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Jungfraujoch/image_analysis/spot_finding/AdaptiveSpotFinderCPU.cpp
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leonarski_fandClaude Opus 5 6081b6bc43 docs: credit the L test, FFT indexing, TORO, Niggli, peakfinder8 and SparseCCL
Six methods the pages name or describe carried no citation: Padilla & Yeates
(the L test), Steller, Bolotovsky & Rossmann (the projection/FFT autoindexing
MOSFLM implements), TORO (what ffbidx implements), Krivy & Gruber and the
ITA lattice-character table (the reduction and Bravais assignment), Cheetah's
peakfinder8 (the per-ring background statistics of the adaptive finder) and
Hennequin et al.'s SparseCCL (already credited to traccc, now also to its
authors). Each gets its ACKNOWLEDGEMENT.md paragraph, a References entry in
CPU_DATA_ANALYSIS.md, and a one-line credit at the algorithm. The
Sheriff & Hendrickson / Popov & Bourenkov entry is re-scoped so each claim
sits on the paper that supports it - P&B 2003 is titled, and credited for the
sigma-aware anisotropy estimation its statistic modelling contains, not for
the tensor and its constraints. All DOIs verified against the publishers;
the SparseCCL DOI resolves to IEEE document 9049184 (IEEE blocks content
scraping, so verified by the resolved document id plus two independent
sources).

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EFEJG6WBQv8th4UJFNe53N
2026-09-02 09:18:36 +02:00

152 lines
6.2 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include <algorithm>
#include <bitset>
#include <cmath>
#include "AdaptiveSpotFinderCPU.h"
#include "AdaptiveThreshold.h"
AdaptiveSpotFinderCPU::AdaptiveSpotFinderCPU(const AzimuthalIntegrationMapping &in_mapping)
: ImageSpotFinderCPU(static_cast<int32_t>(in_mapping.GetWidth()),
static_cast<int32_t>(in_mapping.GetHeight())),
mapping(in_mapping) {
const size_t nbins = mapping.GetBinNumber();
ring_sum.assign(nbins, 0);
ring_sum2.assign(nbins, 0);
ring_cnt.assign(nbins, 0);
ring_mean.assign(nbins, 0.0f);
ring_sigma.assign(nbins, 0.0f);
ring_thr.assign(nbins, 0.0f);
ring_bkg.assign(nbins, NAN);
ring_bits.assign(OutputSize(), 0);
}
// Per-ring background statistics with iterated peak exclusion following peakfinder8:
// Barty et al. (2014) J. Appl. Cryst. 47, 1118-1131
// Accumulate per-ring mean/variance from the raw (photon) image. clip_k <= 0 -> use every valid
// pixel (first pass); clip_k > 0 -> keep only pixels within clip_k sigma of the current ring mean,
// which removes the Bragg peaks from the background estimate.
void AdaptiveSpotFinderCPU::AccumulateRings(const ImagePreprocessorBuffer &image, float clip_k) {
const auto &pixel_to_bin = mapping.GetPixelToBin();
const size_t nbins = ring_sum.size();
const size_t npix = static_cast<size_t>(width) * height;
std::fill(ring_sum.begin(), ring_sum.end(), 0);
std::fill(ring_sum2.begin(), ring_sum2.end(), 0);
std::fill(ring_cnt.begin(), ring_cnt.end(), 0);
for (size_t pxl = 0; pxl < npix; ++pxl) {
const int32_t v = image[pxl];
if (v == INT32_MIN || v == INT32_MAX) continue; // bad / saturated
const uint16_t b = pixel_to_bin[pxl];
if (b >= nbins) continue; // masked / out of range (UINT16_MAX)
if (clip_k > 0.0f) {
const float lo = ring_mean[b] - clip_k * ring_sigma[b];
const float hi = ring_mean[b] + clip_k * ring_sigma[b];
if (v < lo || v > hi) continue; // exclude peaks / outliers
}
ring_sum[b] += v;
ring_sum2[b] += static_cast<uint64_t>(static_cast<int64_t>(v) * v);
ring_cnt[b] += 1;
}
for (size_t b = 0; b < nbins; ++b) {
if (ring_cnt[b] > 0) {
const double m = static_cast<double>(ring_sum[b]) / ring_cnt[b];
const double var = std::max(0.0, static_cast<double>(ring_sum2[b]) / ring_cnt[b] - m * m);
ring_mean[b] = static_cast<float>(m);
ring_sigma[b] = static_cast<float>(std::sqrt(var));
}
}
}
void AdaptiveSpotFinderCPU::Detect(const ImagePreprocessorBuffer &image,
const SpotFindingSettings &settings) {
const size_t nbins = ring_sum.size();
// --- Stage A: robust per-ring background (one plain pass + two sigma-clip passes) ---
AccumulateRings(image, 0.0f);
AccumulateRings(image, 3.0f);
AccumulateRings(image, 3.0f);
// --- Stage B: per-ring threshold from the single portable knob E (false pixels / frame) ---
int64_t n_total = 0;
double g_sum = 0.0, g_sum2 = 0.0;
for (size_t b = 0; b < nbins; ++b) {
n_total += ring_cnt[b];
g_sum += static_cast<double>(ring_sum[b]);
g_sum2 += static_cast<double>(ring_sum2[b]);
}
if (n_total == 0) {
// Nothing valid to threshold against: leave no strong pixels for ExtractSpots to build on.
std::fill(output_buffer.begin(), output_buffer.end(), 0);
std::fill(ring_bkg.begin(), ring_bkg.end(), NAN);
return;
}
for (size_t b = 0; b < nbins; ++b)
ring_bkg[b] = (ring_cnt[b] < adaptive_threshold::MIN_RING_PIXELS) ? NAN : ring_mean[b];
const double E = std::max(1.0f, settings.false_pixels_per_frame);
double p = E / static_cast<double>(n_total);
p = std::min(std::max(p, 1e-9), 0.1);
const float z = static_cast<float>(adaptive_threshold::NormalQuantile(1.0 - p));
// whole-frame fallback background for rings too sparse to trust on their own
const double g_mean = g_sum / n_total;
const double g_sigma = std::sqrt(std::max(0.0, g_sum2 / n_total - g_mean * g_mean));
const float g_thr = adaptive_threshold::RingThreshold(static_cast<float>(g_mean),
static_cast<float>(g_sigma), p, z);
for (size_t b = 0; b < nbins; ++b)
ring_thr[b] = (ring_cnt[b] < adaptive_threshold::MIN_RING_PIXELS)
? g_thr
: adaptive_threshold::RingThreshold(ring_mean[b], ring_sigma[b], p, z);
// --- Stage C: the ring threshold, intersected with the classic local-box SNR test ---
FlagRings(image);
if (settings.signal_to_noise_threshold <= 0.0f) {
// No local test asked for: the ring threshold alone decides, as the fixed photon floor
// alone would in the classic finder.
output_buffer = ring_bits;
return;
}
// The ring threshold IS the photon floor here, so the local pass must not apply another one.
SpotFindingSettings local = settings;
local.photon_count_threshold = 0;
ImageSpotFinderCPU::Detect(image, local);
for (size_t i = 0; i < OutputSize(); ++i)
output_buffer[i] &= ring_bits[i];
}
void AdaptiveSpotFinderCPU::FlagRings(const ImagePreprocessorBuffer &image) {
const auto &pixel_to_bin = mapping.GetPixelToBin();
const size_t nbins = ring_thr.size();
const size_t npix = static_cast<size_t>(width) * height;
std::bitset<32> out = 0;
for (size_t pxl = 0; pxl < npix; ++pxl) {
const int32_t v = image[pxl];
const uint16_t b = pixel_to_bin[pxl];
bool strong = false;
if (v == INT32_MAX)
strong = true;
else if (v != INT32_MIN && b < nbins && v >= ring_thr[b])
strong = true;
const int32_t bit = pxl % 32;
if (strong)
out.set(bit);
if (bit == 31) {
ring_bits[pxl / 32] = out.to_ulong();
out.reset();
}
}
if (npix % 32 != 0)
ring_bits[OutputSize() - 1] = out.to_ulong();
}