leonarski_fandClaude Opus 5
0e23fd3ab9
Two independent pieces in the same code path. The background-estimate variance was never propagated. A reflection's background comes from a finite ring of n_b pixels, so subtracting it adds var(B)/n_b per signal pixel - sqrt(1 + n_d/n_b) = 1.109 with the shipped stencil. Both engines omitted it, which is exactly the 1.11-1.19 gap measured between the off-ring scatter and the reported sigma. Three lines each; it affects every dataset, not only iced ones. The radial correction is new and OFF by default (--background-radial). The signal disk and the background ring are concentric, so for any background LINEAR in position <B>_ann == <B>_disk identically and a plane fit buys nothing; the leading error is the CURVATURE of the radial background, which on a sharp ice ring reaches +26 counts on a single reflection. Since every reflection uses the same stencil, that error is a fixed kernel over radial offset - one short dot product per reflection and no extra pixel reads. Validated on empty apertures before any C++: mean |bias| over 9 bands / 3 crystals 4.33 -> 0.79 counts with the scatter unchanged. Three things it cost a battery each to learn, all now in the code: - the radial curve must be accumulated from CLIPPED annulus pixels, inside the clip pass, or it carries neighbour tails and zingers (so it is inert under --integrator boxsum, which has no clip pass); - the GPU version was a 1.8x slowdown from atomicAdd contention on a small radial array - staged in shared memory per block it now costs nothing measurable; - it is battery-NEUTRAL as a default, because the reflections whose bias it fixes are the ones the ice handling already excludes. Hence off by default. CPU/GPU parity extended with two radial sections: 9002 assertions. Also fixes a latent French-Wilson quadrature collapse: j_max = I + 8 sigma on a fixed 400-point grid degenerates to a single cell once sigma >> 50 <I>, giving F = 0.1 sqrt(sigma) with sigmaF -> 0. Harmless today, but any sigma-inflation scheme detonates it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
465 lines
23 KiB
C++
465 lines
23 KiB
C++
// SPDX-FileCopyrightText: 2026 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 "BraggIntegrationEngineCPU.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdint>
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#include <limits>
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#include <vector>
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#include "../../common/CompressedImage.h"
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#include "../../common/JFJochException.h"
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using namespace bragg_engine;
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namespace {
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// The engine reads pixels in the INT32_MIN(masked)/INT32_MAX(saturated) convention.
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inline bool valid(int32_t v) { return v != INT32_MIN && v != INT32_MAX; }
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// Identity sampler over the preprocessed int32 buffer (already in that convention).
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struct BufferSampler {
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const int32_t *p;
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int32_t operator[](size_t i) const { return p[i]; }
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};
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// Sampler over a raw detector image of pixel type T: masked pixels carry the type minimum, saturated
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// the type maximum (the FPGA image has no lossy-codec +/-1 band). Only the pixels actually read - the
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// reflection disks - are converted, so there is no whole-image pass.
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template <class T>
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struct ImageSampler {
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const T *p;
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int64_t special_value;
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int64_t saturation;
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int32_t operator[](size_t i) const {
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const int64_t v = p[i];
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if (v == special_value) return INT32_MIN;
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if (v == saturation) return INT32_MAX;
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return static_cast<int32_t>(v);
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}
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};
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} // namespace
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BraggIntegrationEngineCPU::BraggIntegrationEngineCPU(const DiffractionExperiment &experiment)
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: BraggIntegrationEngine(experiment) {}
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template <class Sampler>
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std::vector<Reflection> BraggIntegrationEngineCPU::RunImpl(const Sampler &img,
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const std::vector<Reflection> &predicted,
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size_t npredicted, int64_t image_number) {
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std::vector<BraggFitResult> results(npredicted);
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if (npredicted == 0)
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return Finalize(predicted, npredicted, results, image_number);
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const int W = static_cast<int>(xpixel), H = static_cast<int>(ypixel);
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const bool do_clip = bkg_clip_nsigma > 0.0f && mode != IntegratorMode::BoxSum;
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// Symmetric trimmed-mean background fraction (BraggIntegrationSettings, rugnux --background-trim):
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// replaces the r2..r3 ring MEAN with an f-trimmed mean, robust to the high-side contamination
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// (neighbour wings, tails) that biases the plain mean up and makes it over-subtract weak high-angle
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// reflections. The base ctor has already forced it to 0 whenever the clip below is in force, which
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// is the default - the two are alternatives.
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const double bkg_trim_frac = bkg_trim;
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auto grid_idx = [this](int dx, int dy) { return (dy + R) * G + (dx + R); };
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// --- Reflection mask: mark the r2 signal disk of every predicted reflection so a neighbour's
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// disk is excluded from this reflection's r2..r3 background ring. ---
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std::vector<uint8_t> refl_mask(npixel, 0);
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for (size_t i = 0; i < npredicted; ++i) {
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const auto &r = predicted[i];
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const int x0 = std::max(0, static_cast<int>(std::floor(r.predicted_x - r2 - 1.0f)));
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const int x1 = std::min(W - 1, static_cast<int>(std::ceil(r.predicted_x + r2 + 1.0f)));
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const int y0 = std::max(0, static_cast<int>(std::floor(r.predicted_y - r2 - 1.0f)));
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const int y1 = std::min(H - 1, static_cast<int>(std::ceil(r.predicted_y + r2 + 1.0f)));
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for (int y = y0; y <= y1; ++y)
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for (int x = x0; x <= x1; ++x) {
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const double d2 = (x - r.predicted_x) * (x - r.predicted_x) + (y - r.predicted_y) * (y - r.predicted_y);
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if (d2 < r2_sq) refl_mask[y * W + x] = 1;
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}
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}
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// --- Pass A: box-sum every reflection (rough I, background, centroid, strong flag). ---
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struct Rough {
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double I = 0.0, sigma = NAN, bkg = 0.0, obs_x = 0.0, obs_y = 0.0;
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double bkg_var = 0.0; // variance of the background ESTIMATE itself, bkg / n_bkg
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int64_t I_sum = 0; // kept so I can be rebuilt after the radial background correction
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int n_inner = 0;
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int r_bin = 0; // rounded distance from the beam centre, indexes the radial curve
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int cx = 0, cy = 0, shell = -1;
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bool ok = false, strong = false, has_obs = false;
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};
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std::vector<Rough> rough(npredicted);
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double inv_d2_min = std::numeric_limits<double>::max(), inv_d2_max = 0.0;
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std::vector<int32_t> bkg_vals; // reused per reflection for the trimmed-mean background (idea 1)
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// Radial background curve, accumulated from the annulus pixels this pass already reads. A pixel's
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// radius is the reflection's radius plus the pixel's projection on the beam->reflection direction,
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// so no per-pixel sqrt is needed.
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const int n_rad = static_cast<int>(std::ceil(std::hypot(std::max<double>(beam_x, W - beam_x),
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std::max<double>(beam_y, H - beam_y)))) + 2;
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std::vector<double> rad_sum;
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std::vector<int> rad_cnt;
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if (bkg_radial) {
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rad_sum.assign(n_rad, 0.0);
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rad_cnt.assign(n_rad, 0);
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}
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for (size_t i = 0; i < npredicted; ++i) {
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const auto &r = predicted[i];
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Rough out;
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const int x0 = std::max(0, static_cast<int>(std::floor(r.predicted_x - r3 - 1.0)));
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const int x1 = std::min(W - 1, static_cast<int>(std::ceil(r.predicted_x + r3 + 1.0)));
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const int y0 = std::max(0, static_cast<int>(std::floor(r.predicted_y - r3 - 1.0)));
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const int y1 = std::min(H - 1, static_cast<int>(std::ceil(r.predicted_y + r3 + 1.0)));
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// Unit vector beam -> reflection: a stencil pixel's radial offset is its projection on it.
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const double rx = r.predicted_x - beam_x, ry = r.predicted_y - beam_y;
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const double r0 = std::hypot(rx, ry);
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const double ux = r0 > 1e-6 ? rx / r0 : 1.0, uy = r0 > 1e-6 ? ry / r0 : 0.0;
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out.r_bin = std::clamp(static_cast<int>(std::lround(r0)), 0, n_rad - 1);
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int64_t I_sum = 0, I_sum_x = 0, I_sum_y = 0, n_inner = 0, n_inner_valid = 0;
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double bkg_sum = 0.0;
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int n_bkg = 0;
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bkg_vals.clear();
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for (int y = y0; y <= y1; ++y)
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for (int x = x0; x <= x1; ++x) {
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const double d2 = (x - r.predicted_x) * (x - r.predicted_x) + (y - r.predicted_y) * (y - r.predicted_y);
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const int32_t px = img[y * W + x];
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if (d2 < r1_sq) {
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++n_inner;
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if (!valid(px)) continue;
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I_sum += px;
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I_sum_x += static_cast<int64_t>(x) * px;
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I_sum_y += static_cast<int64_t>(y) * px;
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++n_inner_valid;
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} else if (d2 >= r2_sq && d2 < r3_sq) {
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if (refl_mask[y * W + x]) continue;
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if (!valid(px)) continue;
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bkg_sum += static_cast<double>(px);
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if (bkg_trim_frac > 0.0) bkg_vals.push_back(px);
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++n_bkg;
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}
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}
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int n_bkg_used = n_bkg; // pixels behind the FINAL background value (trim/clip shrink it)
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if (n_inner_valid == n_inner && n_bkg > 5) {
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out.bkg = bkg_sum / n_bkg;
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if (bkg_trim_frac > 0.0 && bkg_vals.size() > 5) {
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// Symmetric trimmed mean over the background ring (idea 1): drop the lowest and highest
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// bkg_trim_frac of the pixels, average the rest. Robust to the high-side contamination
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// that biases the plain ring mean and makes it over-subtract at high resolution.
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std::sort(bkg_vals.begin(), bkg_vals.end());
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const size_t lo = static_cast<size_t>(bkg_vals.size() * bkg_trim_frac);
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const size_t hi = bkg_vals.size() - lo;
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if (hi > lo) {
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double s = 0.0;
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for (size_t t = lo; t < hi; ++t) s += bkg_vals[t];
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out.bkg = s / static_cast<double>(hi - lo);
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n_bkg_used = static_cast<int>(hi - lo);
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}
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} else if (do_clip) {
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// One high-outlier sigma-clip pass on the background ring: reject pixels above
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// mean + n*sqrt(mean) to strip a neighbour core or a zinger that biases the mean.
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const double thr = out.bkg + bkg_clip_nsigma * std::sqrt(std::max(out.bkg, 1.0));
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double s = 0.0;
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int n = 0;
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for (int y = y0; y <= y1; ++y)
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for (int x = x0; x <= x1; ++x) {
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const double d2 = (x - r.predicted_x) * (x - r.predicted_x) + (y - r.predicted_y) * (y - r.predicted_y);
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if (!(d2 >= r2_sq && d2 < r3_sq)) continue;
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if (refl_mask[y * W + x]) continue;
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const int32_t px = img[y * W + x];
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if (!valid(px)) continue;
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if (static_cast<double>(px) <= thr) {
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s += px;
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++n;
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if (bkg_radial) {
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const double off = (x - r.predicted_x) * ux + (y - r.predicted_y) * uy;
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const int b = std::clamp(static_cast<int>(std::lround(r0 + off)), 0, n_rad - 1);
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rad_sum[b] += static_cast<double>(px);
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++rad_cnt[b];
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}
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}
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}
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if (n > 5) { out.bkg = s / n; n_bkg_used = n; }
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}
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out.I = static_cast<double>(I_sum) - static_cast<double>(n_inner) * out.bkg;
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// I = I_sum - n_inner*bkg, and bkg is itself estimated from n_bkg_used pixels, so its
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// error enters n_inner times over: var(I) = I_sum + n_inner^2 * bkg/n_bkg_used. Leaving
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// the second term out understates sigma by sqrt(1 + n_inner/n_bkg) - 1.109x at the
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// default r1=4/r2=6/r3=10 stencil, on every reflection of every dataset.
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out.bkg_var = out.bkg / n_bkg_used;
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out.I_sum = I_sum;
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out.n_inner = static_cast<int>(n_inner);
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const double var_bkg_term = static_cast<double>(n_inner) * n_inner * out.bkg_var;
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out.sigma = std::max(1.0, out.I * min_sigma_ratio);
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if (I_sum > 0) {
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out.sigma = std::max(out.sigma, std::sqrt(static_cast<double>(I_sum) + var_bkg_term));
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out.obs_x = static_cast<double>(I_sum_x) / static_cast<double>(I_sum);
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out.obs_y = static_cast<double>(I_sum_y) / static_cast<double>(I_sum);
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out.has_obs = true;
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}
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out.cx = static_cast<int>(std::lround(r.predicted_x));
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out.cy = static_cast<int>(std::lround(r.predicted_y));
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out.ok = true;
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out.strong = out.sigma > 0.0 && out.I / out.sigma >= STRONG_I_OVER_SIGMA;
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if (r.d > 0.0f) {
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const double inv_d2 = 1.0 / (static_cast<double>(r.d) * r.d);
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inv_d2_min = std::min(inv_d2_min, inv_d2);
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inv_d2_max = std::max(inv_d2_max, inv_d2);
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}
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}
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rough[i] = out;
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}
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// --- Radial background curvature correction. The annulus mean is blind to the curvature of the
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// radial background (a linear background cancels between the concentric disk and annulus), so
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// correct it by mean_annulus(B) - mean_disk(B) taken from the curve just accumulated. Reads no
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// pixels: one short dot product per reflection. ---
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if (bkg_radial) {
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for (size_t i = 0; i < npredicted; ++i) {
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auto &rh = rough[i];
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if (!rh.ok) continue;
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// An empty bin contributes the reflection's own background, so a fully empty
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// neighbourhood gives corr == 0 exactly (the kernel weights sum to zero).
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double corr = 0.0;
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for (int k = 0; k < static_cast<int>(k_diff.size()); ++k) {
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const int b = std::clamp(rh.r_bin + k - k_off, 0, n_rad - 1);
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const double v = rad_cnt[b] > 0 ? rad_sum[b] / rad_cnt[b] : rh.bkg;
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corr += static_cast<double>(k_diff[k]) * v;
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}
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rh.bkg -= corr; // annulus mean -> mean over the signal disk
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rh.I = static_cast<double>(rh.I_sum) - static_cast<double>(rh.n_inner) * rh.bkg;
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}
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}
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// --- BoxSum mode is BraggIntegrate2D: emit the rough result directly. ---
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if (mode == IntegratorMode::BoxSum) {
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for (size_t i = 0; i < npredicted; ++i) {
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const auto &rh = rough[i];
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if (!rh.ok) continue;
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results[i] = {static_cast<float>(rh.I), static_cast<float>(rh.sigma), static_cast<float>(rh.bkg),
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static_cast<float>(rh.obs_x), static_cast<float>(rh.obs_y), true, rh.has_obs};
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}
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return Finalize(predicted, npredicted, results, image_number);
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}
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auto shell_of = [&](float d) {
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if (!(d > 0.0f) || inv_d2_max <= inv_d2_min) return 0;
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const double t = (1.0 / (static_cast<double>(d) * d) - inv_d2_min) / (inv_d2_max - inv_d2_min);
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return std::clamp(static_cast<int>(t * N_SHELL), 0, N_SHELL - 1);
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};
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for (size_t i = 0; i < npredicted; ++i)
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if (rough[i].ok) rough[i].shell = shell_of(predicted[i].d);
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// --- Learn the profile per shell (+ global) from the strong spots. ---
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std::vector<std::vector<double>> shell_grid(N_SHELL, std::vector<double>(GG, 0.0));
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std::vector<int> shell_n(N_SHELL, 0);
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std::vector<double> global_grid(GG, 0.0);
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int global_n = 0;
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for (size_t i = 0; i < npredicted; ++i) {
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const auto &rh = rough[i];
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if (!rh.ok || !rh.strong || rh.I <= 0.0) continue;
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for (int dy = -R; dy <= R; ++dy)
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for (int dx = -R; dx <= R; ++dx) {
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const int x = rh.cx + dx, y = rh.cy + dy;
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if (x < 0 || y < 0 || x >= W || y >= H) continue;
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const int32_t px = img[y * W + x];
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if (!valid(px)) continue;
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const double v = (static_cast<double>(px) - rh.bkg) / rh.I;
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shell_grid[rh.shell][grid_idx(dx, dy)] += v;
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global_grid[grid_idx(dx, dy)] += v;
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}
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++shell_n[rh.shell];
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++global_n;
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}
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// Isotropic width (2nd moment) of a learned grid: over the r1 disk (monochromatic) or the full
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// grid (broadband); <r^2> = 2 sigma^2 in 2D.
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auto measure_sigma2 = [&](const std::vector<double> &grid) {
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double m2 = 0.0, m2w = 0.0;
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for (int dy = -R; dy <= R; ++dy)
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for (int dx = -R; dx <= R; ++dx) {
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if (!broadband && dx * dx + dy * dy >= r1_sq) continue;
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const double g = std::max(0.0, grid[grid_idx(dx, dy)]);
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m2 += g * (dx * dx + dy * dy);
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m2w += g;
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}
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return m2w > 0.0 ? std::max(0.25, (m2 / m2w) / 2.0) : 1.0;
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};
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// Normalised profile (sum = 1): empirical average grid, or an isotropic Gaussian of the measured
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// 2nd moment (only used by ProfileEmpirical; ProfileGaussian rebuilds per reflection in Pass B).
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auto build_profile = [&](const std::vector<double> &grid, int n) {
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std::vector<double> P(GG, 0.0);
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if (n <= 0) return P;
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double sum = 0.0;
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for (int k = 0; k < GG; ++k) {
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const double g = std::max(0.0, grid[k]);
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sum += g;
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if (empirical) P[k] = g;
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}
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if (sum <= 0.0) return P;
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if (empirical) {
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for (double &p : P) p /= sum;
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} else {
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const double sigma2 = measure_sigma2(grid);
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double gsum = 0.0;
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for (int dy = -R; dy <= R; ++dy)
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for (int dx = -R; dx <= R; ++dx) {
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const double g = std::exp(-(dx * dx + dy * dy) / (2.0 * sigma2));
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P[grid_idx(dx, dy)] = g;
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gsum += g;
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}
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for (double &p : P) p /= gsum;
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}
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return P;
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};
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const std::vector<double> global_P = build_profile(global_grid, global_n);
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const double global_sigma2 = global_n > 0 ? measure_sigma2(global_grid) : 1.0;
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std::vector<std::vector<double>> shell_P(N_SHELL);
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std::vector<double> shell_sigma2(N_SHELL, global_sigma2);
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for (int s = 0; s < N_SHELL; ++s) {
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if (shell_n[s] >= MIN_STRONG_PER_SHELL) {
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shell_P[s] = build_profile(shell_grid[s], shell_n[s]);
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shell_sigma2[s] = measure_sigma2(shell_grid[s]);
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} else {
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shell_P[s] = global_P;
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}
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}
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// --- Pass B: profile-fit each reflection (Kabsch, de-biased variance v = B + I*P; iterate). ---
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std::vector<double> Pbuf;
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for (size_t i = 0; i < npredicted; ++i) {
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const auto &rh = rough[i];
|
|
if (!rh.ok) continue;
|
|
const int sh = rh.shell < 0 ? 0 : rh.shell;
|
|
|
|
int Rf = R;
|
|
const std::vector<double> *Pvec = &shell_P[sh];
|
|
if (!empirical) {
|
|
const double rx = predicted[i].predicted_x - beam_x, ry = predicted[i].predicted_y - beam_y;
|
|
const double Rpx = std::hypot(rx, ry);
|
|
const double tan2t = Rpx / F_px;
|
|
const double s2t = shell_sigma2[sh];
|
|
double s2r = s2t, ux = 1.0, uy = 0.0;
|
|
bool elong = false;
|
|
if (use_ellipse) {
|
|
const double sbw = bw_sigma * Rpx;
|
|
const double radial_extra = sbw * sbw + c_radial * tan2t * tan2t;
|
|
if (Rpx > 1e-6 && radial_extra > 0.25) {
|
|
ux = rx / Rpx; uy = ry / Rpx;
|
|
s2r = s2t + radial_extra;
|
|
elong = true;
|
|
}
|
|
}
|
|
// Build the Gaussian per reflection, centred on the sub-pixel predicted position and (when
|
|
// needed) radially elongated, on a grid grown to hold the streak.
|
|
const double fx = predicted[i].predicted_x - rh.cx, fy = predicted[i].predicted_y - rh.cy;
|
|
Rf = elong ? std::min(3 * R, static_cast<int>(std::ceil(r2 + 2.0 * std::sqrt(s2r)))) : R;
|
|
const int Gf = 2 * Rf + 1;
|
|
Pbuf.assign(static_cast<size_t>(Gf) * Gf, 0.0);
|
|
double gs = 0.0;
|
|
for (int dy = -Rf; dy <= Rf; ++dy)
|
|
for (int dx = -Rf; dx <= Rf; ++dx) {
|
|
const double ex = dx - fx, ey = dy - fy;
|
|
const double rad = ex * ux + ey * uy, tn = -ex * uy + ey * ux;
|
|
const double g = std::exp(-rad * rad / (2.0 * s2r) - tn * tn / (2.0 * s2t));
|
|
Pbuf[(dy + Rf) * Gf + (dx + Rf)] = g;
|
|
gs += g;
|
|
}
|
|
for (double &p : Pbuf) p /= gs;
|
|
Pvec = &Pbuf;
|
|
}
|
|
|
|
const int Gf = 2 * Rf + 1;
|
|
const double B = std::max(rh.bkg, PIXEL_VARIANCE_FLOOR);
|
|
double I = rh.I, den = 0.0, wsum = 0.0;
|
|
for (int iter = 0; iter < 4; ++iter) {
|
|
double num = 0.0;
|
|
den = 0.0;
|
|
wsum = 0.0;
|
|
for (int dy = -Rf; dy <= Rf; ++dy)
|
|
for (int dx = -Rf; dx <= Rf; ++dx) {
|
|
const double Pp = (*Pvec)[(dy + Rf) * Gf + (dx + Rf)];
|
|
if (Pp <= 0.0) continue;
|
|
const int x = rh.cx + dx, y = rh.cy + dy;
|
|
if (x < 0 || y < 0 || x >= W || y >= H) continue;
|
|
const int32_t px = img[y * W + x];
|
|
if (!valid(px)) continue;
|
|
const double v = B + std::max(0.0, I) * Pp;
|
|
num += Pp * (static_cast<double>(px) - rh.bkg) / v;
|
|
den += Pp * Pp / v;
|
|
wsum += Pp / v;
|
|
}
|
|
if (den > 0.0) I = num / den; else break;
|
|
}
|
|
if (!(den > 0.0)) continue;
|
|
|
|
// Guard against profile-fit runaways: on a weak / near-zero reflection the reweighted Kabsch
|
|
// iteration has no real peak to lock onto and can manufacture intensity the box sum never sees.
|
|
// Keep the profile intensity only if it agrees with the summation seed within the margin;
|
|
// otherwise fall back to the summation, which is robust there.
|
|
// 1/den is the profile-fit variance with the background taken as exact. The fit is
|
|
// I = sum(P*(px-bkg)/v) / sum(P^2/v), so dI/dbkg = -wsum/den and the background estimate's
|
|
// own error adds (wsum/den)^2 * var(bkg) - the same term the box sum was missing.
|
|
double sigma = std::sqrt(1.0 / den + (wsum / den) * (wsum / den) * rh.bkg_var);
|
|
if (std::abs(I - rh.I) > PROFILE_SUMMATION_MAX_NSIGMA * rh.sigma) {
|
|
I = rh.I;
|
|
sigma = rh.sigma;
|
|
}
|
|
// Carry the Pass-A box-sum intensity-weighted centroid (observed spot position) through the
|
|
// profile path too - post-refinement uses it as the observed position (beam-centre / distance).
|
|
results[i] = {static_cast<float>(I), static_cast<float>(sigma),
|
|
static_cast<float>(rh.bkg),
|
|
static_cast<float>(rh.obs_x), static_cast<float>(rh.obs_y), true, rh.has_obs};
|
|
}
|
|
|
|
return Finalize(predicted, npredicted, results, image_number);
|
|
}
|
|
|
|
std::vector<Reflection> BraggIntegrationEngineCPU::Run(const ImagePreprocessorBuffer &image,
|
|
const std::vector<Reflection> &predicted,
|
|
size_t npredicted, int64_t image_number) {
|
|
if (image.size() != npixel)
|
|
return Finalize(predicted, npredicted, std::vector<BraggFitResult>(npredicted), image_number);
|
|
return RunImpl(BufferSampler{image.data()}, predicted, npredicted, image_number);
|
|
}
|
|
|
|
std::vector<Reflection> BraggIntegrationEngineCPU::Run(const CompressedImage &image,
|
|
const std::vector<Reflection> &predicted,
|
|
size_t npredicted, int64_t image_number) {
|
|
if (image.GetWidth() * image.GetHeight() != npixel)
|
|
return Finalize(predicted, npredicted, std::vector<BraggFitResult>(npredicted), image_number);
|
|
|
|
std::vector<uint8_t> scratch;
|
|
const auto *ptr = image.GetUncompressedPtr(scratch);
|
|
switch (image.GetMode()) {
|
|
case CompressedImageMode::Int8:
|
|
return RunImpl(ImageSampler<int8_t>{reinterpret_cast<const int8_t *>(ptr), INT8_MIN, INT8_MAX},
|
|
predicted, npredicted, image_number);
|
|
case CompressedImageMode::Int16:
|
|
return RunImpl(ImageSampler<int16_t>{reinterpret_cast<const int16_t *>(ptr), INT16_MIN, INT16_MAX},
|
|
predicted, npredicted, image_number);
|
|
case CompressedImageMode::Int32:
|
|
return RunImpl(ImageSampler<int32_t>{reinterpret_cast<const int32_t *>(ptr), INT32_MIN, INT32_MAX},
|
|
predicted, npredicted, image_number);
|
|
case CompressedImageMode::Uint8:
|
|
return RunImpl(ImageSampler<uint8_t>{reinterpret_cast<const uint8_t *>(ptr), UINT8_MAX, UINT8_MAX},
|
|
predicted, npredicted, image_number);
|
|
case CompressedImageMode::Uint16:
|
|
return RunImpl(ImageSampler<uint16_t>{reinterpret_cast<const uint16_t *>(ptr), UINT16_MAX, UINT16_MAX},
|
|
predicted, npredicted, image_number);
|
|
case CompressedImageMode::Uint32:
|
|
return RunImpl(ImageSampler<uint32_t>{reinterpret_cast<const uint32_t *>(ptr), UINT32_MAX, UINT32_MAX},
|
|
predicted, npredicted, image_number);
|
|
default:
|
|
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Image mode not supported");
|
|
}
|
|
}
|