Jungfraujoch/image_analysis/ImageAnalysisCPU.cpp

// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only

#include "ImageAnalysisCPU.h"

#include "StrongPixelSet.h"
#include "../compression/JFJochDecompress.h"
#include "indexing/IndexerFactory.h"

ImageAnalysisCPU::ImageAnalysisCPU(const DiffractionExperiment &in_experiment,
                                   const AzimuthalIntegration &in_integration,
                                   const PixelMask &in_mask)
        : experiment(in_experiment),
          integration(in_integration),
          npixels(experiment.GetPixelsNum()),
          xpixels(experiment.GetXPixelsNum()),
          mask_1byte(npixels, 0),
          spotFinder(in_integration),
          saturation_limit(experiment.GetSaturationLimit()),
          integrate(in_experiment),
          roi_count(0) {

    nquads = 2;

    UpdateROI();

    for (int i = 0; i < npixels; i++)
        mask_1byte[i] = (in_mask.GetMask().at(i) != 0);

    indexer = CreateIndexer(experiment);
}

void ImageAnalysisCPU::UpdateROI() {
    roi_map = experiment.ExportROIMap();
    roi_count = experiment.ROI().size();
    roi_names = experiment.ROI().GetROINameMap();
}

void ImageAnalysisCPU::Analyze(DataMessage &output, std::vector<uint8_t> &image, AzimuthalIntegrationProfile &profile, const SpotFindingSettings &spot_finding_settings) {
    if ((output.image.GetWidth() != xpixels)
        || (output.image.GetWidth() * output.image.GetHeight() != npixels))
        throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
                              "Mismatch in pixel size");

    const uint8_t *image_ptr = output.image.GetUncompressedPtr(image);

    switch (output.image.GetMode()) {
        case CompressedImageMode::Int8:
            Analyze<int8_t>(output, image_ptr, INT8_MIN, INT8_MAX, profile, spot_finding_settings);
            break;
        case CompressedImageMode::Int16:
            Analyze<int16_t>(output, image_ptr, INT16_MIN, INT16_MAX, profile, spot_finding_settings);
            break;
        case CompressedImageMode::Int32:
            Analyze<int32_t>(output, image_ptr, INT32_MIN, INT32_MAX, profile, spot_finding_settings);
            break;
        case CompressedImageMode::Uint8:
            Analyze<uint8_t>(output, image_ptr, UINT8_MAX, UINT8_MAX, profile, spot_finding_settings);
            break;
        case CompressedImageMode::Uint16:
            Analyze<uint16_t>(output, image_ptr, UINT16_MAX, UINT16_MAX, profile, spot_finding_settings);
            break;
        case CompressedImageMode::Uint32:
            Analyze<uint32_t>(output, image_ptr, UINT32_MAX, UINT32_MAX, profile, spot_finding_settings);
            break;
        default:
            throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "RGB/float mode not supported");
    }
}

template <class T>
void ImageAnalysisCPU::Analyze(DataMessage &output,
                               const uint8_t *in_image,
                               T err_pixel_val,
                               T sat_pixel_val,
                               AzimuthalIntegrationProfile &profile,
                               const SpotFindingSettings &spot_finding_settings) {

    auto image = (T *) in_image;

    std::vector<ROIMessage> roi(roi_count);

    size_t err_pixels = 0;
    size_t masked_pixels = 0;
    size_t sat_pixels = 0;
    int64_t min_value = INT64_MAX;
    int64_t max_value = INT64_MIN;

    if (sat_pixel_val > saturation_limit)
        sat_pixel_val = static_cast<T>(saturation_limit);

    auto &pixel_to_bin = integration.GetPixelToBin();
    auto &corrections = integration.Corrections();
    auto nbins = integration.GetBinNumber();

    std::vector<int32_t> updated_image(experiment.GetPixelsNum());

    std::vector<float> sum(nbins);
    std::vector<float> sum2(nbins);
    std::vector<uint32_t> count(nbins);

    for (int i = 0; i < npixels; i++) {
        auto bin = pixel_to_bin[i];
        auto value = image[i] * corrections[i];

        if (mask_1byte[i] != 0) {
            updated_image[i] = INT32_MIN;
            ++masked_pixels;
        } else if (image[i] >= sat_pixel_val) {
            updated_image[i] = INT32_MIN;
            ++sat_pixels;
        } else if (std::is_signed<T>::value && (image[i] == err_pixel_val)) {// Error pixels are possible only for signed types
            updated_image[i] = INT32_MIN;
            ++err_pixels;
        } else {
            updated_image[i] = static_cast<int32_t>(image[i]);

            if (image[i] > max_value)
                max_value = image[i];
            if (image[i] < min_value)
                min_value = image[i];

            if (roi_count > 0 && (roi_map[i] != 0)) {
                int64_t x = i % xpixels;
                int64_t y = i / xpixels;

                for (int8_t r = 0; r < roi_count; r++) {
                    if ((roi_map[i] & (1<<r)) != 0) {
                        roi[r].sum += image[i];
                        roi[r].sum_square += image[i] * image[i];
                        roi[r].pixels += 1;
                        if (image[i] > roi[r].max_count)
                            roi[r].max_count = image[i];
                        roi[r].x_weighted += x * image[i];
                        roi[r].y_weighted += y * image[i];
                    }
                }
            }
            if (bin < nbins) {
                sum[bin] += value;
                sum2[bin] += value * value;
                count[bin] += 1;
            }
        }
    }

    profile.Clear(integration);
    profile.Add(sum, count);

    std::vector<DiffractionSpot> spots;
    if (spot_finding_settings.enable)
        spots = spotFinder.Run(image, spot_finding_settings);

    std::vector<DiffractionSpot> spots_out;
    FilterSpotsByCount(max_spot_count, spots, spots_out);

    for (const auto &spot: spots_out)
        output.spots.push_back(spot);

    if (indexer && spot_finding_settings.indexing) {
        auto latt = indexer->Run(output, spots_out);
        if (latt && spot_finding_settings.quick_integration) {
            auto res = integrate.Integrate(
                    CompressedImage(updated_image, experiment.GetXPixelsNum(),
                    experiment.GetYPixelsNum()), latt.value(), spot_finding_settings.quick_integration_d_min_A);
            output.reflections = res.reflections;
            output.b_factor = res.b_factor;
        }
    }

    output.max_viable_pixel_value = max_value;
    output.min_viable_pixel_value = min_value;
    output.error_pixel_count = err_pixels;
    output.saturated_pixel_count = sat_pixels;
    output.az_int_profile = profile.GetResult();
    output.bkg_estimate = profile.GetBkgEstimate(integration.Settings());
    if ((inference_client != nullptr) && spot_finding_settings.resolution_estimate)
        output.resolution_estimate = inference_client->Inference(experiment, image, nquads);

    for (const auto &[key, val]: roi_names)
        output.roi[key] = roi[val];
}

ImageAnalysisCPU &ImageAnalysisCPU::NeuralNetInference(NeuralNetInferenceClient *client) {
    inference_client = client;
    return *this;
}