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Jungfraujoch/image_analysis/geom_refinement/RingOptimizer.cpp
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v1.0.0-rc.153 (#63)
This is an UNSTABLE release. It includes many experimental features, as well as many AI generated fixes. We recommend using rc.152 for production use.

* jfjoch_broker: Add EXPERIMENTAL pixelrefine mode for image processing
* jfjoch_broker: Allow to load user mask from 8-bit and 16-bit TIFF files
* jfjoch_broker: Add ROI calculation in non-FPGA workflow
* jfjoch_broker: Fixes to TCP image pusher
* jfjoch_broker: Remove NUMA bindings
* jfjoch_broker: Improvements to indexing
* jfjoch_broker: For PSI EIGER, trimming energies are taken from the detector configuration (now compulsory) instead of hardcoded values
* jfjoch_writer: Save ROI definitions and the per-pixel ROI bitmap in the master file; azimuthal ROIs support phi (angular) sectors
* jfjoch_viewer: Major redesign with dockable panels and saved layouts, plus on-canvas creation/move/resize of box, circle and azimuthal ROIs
* jfjoch_viewer: Run jfjoch_process reprocessing jobs from inside the GUI and overlay per-run results

Reviewed-on: #63
2026-06-23 20:29:49 +02:00

102 lines
3.5 KiB
C++

// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "../../common/JFJochMath.h"
#include "RingOptimizer.h"
#include "ceres/ceres.h"
struct RingResidual {
RingResidual(double x, double y, double lambda,
double pixel_size,
double expected_q)
: obs_x(x), obs_y(y),
lambda(lambda),
pixel_size(pixel_size),
expected_len_recip_sq(expected_q * expected_q / (4.0 * PI * PI)) {}
template<typename T>
bool operator()(const T* const center_x, const T* const center_y,
const T* const distance, const T* const rot1,
const T* const rot2, T* residual) const {
// Calculate lab coordinates from observed pixel coordinates
T x_lab = (T(obs_x) - center_x[0]) * T(pixel_size); // convert to mm
T y_lab = (T(obs_y) - center_y[0]) * T(pixel_size);
T z_lab = distance[0];
// Apply rotations around y and x axes
T c1 = ceres::cos(rot1[0]);
T c2 = ceres::cos(rot2[0]);
T s1 = ceres::sin(rot1[0]);
T s2 = ceres::sin(rot2[0]);
T x = x_lab * c1 + z_lab * s1;
T y = y_lab * c2 + (-x_lab * s1 + z_lab * c1) * s2;
T z = -y_lab * s2 + (-x_lab * s1 + z_lab * c1) * c2;
// convert to recip space
T lab_norm = ceres::sqrt(x*x + y*y + z*z);
T R_x = x / (lab_norm * T(lambda));
T R_y = y / (lab_norm * T(lambda));
T R_z = (z / lab_norm - T(1.0)) / T(lambda);
T predicted_len_recip_sq = R_x * R_x + R_y * R_y + R_z * R_z;
residual[0] = predicted_len_recip_sq - T(expected_len_recip_sq);
return true;
}
const double obs_x, obs_y;
const double lambda;
const double pixel_size;
const double expected_len_recip_sq;
};
RingOptimizer::RingOptimizer(const DiffractionGeometry& geom) : reference(geom) {}
DiffractionGeometry RingOptimizer::Run(const std::vector<RingOptimizerInput> &input) {
// Initial guess for the parameters
double center_x = reference.GetBeamX_pxl();
double center_y = reference.GetBeamY_pxl();
double distance = reference.GetDetectorDistance_mm();
double rot1 = reference.GetPoniRot1_rad();
double rot2 = reference.GetPoniRot2_rad();
ceres::Problem problem;
// Add residuals for each point
for (const auto& pt : input) {
problem.AddResidualBlock(
new ceres::AutoDiffCostFunction<RingResidual, 1, 1, 1, 1, 1, 1>(
new RingResidual(pt.x, pt.y,
reference.GetWavelength_A(),
reference.GetPixelSize_mm(),
pt.q_expected)),
nullptr,
&center_x,
&center_y,
&distance,
&rot1,
&rot2
);
}
// Configure solver
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;
options.minimizer_progress_to_stdout = false;
options.logging_type = ceres::LoggingType::SILENT;
options.num_threads = 1;
ceres::Solver::Summary summary;
// Run optimization
ceres::Solve(options, &problem, &summary);
DiffractionGeometry refined_geom(reference);
refined_geom.BeamX_pxl(center_x).BeamY_pxl(center_y).DetectorDistance_mm(distance)
.PoniRot1_rad(rot1).PoniRot2_rad(rot2);
return refined_geom;
}