283 lines
10 KiB
C++
283 lines
10 KiB
C++
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/objdetect.hpp>
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#include <iostream>
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using namespace cv;
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using namespace std;
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static
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void visualize(Mat& input, int frame, Mat& faces, double fps, int thickness = 2)
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{
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std::string fpsString = cv::format("FPS : %.2f", (float)fps);
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if (frame >= 0)
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cout << "Frame " << frame << ", ";
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cout << "FPS: " << fpsString << endl;
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for (int i = 0; i < faces.rows; i++)
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{
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// Print results
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cout << "Face " << i
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<< ", top-left coordinates: (" << faces.at<float>(i, 0) << ", " << faces.at<float>(i, 1) << "), "
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<< "box width: " << faces.at<float>(i, 2) << ", box height: " << faces.at<float>(i, 3) << ", "
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<< "score: " << cv::format("%.2f", faces.at<float>(i, 14))
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<< endl;
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// Draw bounding box
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rectangle(input, Rect2i(int(faces.at<float>(i, 0)), int(faces.at<float>(i, 1)), int(faces.at<float>(i, 2)), int(faces.at<float>(i, 3))), Scalar(0, 255, 0), thickness);
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// Draw landmarks
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circle(input, Point2i(int(faces.at<float>(i, 4)), int(faces.at<float>(i, 5))), 2, Scalar(255, 0, 0), thickness);
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circle(input, Point2i(int(faces.at<float>(i, 6)), int(faces.at<float>(i, 7))), 2, Scalar(0, 0, 255), thickness);
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circle(input, Point2i(int(faces.at<float>(i, 8)), int(faces.at<float>(i, 9))), 2, Scalar(0, 255, 0), thickness);
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circle(input, Point2i(int(faces.at<float>(i, 10)), int(faces.at<float>(i, 11))), 2, Scalar(255, 0, 255), thickness);
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circle(input, Point2i(int(faces.at<float>(i, 12)), int(faces.at<float>(i, 13))), 2, Scalar(0, 255, 255), thickness);
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}
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putText(input, fpsString, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 2);
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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,
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"{help h | | Print this message}"
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"{image1 i1 | | Path to the input image1. Omit for detecting through VideoCapture}"
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"{image2 i2 | | Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm}"
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"{video v | 0 | Path to the input video}"
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"{scale sc | 1.0 | Scale factor used to resize input video frames}"
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"{fd_model fd | face_detection_yunet_2021dec.onnx| Path to the model. Download yunet.onnx in https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet}"
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"{fr_model fr | face_recognition_sface_2021dec.onnx | Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface}"
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"{score_threshold | 0.9 | Filter out faces of score < score_threshold}"
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"{nms_threshold | 0.3 | Suppress bounding boxes of iou >= nms_threshold}"
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"{top_k | 5000 | Keep top_k bounding boxes before NMS}"
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"{save s | false | Set true to save results. This flag is invalid when using camera}"
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);
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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String fd_modelPath = parser.get<String>("fd_model");
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String fr_modelPath = parser.get<String>("fr_model");
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float scoreThreshold = parser.get<float>("score_threshold");
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float nmsThreshold = parser.get<float>("nms_threshold");
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int topK = parser.get<int>("top_k");
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bool save = parser.get<bool>("save");
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float scale = parser.get<float>("scale");
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double cosine_similar_thresh = 0.363;
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double l2norm_similar_thresh = 1.128;
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//! [initialize_FaceDetectorYN]
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// Initialize FaceDetectorYN
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Ptr<FaceDetectorYN> detector = FaceDetectorYN::create(fd_modelPath, "", Size(320, 320), scoreThreshold, nmsThreshold, topK);
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//! [initialize_FaceDetectorYN]
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TickMeter tm;
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// If input is an image
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if (parser.has("image1"))
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{
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String input1 = parser.get<String>("image1");
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Mat image1 = imread(samples::findFile(input1));
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if (image1.empty())
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{
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std::cerr << "Cannot read image: " << input1 << std::endl;
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return 2;
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}
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int imageWidth = int(image1.cols * scale);
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int imageHeight = int(image1.rows * scale);
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resize(image1, image1, Size(imageWidth, imageHeight));
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tm.start();
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//! [inference]
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// Set input size before inference
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detector->setInputSize(image1.size());
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Mat faces1;
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detector->detect(image1, faces1);
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if (faces1.rows < 1)
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{
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std::cerr << "Cannot find a face in " << input1 << std::endl;
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return 1;
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}
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//! [inference]
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tm.stop();
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// Draw results on the input image
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visualize(image1, -1, faces1, tm.getFPS());
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// Save results if save is true
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if (save)
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{
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cout << "Saving result.jpg...\n";
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imwrite("result.jpg", image1);
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}
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// Visualize results
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imshow("image1", image1);
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pollKey(); // handle UI events to show content
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if (parser.has("image2"))
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{
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String input2 = parser.get<String>("image2");
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Mat image2 = imread(samples::findFile(input2));
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if (image2.empty())
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{
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std::cerr << "Cannot read image2: " << input2 << std::endl;
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return 2;
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}
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tm.reset();
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tm.start();
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detector->setInputSize(image2.size());
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Mat faces2;
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detector->detect(image2, faces2);
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if (faces2.rows < 1)
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{
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std::cerr << "Cannot find a face in " << input2 << std::endl;
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return 1;
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}
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tm.stop();
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visualize(image2, -1, faces2, tm.getFPS());
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if (save)
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{
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cout << "Saving result2.jpg...\n";
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imwrite("result2.jpg", image2);
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}
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imshow("image2", image2);
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pollKey();
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//! [initialize_FaceRecognizerSF]
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// Initialize FaceRecognizerSF
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Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(fr_modelPath, "");
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//! [initialize_FaceRecognizerSF]
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//! [facerecognizer]
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// Aligning and cropping facial image through the first face of faces detected.
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Mat aligned_face1, aligned_face2;
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faceRecognizer->alignCrop(image1, faces1.row(0), aligned_face1);
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faceRecognizer->alignCrop(image2, faces2.row(0), aligned_face2);
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// Run feature extraction with given aligned_face
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Mat feature1, feature2;
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faceRecognizer->feature(aligned_face1, feature1);
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feature1 = feature1.clone();
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faceRecognizer->feature(aligned_face2, feature2);
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feature2 = feature2.clone();
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//! [facerecognizer]
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//! [match]
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double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_COSINE);
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double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_NORM_L2);
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//! [match]
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if (cos_score >= cosine_similar_thresh)
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{
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std::cout << "They have the same identity;";
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}
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else
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{
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std::cout << "They have different identities;";
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}
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std::cout << " Cosine Similarity: " << cos_score << ", threshold: " << cosine_similar_thresh << ". (higher value means higher similarity, max 1.0)\n";
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if (L2_score <= l2norm_similar_thresh)
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{
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std::cout << "They have the same identity;";
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}
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else
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{
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std::cout << "They have different identities.";
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}
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std::cout << " NormL2 Distance: " << L2_score << ", threshold: " << l2norm_similar_thresh << ". (lower value means higher similarity, min 0.0)\n";
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}
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cout << "Press any key to exit..." << endl;
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waitKey(0);
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}
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else
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{
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int frameWidth, frameHeight;
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VideoCapture capture;
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std::string video = parser.get<string>("video");
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if (video.size() == 1 && isdigit(video[0]))
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capture.open(parser.get<int>("video"));
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else
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capture.open(samples::findFileOrKeep(video)); // keep GStreamer pipelines
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if (capture.isOpened())
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{
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frameWidth = int(capture.get(CAP_PROP_FRAME_WIDTH) * scale);
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frameHeight = int(capture.get(CAP_PROP_FRAME_HEIGHT) * scale);
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cout << "Video " << video
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<< ": width=" << frameWidth
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<< ", height=" << frameHeight
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<< endl;
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}
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else
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{
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cout << "Could not initialize video capturing: " << video << "\n";
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return 1;
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}
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detector->setInputSize(Size(frameWidth, frameHeight));
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cout << "Press 'SPACE' to save frame, any other key to exit..." << endl;
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int nFrame = 0;
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for (;;)
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{
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// Get frame
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Mat frame;
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if (!capture.read(frame))
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{
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cerr << "Can't grab frame! Stop\n";
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break;
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}
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resize(frame, frame, Size(frameWidth, frameHeight));
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// Inference
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Mat faces;
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tm.start();
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detector->detect(frame, faces);
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tm.stop();
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Mat result = frame.clone();
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// Draw results on the input image
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visualize(result, nFrame, faces, tm.getFPS());
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// Visualize results
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imshow("Live", result);
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int key = waitKey(1);
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bool saveFrame = save;
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if (key == ' ')
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{
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saveFrame = true;
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key = 0; // handled
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}
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if (saveFrame)
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{
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std::string frame_name = cv::format("frame_%05d.png", nFrame);
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std::string result_name = cv::format("result_%05d.jpg", nFrame);
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cout << "Saving '" << frame_name << "' and '" << result_name << "' ...\n";
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imwrite(frame_name, frame);
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imwrite(result_name, result);
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}
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++nFrame;
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if (key > 0)
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break;
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}
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cout << "Processed " << nFrame << " frames" << endl;
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}
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cout << "Done." << endl;
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return 0;
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}
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