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@@ -0,0 +1,277 @@
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+#include <iostream>
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+#include <string.h>
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+#include <memory>
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+
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+#include "lodepng.h"
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+#include "rawls.h"
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+
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+#include <algorithm>
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+#include <filesystem>
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+#include <regex>
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+
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+// number of means expected
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+const unsigned numberOfMeans = 20;
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+
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+void writeProgress(float progress, bool moveUp = false){
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+ int barWidth = 200;
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+
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+ if (moveUp){
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+ // move up line
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+ std::cout << "\e[A";
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+ std::cout.flush();
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+ }
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+
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+ std::cout << "[";
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+ int pos = barWidth * progress;
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+ for (int i = 0; i < barWidth; ++i) {
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+ if (i < pos) std::cout << "=";
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+ else if (i == pos) std::cout << ">";
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+ else std::cout << " ";
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+ }
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+ std::cout << "] " << int(progress * 100.0) << " %\r";
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+ std::cout.flush();
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+}
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+
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+void insertSample(unsigned* occurences, float* values, float value){
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+
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+ /* generate secret number: */
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+ unsigned foundIndex = rand() % numberOfMeans;
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+
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+ values[foundIndex] += value;
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+ occurences[foundIndex] += 1;
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+}
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+
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+/*
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+ * Compute array of means and sort values
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+ */
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+float* prepareMeans(unsigned* occurences, float* values){
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+
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+ float* means = new float[numberOfMeans];
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+
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+ for (int i = 0; i < numberOfMeans; i++){
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+ means[i] = values[i] / occurences[i];
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+ }
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+
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+ std::sort(means, means + numberOfMeans, std::less<float>());
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+
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+ return means;
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+}
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+
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+/*
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+ * Returns median value from array of values
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+ */
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+float getMedianValue(float values[]){
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+
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+ if (numberOfMeans % 2 == 0)
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+ {
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+ return (values[numberOfMeans / 2 - 1] + values[numberOfMeans / 2]) / 2;
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+ }
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+ else
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+ {
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+ return values[numberOfMeans / 2];
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+ }
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+}
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+
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+/*
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+ * Save current step images from current buffer
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+ */
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+bool saveCurrentImage(int width, int height, int nbChanels, float* buffer, std::string outfileName, std::string comments){
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+
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+ // create outfile
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+ if (rawls::HasExtension(outfileName, ".ppm")){
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+ rawls::saveAsPPM(width, height, nbChanels, buffer, outfileName);
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+ }
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+ else if (rawls::HasExtension(outfileName, ".png")){
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+ rawls::saveAsPNG(width, height, nbChanels, buffer, outfileName);
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+ }
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+ else if (rawls::HasExtension(outfileName, ".rawls") || rawls::HasExtension(outfileName, ".rawls_20")){
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+
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+ // Here no gamma conversion is done, only mean of samples
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+ rawls::saveAsRAWLS(width, height, nbChanels, comments, buffer, outfileName);
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+ }
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+ else{
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+ std::cout << "Unexpected output extension image" << std::endl;
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+ return false;
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+ }
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+
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+ return true;
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+}
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+
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+/*
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+ * Incremental merge of `rawls` images using `median-of-means`
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+ */
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+int main(int argc, char *argv[]){
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+
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+ /* initialize random seed: */
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+ srand ( time(NULL) );
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+
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+ std::string folderName;
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+ std::string outputFolder;
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+ std::string prefixImageName;
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+ std::string imageExtension;
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+
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+ unsigned step = 10;
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+ unsigned maxSamples = 0;
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+ bool random;
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+
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+ for (int i = 1; i < argc; ++i) {
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+ if (!strcmp(argv[i], "--folder") || !strcmp(argv[i], "-folder")) {
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+ folderName = argv[++i];
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+ } else if (!strcmp(argv[i], "--step") || !strcmp(argv[i], "-step")) {
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+ step = atoi(argv[++i]);
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+ }else if (!strcmp(argv[i], "--random") || !strcmp(argv[i], "-random")) {
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+ random = bool(atoi(argv[++i]));
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+ }else if (!strcmp(argv[i], "--output") || !strcmp(argv[i], "-output")) {
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+ outputFolder = argv[++i];
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+ }else if (!strcmp(argv[i], "--prefix") || !strcmp(argv[i], "-prefix")) {
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+ prefixImageName = argv[++i];
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+ }else if (!strcmp(argv[i], "--max") || !strcmp(argv[i], "-max")) {
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+ maxSamples = atoi(argv[++i]);
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+ }else if (!strcmp(argv[i], "--extension") || !strcmp(argv[i], "-extension")) {
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+ imageExtension = argv[++i];
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+ }
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+ }
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+
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+ std::vector<std::string> imagesPath;
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+
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+ for (const auto & entry : std::filesystem::directory_iterator(folderName)){
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+ std::string imageName = entry.path().string();
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+ if (rawls::HasExtension(imageName, ".rawls") || rawls::HasExtension(imageName, ".rawls_20")){
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+ imagesPath.push_back(imageName);
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+ }
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+ }
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+
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+ // sort or shuffle the images path
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+ if (!random){
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+ std::sort(imagesPath.begin(), imagesPath.end(), std::less<std::string>());
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+ }else{
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+ std::random_shuffle(imagesPath.begin(), imagesPath.end());
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+ }
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+
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+ unsigned width, height, nbChanels;
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+
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+ float** sumBuffer; // stores sum array for each sample
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+ unsigned** occurencesMeanBuffer;
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+ float* outputStepBuffer; // buffer which stores kept median for each generated image (median is found using `outputMeanBuffer`)
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+
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+ if (imagesPath.size() > 0){
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+
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+ std::tuple<unsigned, unsigned, unsigned> dimensions = rawls::getDimensionsRAWLS(imagesPath.at(0));
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+
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+ width = std::get<0>(dimensions);
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+ height = std::get<1>(dimensions);
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+ nbChanels = std::get<2>(dimensions);
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+
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+ // init all pointers size
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+ sumBuffer = new float*[width * height * nbChanels];
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+ occurencesMeanBuffer = new unsigned*[width * height * nbChanels];
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+ outputStepBuffer = new float[width * height * nbChanels];
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+
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+ // init values of buffer
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+ for (int i = 0; i < height * width * nbChanels; i++){
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+ // define array size and initialization
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+ sumBuffer[i] = new float[numberOfMeans];
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+ occurencesMeanBuffer[i] = new unsigned[numberOfMeans];
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+
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+ for (int j = 0; j < numberOfMeans; j++){
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+ sumBuffer[i][j] = 0;
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+ occurencesMeanBuffer[i][j] = 0;
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+ }
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+
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+ outputStepBuffer[i] = 0;
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+ }
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+ }
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+ else
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+ {
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+ std::cout << "Folder is empty..." << std::endl;
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+ return 1;
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+ }
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+
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+ // just for indication
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+ float progress = 0.0;
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+ unsigned bufferSize = width * height * nbChanels;
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+ std::string comments;
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+
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+ // get comments if output is also `rawls` file
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+ if (rawls::HasExtension(imageExtension, "rawls")){
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+ comments = rawls::getCommentsRAWLS(imagesPath.at(0));
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+ }
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+
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+ for (unsigned i = 0; i < maxSamples; i++){
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+
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+ unsigned currentSample = i + 1;
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+
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+ // read into folder all `.rawls` file and merge pixels values
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+ float* buffer = rawls::getPixelsRAWLS(imagesPath.at(i));
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+
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+ for(unsigned y = 0; y < height; y++){
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+
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+ for(unsigned x = 0; x < width; x++) {
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+
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+ for(unsigned j = 0; j < nbChanels; j++){
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+
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+ unsigned currentIndex = nbChanels * width * y + nbChanels * x + j;
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+
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+ float value = buffer[currentIndex];
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+
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+ // add new `luminance` of chanel[j] found randomly (uniformly) into means array
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+ insertSample(occurencesMeanBuffer[currentIndex], sumBuffer[currentIndex], value);
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+ }
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+ }
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+ }
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+
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+ // save a new
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+ if (currentSample % step == 0){
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+
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+ float currentMean;
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+
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+ // get median all samples values by number of samples used (using MON method)
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+ for (int j = 0; j < height * width * nbChanels; j++){
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+
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+ float* means = prepareMeans(occurencesMeanBuffer[j], sumBuffer[j]);
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+
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+ // get meadian of these means as expected output luminance
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+ outputStepBuffer[j] = getMedianValue(means);
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+
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+ // remove pointer values
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+ delete means;
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+ }
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+
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+ // add suffix with `5` digits
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+ std::string suffix = std::to_string(currentSample);
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+
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+ while(suffix.length() < 5){
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+ suffix = "0" + suffix;
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+ }
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+
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+ // build output path of image
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+ std::string outfileName = outputFolder + "/" + prefixImageName + "_" + suffix + "." + imageExtension;
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+ outfileName = std::regex_replace(outfileName, std::regex("\\//"), "/"); // fix path
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+
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+ // save the expected `step` image using built outpath
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+ saveCurrentImage(width, height, nbChanels, outputStepBuffer, outfileName, comments);
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+
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+ // just for progress information with erasing previous info
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+ writeProgress(progress, true);
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+ }
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+
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+ // update and write progress information
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+ progress += (1 / (float)maxSamples);
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+ writeProgress(progress);
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+
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+ delete buffer;
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+ }
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+
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+ writeProgress(1.);
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+ std::cout << std::endl;
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+
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+ // clear all pointers memory
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+ for (int j = 0; j < height * width * nbChanels; j++){
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+ delete[] sumBuffer[j];
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+ delete[] occurencesMeanBuffer[j];
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+ }
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+
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+ delete sumBuffer;
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+ delete occurencesMeanBuffer;
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+ delete outputStepBuffer;
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+}
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