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Jungfraujoch/image_analysis/hot_pixels/HotPixelsGPU.h
T
leonarski_f 711f902aa7 One gain mechanism: frames analysed in their own units, the gain applied where a photon count is assumed
The gain of an input in detector units (SMV, marCCD, XTC, the XFEL formats) is measured once, before
anything reads the frames (RugnuxMeasureGain: photon-peak spacing and background variance per unit
mean), or given with --gain, and handed to the integration and the spot finding by one function
(ApplyRugnuxPixelGain). The rotation pre-scan no longer measures it, and the stills reader no longer
converts frames of a detector whose photons are not resolved: SACLA MPCCD, Rayonix and CCD frames are
integrated in ADU at the gain, rotation and stills alike.

Where a photon count was assumed, it is now taken at the gain (bit-identical at gain 1):
- integration: the background clip's one-photon floor, the sigma floor (one photon), the profile fit's
  background floor (0.01 photon), and a still's overload kept as a lower bound, which was left in ADU
  while every other reflection left the integrator in photons - the cause of the earlier loss of the
  engine route on clipped CCD stills (merged low resolution inflated by the gain, the rocking-width
  fit's bandwidth collapsing); CPU and CUDA, Metal through the shared CPU code;
- the adaptive spot threshold (Poisson arm and its one-photon floor), CPU and GPU;
- the pre-scan spot-width measurement (strong-spot SNR, the ring clip) behind the stills bandwidth and
  the rotation signal radius;
- the hot/dead pixel tests, CPU and GPU; the empty-aperture ring clip.
BackgroundPixelGain is gone: one pixel_gain.

A detector whose background resolves single photons (photon peaks: JUNGFRAU in keV, AGIPD) is a photon
counter and its frames are still counted in whole photons as read: analysed in keV at the gain instead,
a photon-counting still set lost 0.008 R-free (fewer spots, a noisier beam-centre trial) - the rounding
is the detector's own discrimination. --gain sets the value, the background still says which kind.

Validation, against rc178: photon-counting and g<=1 rotation p.hkl md5-identical (lyso, thau, cytc,
myob, insu, a small-molecule set, 5reo, 6cee, 6i3j, 2xfw, 6s1u, 8v4j); CCD g>1 at or within noise of rc178 (5uth
R_meas 0.113 -> 0.108). Stills R-free (0-cycle REFMAC): MPCCD sets 0.184 -> 0.185 and 0.199 -> 0.196,
Rayonix identical, photon-resolving sets back on their rc178 numbers.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit da8dbfc8147354b63e375f2d9521dffb80938b6d)
2026-10-23 17:56:51 +02:00

109 lines
5.4 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <cstdint>
#include <memory>
#include <mutex>
#include <vector>
#include "../indexing/CUDAMemHelpers.h"
// What a frame's levels and lit thresholds are made of (HotPixelFinder's constants), handed to the
// device so the two halves cannot drift apart.
struct HotPixelLevelRules {
int min_sector_pixels;
int min_ring_pixels;
float lit_nsigma;
float lit_offset; // in the frames' units
float gain; // the frames' units per photon
};
// The device half of HotPixelFinder, for frames already preprocessed on the GPU. Everything a frame adds
// stays on the device: the per-frame order statistics (each ring-sector's median, each ring's median
// and median absolute deviation), the levels and thresholds made from them, and the per-pixel and
// per-key sums. Every statistic is an exact order statistic of integers, the threshold is computed
// with the same rounding steps as the host's (see HotPixelFinder::AddLevels) and every sum is an
// integer, so the sums are identical to what HotPixelFinder::AddImage produces. When the mask is read
// only what can decide it comes back: per-ring counts for the chance rate, then the few pixels that can
// be persistent or carry the error value.
class HotPixelFinderGPU {
int device = 0; // the card it was built on, where everything below lives
const size_t npixels;
const size_t nkeys;
const int nrings;
const int sectors;
const HotPixelLevelRules rules;
CudaDevicePtr<int32_t> key; // ring * sectors + sector of each pixel, -1 masked
CudaDevicePtr<uint32_t> pixels_by_key; // the unmasked pixels, grouped by key, in pixel order
CudaDevicePtr<uint32_t> key_begin; // where each key's pixels start in pixels_by_key
// The per-pixel and per-key sums, exactly those of HotPixelFinder.
CudaDevicePtr<uint16_t> n_lit, n_error, n_error_ring_ok;
CudaDevicePtr<int64_t> sum_value, error_level_sum;
CudaDevicePtr<uint32_t> key_frames;
CudaDevicePtr<int64_t> key_level_sum, key_local_level_sum;
// Each pixel's sums are plain read-modify-writes, so the frames add to them one after another:
// every accumulation waits on the device for the one before it (an event, not the host).
std::mutex accumulate_mutex;
cudaEvent_t last_accumulate = nullptr;
public:
// One worker's buffers, on the stream its frames are preprocessed on.
struct Frame {
explicit Frame(std::shared_ptr<CudaStream> stream) : stream(std::move(stream)) {}
// Its frames are queued, not waited for (Add), so the buffers below must not be freed before
// the stream has used them.
~Frame() { if (stream) cudaStreamSynchronize(*stream); }
Frame(Frame &&) = default;
std::shared_ptr<CudaStream> stream;
CudaDevicePtr<uint32_t> count; // valid pixels per key
CudaDevicePtr<int32_t> sector_median; // per key
CudaDevicePtr<int32_t> ring_median, ring_mad;
CudaDevicePtr<int32_t> level;
CudaDevicePtr<float> threshold;
CudaDevicePtr<char> ring_ok;
};
// The keys of HotPixelFinder: one per pixel, and where each key's pixels start among the unmasked
// pixels sorted by key.
HotPixelFinderGPU(const int32_t *key, size_t npixels, const std::vector<uint32_t> &key_begin, int nrings,
int sectors, const HotPixelLevelRules &rules);
~HotPixelFinderGPU();
HotPixelFinderGPU(const HotPixelFinderGPU &) = delete;
HotPixelFinderGPU &operator=(const HotPixelFinderGPU &) = delete;
[[nodiscard]] int GetDevice() const { return device; }
// Add the sums another card accumulated to this one's. Every sum is an integer, so the result does
// not depend on how the frames were split between the cards or on the order they are folded in.
// Called with this finder's card current.
void Fold(HotPixelFinderGPU &other);
// Add one frame, preprocessed on frame.stream, as HotPixelFinder::AddImage does. Queued on that
// stream; nothing is waited for on the host.
void Add(const int32_t *device_image, Frame &frame);
// Per ring, over the pixels lit on no more than half of their valid frames: the lit frames and the
// valid frames, summed (HotPixelFinder::GetMask's chance rate). Waits for every frame added.
void ChanceCounts(std::vector<int64_t> &lit, std::vector<int64_t> &seen);
// The pixels that can be masked: those holding the error value on more than half of `frames`;
// where spacing_ok, those lit on at least min(max(2, k_chance[ring]), valid frames) of at least
// min_valid valid frames - a persistent pixel is lit on at least that many, because one reflection
// explains at least two; and those with no error frame, at least min_valid valid frames and a sum
// at most half of their sector's level sum, which a dead pixel has. For each, its index and
// per-pixel sums; and every key's sums.
struct Candidates {
std::vector<uint32_t> index;
std::vector<uint16_t> n_lit, n_error, n_error_ring_ok;
std::vector<int64_t> sum_value, error_level_sum;
std::vector<uint32_t> key_frames;
std::vector<int64_t> key_level_sum, key_local_level_sum;
};
Candidates GetCandidates(uint32_t frames, int min_valid, bool spacing_ok, const std::vector<int> &k_chance);
};