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Merge pull request #27051 from gursimarsingh:move_ccm_to_photo_module
Adding color correction module to photo module from opencv_contrib #27051 This PR moved color correction module from opencv_contrib to main repo inside photo module. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Gursimar Singh
GitHub
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//! [tutorial]
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#include <opencv2/core.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/photo.hpp>
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#include <opencv2/objdetect.hpp>
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#include <opencv2/dnn.hpp>
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#include <iostream>
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#include "../dnn/common.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::dnn;
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using namespace cv::ccm;
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using namespace mcc;
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const string about =
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"This sample detects Macbeth color checker using DNN or thresholding and applies color correction."
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"To run default:\n"
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"\t ./example_cpp_color_correction_model --input=path/to/your/input/image --query=path/to/your/query/image\n"
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"With DNN model:\n"
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"\t ./example_cpp_color_correction_model mcc --input=path/to/your/input/image --query=path/to/your/query/image\n\n"
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"Using pre-computed CCM:\n"
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"\t ./example_cpp_color_correction_model mcc --ccm_file=path/to/ccm_output.yaml --query=path/to/your/query/image\n\n"
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"Model path can also be specified using --model argument. And config path can be specified using --config. Download it using python download_models.py mcc from dnn samples directory\n\n";
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const string param_keys =
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"{ help h | | Print help message. }"
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"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
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"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
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"{ input i | mcc_ccm_test.jpg | Path to input image for computing CCM.}"
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"{ query q | baboon.jpg | Path to query image to apply color correction. If not provided, input image will be used. }"
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"{ type | 0 | chartType: 0-Standard, 1-DigitalSG, 2-Vinyl }"
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"{ num_charts | 1 | Maximum number of charts in the image }"
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"{ ccm_file | | Path to YAML file containing pre-computed CCM parameters}";
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const string backend_keys = format(
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"{ backend | default | Choose one of computation backends: "
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"default: automatically (by default), "
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"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA, "
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"webnn: WebNN }");
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const string target_keys = format(
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"{ target | cpu | Choose one of target computation devices: "
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"cpu: CPU target (by default), "
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"opencl: OpenCL, "
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"opencl_fp16: OpenCL fp16 (half-float precision), "
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"vpu: VPU, "
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"vulkan: Vulkan, "
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"cuda: CUDA, "
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"cuda_fp16: CUDA fp16 (half-float preprocess) }");
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string keys = param_keys + backend_keys + target_keys;
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static bool processFrame(const Mat& frame, Ptr<CCheckerDetector> detector, Mat& src, int nc){
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if (!detector->process(frame, nc))
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{
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return false;
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}
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vector<Ptr<CChecker>> checkers = detector->getListColorChecker();
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src = checkers[0]->getChartsRGB(false);
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return true;
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}
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int main(int argc, char* argv[]) {
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CommandLineParser parser(argc, argv, keys);
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parser.about(about);
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if (parser.has("help")) {
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cout << about << endl;
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parser.printMessage();
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return 0;
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}
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string modelName = parser.get<String>("@alias");
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string zooFile = parser.get<String>("zoo");
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const char* path = getenv("OPENCV_SAMPLES_DATA_PATH");
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if ((path != NULL) || parser.has("@alias")) {
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zooFile = findFile(zooFile);
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}
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else{
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cout<<"[WARN] set the environment variables or pass the arguments --model, --config and models.yml file using --zoo for using dnn based detector. Continuing with default detector.\n\n";
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}
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keys += genPreprocArguments(modelName, zooFile);
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parser = CommandLineParser(argc, argv, keys);
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int t = parser.get<int>("type");
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if (t < 0 || t > 2)
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{
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cout << "Error: --type must be 0, 1 or 2" << endl;
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parser.printMessage(); // prints full usage
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return -1;
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}
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ColorChart chartType = ColorChart(t);
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const string sha1 = parser.get<String>("sha1");
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const string modelPath = findModel(parser.get<string>("model"), sha1);
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const string config_sha1 = parser.get<String>("config_sha1");
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const string configPath = findModel(parser.get<string>("config"), config_sha1);
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const string backend = parser.get<String>("backend");
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const string target = parser.get<String>("target");
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int nc = parser.get<int>("num_charts");
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// Get input and target image paths
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const string inputFile = parser.get<String>("input");
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const string queryFile = parser.get<String>("query");
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const string ccmFile = parser.get<String>("ccm_file");
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if (!ccmFile.empty()) {
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// When ccm_file is provided, only query is required
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if (queryFile.empty()) {
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cout << "Error: Query image path must be provided when using pre-computed CCM." << endl;
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parser.printMessage();
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return -1;
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}
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} else {
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// Original validation for when computing new CCM
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if (inputFile.empty()) {
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cout << "Error: Input image path must be provided." << endl;
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parser.printMessage();
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return -1;
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}
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}
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ColorCorrectionModel model;
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Mat queryImage;
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if (!ccmFile.empty()) {
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// Load CCM from YAML file
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FileStorage fs(ccmFile, FileStorage::READ);
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if (!fs.isOpened()) {
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cout << "Error: Unable to open CCM file: " << ccmFile << endl;
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return -1;
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}
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model.read(fs["ColorCorrectionModel"]);
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fs.release();
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cout << "Loaded CCM from file: " << ccmFile << endl;
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// Read query image when using pre-computed CCM
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queryImage = imread(findFile(queryFile));
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if (queryImage.empty()) {
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cout << "Error: Unable to read query image." << endl;
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return -1;
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}
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} else {
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// Read input image for computing new CCM
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Mat originalImage = imread(findFile(inputFile));
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if (originalImage.empty()) {
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cout << "Error: Unable to read input image." << endl;
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return -1;
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}
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// Process first image to compute CCM
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Mat image = originalImage.clone();
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Mat src;
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Ptr<CCheckerDetector> detector;
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if (!modelPath.empty() && !configPath.empty()) {
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Net net = readNetFromTensorflow(modelPath, configPath);
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net.setPreferableBackend(getBackendID(backend));
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net.setPreferableTarget(getTargetID(target));
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detector = CCheckerDetector::create(net);
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cout << "Using DNN-based checker detector." << endl;
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} else {
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detector = CCheckerDetector::create();
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cout << "Using thresholding-based checker detector." << endl;
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}
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detector->setColorChartType(chartType);
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if (!processFrame(image, detector, src, nc)) {
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cout << "No chart detected in the input image!" << endl;
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return -1;
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}
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// Convert to double and normalize
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src.convertTo(src, CV_64F, 1.0/255.0);
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// Color correction model
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model = ColorCorrectionModel(src, COLORCHECKER_MACBETH);
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model.setCcmType(CCM_LINEAR);
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model.setDistance(DISTANCE_CIE2000);
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model.setLinearization(LINEARIZATION_GAMMA);
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model.setLinearizationGamma(2.2);
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Mat ccm = model.compute();
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cout << "Computed CCM Matrix:\n" << ccm << endl;
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cout << "Loss: " << model.getLoss() << endl;
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// Save model parameters to YAML file
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FileStorage fs("ccm_output.yaml", FileStorage::WRITE);
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model.write(fs);
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fs.release();
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cout << "Model parameters saved to ccm_output.yaml" << endl;
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// Set query image for correction
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if (queryFile.empty()) {
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cout << "[WARN] No query image provided, applying color correction on input image" << endl;
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queryImage = originalImage.clone();
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} else {
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queryImage = imread(findFile(queryFile));
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if (queryImage.empty()) {
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cout << "Error: Unable to read query image." << endl;
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return -1;
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}
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}
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}
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Mat calibratedImage;
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model.correctImage(queryImage, calibratedImage);
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imshow("Original Image", queryImage);
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imshow("Corrected Image", calibratedImage);
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waitKey(0);
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return 0;
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}
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//! [tutorial]
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