocuda (push) Successful in 20m45s
Build Packages / Generate python client (push) Successful in 37s
Build Packages / build:jfjoch:rocky8:cuda-sls9 (push) Successful in 20m20s
Build Packages / Build documentation (push) Successful in 1m32s
Build Packages / build:rugnux:windows-x86_64:cuda (push) Successful in 14m37s
Build Packages / build:jfjoch:rocky9:cuda-sls9 (push) Successful in 21m6s
Build Packages / build:jfjoch:rocky8:cuda (push) Successful in 19m49s
Build Packages / build:jfjoch:rocky9:cuda (push) Successful in 20m29s
Build Packages / build:jfjoch:ubuntu2204:cuda (push) Successful in 17m2s
Build Packages / build:jfjoch:ubuntu2404:cuda (push) Successful in 14m27s
Build Packages / Unit tests (push) Successful in 1h18m12s
1.0.0-rc.174 (#84)
* Rugnux: Performance improvements on GPU and CPU (more of the pre-scan and of scaling on the GPU, faster CPU spot finding and crystal refinement), with unchanged results.
* Rugnux: More robust processing - patches of persistently hot pixels are masked, an inconsistent merge triggers a retry at the measured beam centre, and builds targeting different CPU levels give the same results.
* Rugnux: Improved scaling and merging - reflections with an overloaded pixel are dropped, as in XDS, sparse rotation sweeps are scaled more reliably, and French-Wilson amplitudes use an anisotropic Wilson prior.
* Rugnux: Improved space-group determination - glide planes in groups without a centre of symmetry, screw axes from short or weak axial rows kept when a higher group is adopted, and more reliable decisions on twinned and pseudo-symmetric crystals.
* Rugnux: Improved small-molecule processing - spots that grow wider than the integration disk and split spots are integrated over their measured footprint, sparse lattices are integrated on every frame, and the `.hkl` file holds unmerged scaled reflections (SHELX HKLF 4).
* Rugnux: Reads Rigaku d*TREK SMV images (Saturn CCD), including detector 2theta and encoded pixel overflows; home-source (rotating-anode) datasets were added to the validation battery.
* jfjoch_viewer: Fixed processing failing at the end with "Wrong JPEG library version" on Linux; the merge window shows the space group with proper subscripts and a checklist of crystal pathologies.

Reviewed-on: #84
Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
2026-10-06 14:03:18 +02:00

111 lines
6.6 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <vector>
#include "ImageSpotFinderCPU.h"
#include "SpotFindingSettings.h"
#include "../../common/AzimuthalIntegrationMapping.h"
#include "../../common/AzimuthalIntegrationProfile.h"
// Self-calibrating strong-pixel detector for the offline (rugnux/viewer) path.
//
// The classic finder (ImageSpotFinderCPU) marks a pixel strong when it clears a *fixed* photon
// count AND a local-box SNR. The fixed photon floor is what forces per-dataset tuning: it must sit
// above the background (wants high) yet not bury weak spots (wants low), and the background level
// differs per dataset, so the sweet spot is narrow (~12 photons on one serial-stills set, ~5 on a
// weaker one).
//
// Here the floor is replaced by a per-resolution-ring threshold derived from a single portable
// number: E = the expected count of noise pixels tolerated per frame (default ~100). For a ring
// whose (peak-excluded) background mean is mu, the threshold is the smallest count whose Poisson
// upper tail is <= p = E / N_pixels, max'd with a Gaussian arm mu + z*sigma to absorb read/flat-field
// excess. Because it is set from the image's own noise, the SAME E lands at ~12 photons on the first
// set and ~5 on the weaker one with no user input.
//
// The ring threshold replaces the floor and ONLY the floor: the classic finder's local-box SNR test
// still has to pass, which is why this engine runs it (ImageSpotFinderCPU) and intersects the two
// masks.
//
// A whole-ring threshold is an ABSOLUTE contour with no feedback from the pixel's own surroundings,
// so the area a spot puts above it grows as sigma^2 * ln(peak/threshold) and never saturates: on a
// strongly diffracting rotation set the detected footprint grows by 8 pixels per e-fold of peak, so
// the brightest reflections came out as 100-500 pixel blobs. The local box has no such contour. The
// spot inflates the box's own variance, and the peak divides out of the acceptance test, so the box
// cuts every spot at roughly a fixed FRACTION of its own height - a peak-relative contour. Measured
// on the same set, the footprint then grows by -0.2 pixels per e-fold, i.e. not at all, and lands on
// the classic finder's own number to two decimals.
//
// The two arms bind in different regimes, which is the point of intersecting rather than choosing.
// On serial stills the ring background is a fraction of a count and the ring threshold lands BELOW
// the fixed floor the classic finder would use, so the ring arm decides and the local box passes
// everything - which is the whole reason this engine exists. On a bright rotation set the ring
// background is tens of counts, the ring threshold lands several times ABOVE that floor, and the
// local box decides. The engine is therefore never worse than the classic finder on footprint, and
// never worse than a fixed floor on a weak background.
class AdaptiveSpotFinderCPU : public ImageSpotFinderCPU {
const AzimuthalIntegrationMapping &mapping;
// per-ring scratch, sized to the mapping's bin count
// Exact integers: a preprocessed pixel is an int32 and the sentinels are skipped, so v and v*v
// are exact in 64 bits. That is what lets the GPU engine reproduce these bit for bit - integer
// addition is associative, so its block atomics can arrive in any order.
std::vector<int64_t> ring_sum;
std::vector<uint64_t> ring_sum2;
std::vector<int64_t> ring_cnt;
std::vector<float> ring_mean;
std::vector<float> ring_sigma;
std::vector<float> ring_thr; // nbins + 1: the last is +inf, the threshold of a pixel outside every ring
// ring_mean of the last Detect(), NaN where the ring holds too few pixels to be its own background.
// Kept separately because ring_mean carries the previous frame's value for an empty ring.
std::vector<float> ring_bkg;
// Pixels at or above their ring's threshold, packed like output_buffer. Intersected with the
// local-box mask that ImageSpotFinderCPU::Detect leaves in output_buffer.
std::vector<uint32_t> ring_bits;
// The plain pass's valid pixels as a per-ring histogram of their values (HIST_VALUES bins per
// ring) plus a list of the values outside it. The plain sums and the two sigma-clip passes are all
// taken from it, over distinct values instead of over the image. Integer sums, so the same totals.
static constexpr int32_t HIST_VALUES = 1024;
std::vector<uint32_t> ring_hist;
std::vector<std::pair<uint16_t, int32_t>> ring_overflow; // (ring, value)
// The azimuthal-integration profile, taken in the plain ring pass when asked for: the
// same pixels in the same order and the same arithmetic as AzIntEngineCPU, so the same sums, and
// one pass over the image less (the CPU twin of the fused GPU engine).
bool fuse_azint = false;
std::vector<float> azint_sum;
std::vector<float> azint_sum2;
std::vector<uint32_t> azint_count;
// Set by BeginRings(): the plain ring pass of the next Detect() is being accumulated block by block.
bool rings_from_blocks = false;
// Zero the sums of the plain ring pass (and of the fused profile).
void ResetRings();
// One sigma-clip pass over the plain pass's values; clip_k = INFINITY gives the plain sums.
void ClipRings(float clip_k);
// ring_mean / ring_sigma from the current sums.
void UpdateRingStatistics();
// Set the ring_bits of the pixels of one row from the thresholds of the current frame. ring_bits
// is zeroed before the first row.
void FlagRow(const ImagePreprocessorBuffer &image, int32_t row);
public:
explicit AdaptiveSpotFinderCPU(const AzimuthalIntegrationMapping &mapping);
void Detect(const ImagePreprocessorBuffer &image, const SpotFindingSettings &settings) override;
[[nodiscard]] const std::vector<float> &GetRingBackground() const override { return ring_bkg; }
// The plain ring pass taken while the image is being preprocessed, so the pixels are read while
// still in cache: BeginRings(), then AccumulateRingsBlock() over every block of the image in pixel
// order - the order keeps the profile's float sums the same - and the next Detect() of that image
// starts from these sums instead of passing over the image for them.
void BeginRings();
void AccumulateRingsBlock(const ImagePreprocessorBuffer &image, size_t first, size_t n);
void FuseAzimuthalIntegration(bool enable) { fuse_azint = enable; }
// The profile of the last Detect(), when fused.
void GetProfile(AzimuthalIntegrationProfile &profile) const;
};