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Adding macbeth chart detector to objdetect module from opencv_contrib (#26906)
* Added mcc to opencv modules * Removed color correction module * Updated parameters return type * Added python sample for macbeth_chart_detection * Added models.yml support to samples * Removed unnecessary headers and classes * fixed datatype conversion * fixed datatype conversion * Cleaned headers and added reference/actual colors to samples * Added mcc tutorial * fixed datatype and header * replaced unsigned with int * Aligned actual and reference color function, added imread * Fixed shadow variable * Updated samples * Added last frame colors prints * updated detector class * Added getter functions and useNet function * Refactoring * Fixes in test * fixed infinite divison issue
This commit is contained in:
Gursimar Singh
GitHub
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#include <opencv2/core.hpp>
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#include <opencv2/highgui.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 mcc;
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const string about =
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"This sample demonstrates mcc checker detection with DNN based model and thresholding (default) techniques.\n\n"
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"To run default:\n"
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"\t ./example_cpp_macbeth_chart_detection --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)\n"
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"With DNN model:\n"
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"\t ./example_cpp_macbeth_chart_detection mcc --input=path/to/your/input/image/or/video\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 | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
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"{ type | 0 | chartType: 0-Standard, 1-DigitalSG, 2-Vinyl, default:0 }"
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"{ num_charts | 1 | Maximum number of charts in the image }"
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"{ model | | Path to the model file for using dnn model. }";
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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, Mat& tgt, int nc){
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Mat imageCopy = frame.clone();
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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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detector->draw(checkers, frame);
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src = checkers[0]->getChartsRGB(false);
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tgt = detector->getRefColors();
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imshow("Image result", frame);
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imshow("Original", imageCopy);
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return true;
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}
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int main(int argc, char *argv[])
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{
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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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{
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cout << about << endl;
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parser.printMessage();
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return -1;
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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") || (parser.get<String>("model") != "")) {
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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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CV_Assert(0 <= t && t <= 2);
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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 model_path = 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 pbtxt_path = 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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Ptr<CCheckerDetector> detector;
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if (model_path != "" && pbtxt_path != ""){
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EngineType engine = ENGINE_AUTO;
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if (backend != "default" || target != "cpu"){
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engine = ENGINE_CLASSIC;
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}
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Net net = readNetFromTensorflow(model_path, pbtxt_path, engine);
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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<<"Detecting checkers using neural network."<<endl;
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}
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else{
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detector = CCheckerDetector::create();
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}
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detector->setColorChartType(chartType);
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bool isVideo = true;
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Mat image;
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VideoCapture cap;
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if (parser.has("input")){
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const string inputFile = parser.get<String>("input");
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image = imread(findFile(inputFile));
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if (!image.empty())
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{
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isVideo = false;
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}
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else
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{
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// Not an image, so try opening it as a video.
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cap.open(findFile(inputFile));
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if (!cap.isOpened())
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{
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cout << "[ERROR] Could not open file as an image or video: " << inputFile << endl;
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return -1;
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}
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}
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}
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else
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cap.open(0);
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Mat src, tgt;
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bool found = false;
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if (isVideo){
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cout<<"To print the actual colors and reference colors for current frame press SPACEBAR. To resume press SPACEBAR again"<<endl;
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while (cap.grab())
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{
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Mat frame;
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cap.retrieve(frame);
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found = processFrame(frame, detector, src, tgt, nc);
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int key = waitKey(10);
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if (key == ' '){
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if(found){
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cout<<"Reference colors: "<<tgt<<endl<<"--------------------"<<endl;
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cout<<"Actual colors: "<<src<<endl<<endl;
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cout<<"Press spacebar to resume."<<endl;
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waitKey(0);
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cout << "Resumed! Processing continues..." << endl;
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}
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else{
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cout<<"No color chart detected!!"<<endl;
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}
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}
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else if (key == 27) exit(0);
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}
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if(found){
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cout<<"Reference colors: "<<tgt<<endl<<"--------------------"<<endl;
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cout<<"Actual colors: "<<src<<endl<<endl;
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}
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}
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else{
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found = processFrame(image, detector, src, tgt, nc);
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if(found){
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cout<<"Reference colors: "<<tgt<<endl<<"--------------------"<<endl;
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cout<<"Actual colors: "<<src<<endl<<endl;
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waitKey(0);
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}
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else{
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cout<<"No chart detected!!"<<endl;
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}
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}
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return 0;
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}
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