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https://github.com/opencv/opencv.git
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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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@@ -477,3 +477,18 @@ ldm_inpainting:
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height: 512
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rgb: true
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sample: "ldm_inpainting"
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################################################################################
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# Macbeth chart detection model.
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################################################################################
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mcc:
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load_info:
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url: "https://github.com/gursimarsingh/opencv_zoo/raw/refs/heads/mcc_model/models/macbeth_chart_detector/frozen_inference_graph.pb?download="
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sha1: "fae7dbef14c4ae1fca76f3662220fbd460ed5ed6"
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model: "frozen_inference_graph.pb"
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config_load_info:
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url: "https://github.com/gursimarsingh/opencv_zoo/raw/refs/heads/mcc_model/models/macbeth_chart_detector/graph.pbtxt?download="
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sha1: "8350cb8f078ecefa1cd566e89930ede25a192310"
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config: "graph.pbtxt"
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sample: "mcc"
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@@ -0,0 +1,155 @@
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import cv2 as cv
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import numpy as np
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import argparse
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
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from dnn.common import *
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def get_args_parser(func_args):
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backends = ("default", "openvino", "opencv", "vkcom", "cuda")
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targets = ("cpu", "opencl", "opencl_fp16", "ncs2_vpu", "hddl_vpu", "vulkan", "cuda", "cuda_fp16")
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), '../dnn', 'models.yml'),
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help='An optional path to file with preprocessing parameters.')
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parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.', default=0, required=False)
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parser.add_argument('--method', help='choose method: dexined or canny', default='canny', required=False)
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parser.add_argument('--chart_type', type=int, default=0,
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help='chartType: 0-Standard, 1-DigitalSG, 2-Vinyl, default:0')
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parser.add_argument('--num_charts', type=int, default=1,
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help='Maximum number of charts in the image')
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parser.add_argument('--backend', default="default", type=str, choices=backends,
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help="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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parser.add_argument('--target', default="cpu", type=str, choices=targets,
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help="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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"ncs2_vpu: NCS2 VPU, "
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"hddl_vpu: HDDL 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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args, _ = parser.parse_known_args()
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add_preproc_args(args.zoo, parser, 'mcc', 'mcc')
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parser = argparse.ArgumentParser(parents=[parser],
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description='''
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To run:
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Default:
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python macbeth_chart_detection.py --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)
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DNN model:
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python macbeth_chart_detection.py mcc --input=path/to/your/input/image/or/video
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Model path can also be specified using --model argument. And config path can be specified using --config.
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''', formatter_class=argparse.RawTextHelpFormatter)
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return parser.parse_args(func_args)
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def process_frame(frame, detector, num_charts):
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image_copy = frame.copy()
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if not detector.process(frame, num_charts):
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return None, None
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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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cv.imshow("image result | Press ESC to quit", frame)
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cv.imshow("original", image_copy)
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return src, tgt
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def main(func_args=None):
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args = get_args_parser(func_args)
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if not (0 <= args.chart_type <= 2):
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raise ValueError("chartType must be 0, 1, or 2")
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if os.getenv('OPENCV_SAMPLES_DATA_PATH') is not None:
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try:
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args.model = findModel(args.model, args.sha1)
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args.config = findModel(args.config, args.config_sha1)
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except:
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print("[WARN] Model file not provided, using default detector. Pass model using --model and config using --config to use dnn based detector.\n\n")
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args.model = None
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args.config = None
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else:
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args.model = None
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args.config = None
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print("[WARN] Model file not provided, using default detector. Pass model using --model and config using --config to use dnn based detector. Or, set OPENCV_SAMPLES_DATA_PATH environment variable.\n\n")
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if args.model and args.config:
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# Load the DNN from TensorFlow model
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engine = cv.dnn.ENGINE_AUTO
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if args.backend != "default" or args.target != "cpu":
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engine = cv.dnn.ENGINE_CLASSIC
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net = cv.dnn.readNetFromTensorflow(args.model, args.config, engine)
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net.setPreferableBackend(get_backend_id(args.backend))
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net.setPreferableTarget(get_target_id(args.target))
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detector = cv.mcc_CCheckerDetector.create(net)
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print("Detecting checkers using neural network.")
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else:
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detector = cv.mcc_CCheckerDetector.create()
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print("Detecting checkers using default method (no DNN).")
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detector.setColorChartType(args.chart_type)
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is_video = True
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if args.input:
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image = cv.imread(findFile(args.input))
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if image is not None:
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is_video = False
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else:
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cap = cv.VideoCapture(findFile(args.input))
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else:
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cap = cv.VideoCapture(0)
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if is_video:
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print("To print the actual colors and reference colors for current frame press SPACEBAR. To resume press SPACEBAR again")
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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src, tgt = process_frame(frame, detector, args.num_charts)
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key = cv.waitKey(10) & 0xFF
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if key == ord(' '):
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if src is None or tgt is None:
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print("No color chart detected!!")
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else:
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print("Actual colors: ", src)
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print("Reference colors: ", tgt)
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print("Press spacebar to resume.")
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cv.waitKey(0)
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print("Resumed! Processing continues...")
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elif key == 27:
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exit(0)
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if src is not None or tgt is not None:
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print("Actual colors: ", src)
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print("Reference colors: ", tgt)
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else:
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src, tgt = process_frame(image, detector, args.num_charts)
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if src is None or tgt is None:
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print("No color chart detected!!")
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else:
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print("Actual colors: ", src)
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print("Reference colors: ", tgt)
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cv.waitKey(0)
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cv.destroyAllWindows()
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if __name__ == "__main__":
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main()
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