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| #include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/objdetect.hpp>
#include <iostream>
using namespace cv;
using namespace std;
static
void visualize(Mat& input, int frame, Mat& faces, double fps, int thickness = 2)
{
std::string fpsString = cv::format("FPS : %.2f", (float)fps);
if (frame >= 0)
cout << "Frame " << frame << ", ";
cout << "FPS: " << fpsString << endl;
for (int i = 0; i < faces.rows; i++)
{
cout << "Face " << i
<< ", top-left coordinates: (" << faces.at<float>(i, 0) << ", " << faces.at<float>(i, 1) << "), "
<< "box width: " << faces.at<float>(i, 2) << ", box height: " << faces.at<float>(i, 3) << ", "
<< "score: " << cv::format("%.2f", faces.at<float>(i, 14))
<< endl;
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);
circle(input, Point2i(int(faces.at<float>(i, 4)), int(faces.at<float>(i, 5))), 2, Scalar(255, 0, 0), thickness);
circle(input, Point2i(int(faces.at<float>(i, 6)), int(faces.at<float>(i, 7))), 2, Scalar(0, 0, 255), thickness);
circle(input, Point2i(int(faces.at<float>(i, 8)), int(faces.at<float>(i, 9))), 2, Scalar(0, 255, 0), thickness);
circle(input, Point2i(int(faces.at<float>(i, 10)), int(faces.at<float>(i, 11))), 2, Scalar(255, 0, 255), thickness);
circle(input, Point2i(int(faces.at<float>(i, 12)), int(faces.at<float>(i, 13))), 2, Scalar(0, 255, 255), thickness);
}
putText(input, fpsString, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 2);
}
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv,
"{help h | | Print this message}"
"{image1 i1 | | Path to the input image1. Omit for detecting through VideoCapture}"
"{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}"
"{video v | 0 | Path to the input video}"
"{scale sc | 1.0 | Scale factor used to resize input video frames}"
"{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}"
"{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}"
"{score_threshold | 0.9 | Filter out faces of score < score_threshold}"
"{nms_threshold | 0.3 | Suppress bounding boxes of iou >= nms_threshold}"
"{top_k | 5000 | Keep top_k bounding boxes before NMS}"
"{save s | false | Set true to save results. This flag is invalid when using camera}"
);
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
String fd_modelPath = parser.get<String>("fd_model");
String fr_modelPath = parser.get<String>("fr_model");
float scoreThreshold = parser.get<float>("score_threshold");
float nmsThreshold = parser.get<float>("nms_threshold");
int topK = parser.get<int>("top_k");
bool save = parser.get<bool>("save");
float scale = parser.get<float>("scale");
double cosine_similar_thresh = 0.363;
double l2norm_similar_thresh = 1.128;
Ptr<FaceDetectorYN> detector = FaceDetectorYN::create(fd_modelPath, "", Size(320, 320), scoreThreshold, nmsThreshold, topK);
TickMeter tm;
if (parser.has("image1"))
{
String input1 = parser.get<String>("image1");
Mat image1 = imread(samples::findFile(input1));
if (image1.empty())
{
std::cerr << "Cannot read image: " << input1 << std::endl;
return 2;
}
int imageWidth = int(image1.cols * scale);
int imageHeight = int(image1.rows * scale);
resize(image1, image1, Size(imageWidth, imageHeight));
tm.start();
detector->setInputSize(image1.size());
Mat faces1;
detector->detect(image1, faces1);
if (faces1.rows < 1)
{
std::cerr << "Cannot find a face in " << input1 << std::endl;
return 1;
}
tm.stop();
visualize(image1, -1, faces1, tm.getFPS());
if (save)
{
cout << "Saving result.jpg...\n";
imwrite("result.jpg", image1);
}
imshow("image1", image1);
pollKey();
if (parser.has("image2"))
{
String input2 = parser.get<String>("image2");
Mat image2 = imread(samples::findFile(input2));
if (image2.empty())
{
std::cerr << "Cannot read image2: " << input2 << std::endl;
return 2;
}
tm.reset();
tm.start();
detector->setInputSize(image2.size());
Mat faces2;
detector->detect(image2, faces2);
if (faces2.rows < 1)
{
std::cerr << "Cannot find a face in " << input2 << std::endl;
return 1;
}
tm.stop();
visualize(image2, -1, faces2, tm.getFPS());
if (save)
{
cout << "Saving result2.jpg...\n";
imwrite("result2.jpg", image2);
}
imshow("image2", image2);
pollKey();
Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(fr_modelPath, "");
Mat aligned_face1, aligned_face2;
faceRecognizer->alignCrop(image1, faces1.row(0), aligned_face1);
faceRecognizer->alignCrop(image2, faces2.row(0), aligned_face2);
Mat feature1, feature2;
faceRecognizer->feature(aligned_face1, feature1);
feature1 = feature1.clone();
faceRecognizer->feature(aligned_face2, feature2);
feature2 = feature2.clone();
double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_COSINE);
double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_NORM_L2);
if (cos_score >= cosine_similar_thresh)
{
std::cout << "They have the same identity;";
}
else
{
std::cout << "They have different identities;";
}
std::cout << " Cosine Similarity: " << cos_score << ", threshold: " << cosine_similar_thresh << ". (higher value means higher similarity, max 1.0)\n";
if (L2_score <= l2norm_similar_thresh)
{
std::cout << "They have the same identity;";
}
else
{
std::cout << "They have different identities.";
}
std::cout << " NormL2 Distance: " << L2_score << ", threshold: " << l2norm_similar_thresh << ". (lower value means higher similarity, min 0.0)\n";
}
cout << "Press any key to exit..." << endl;
waitKey(0);
}
else
{
int frameWidth, frameHeight;
VideoCapture capture;
std::string video = parser.get<string>("video");
if (video.size() == 1 && isdigit(video[0]))
capture.open(parser.get<int>("video"));
else
capture.open(samples::findFileOrKeep(video));
if (capture.isOpened())
{
frameWidth = int(capture.get(CAP_PROP_FRAME_WIDTH) * scale);
frameHeight = int(capture.get(CAP_PROP_FRAME_HEIGHT) * scale);
cout << "Video " << video
<< ": width=" << frameWidth
<< ", height=" << frameHeight
<< endl;
}
else
{
cout << "Could not initialize video capturing: " << video << "\n";
return 1;
}
detector->setInputSize(Size(frameWidth, frameHeight));
cout << "Press 'SPACE' to save frame, any other key to exit..." << endl;
int nFrame = 0;
for (;;)
{
Mat frame;
if (!capture.read(frame))
{
cerr << "Can't grab frame! Stop\n";
break;
}
resize(frame, frame, Size(frameWidth, frameHeight));
Mat faces;
tm.start();
detector->detect(frame, faces);
tm.stop();
Mat result = frame.clone();
visualize(result, nFrame, faces, tm.getFPS());
imshow("Live", result);
int key = waitKey(1);
bool saveFrame = save;
if (key == ' ')
{
saveFrame = true;
key = 0;
}
if (saveFrame)
{
std::string frame_name = cv::format("frame_%05d.png", nFrame);
std::string result_name = cv::format("result_%05d.jpg", nFrame);
cout << "Saving '" << frame_name << "' and '" << result_name << "' ...\n";
imwrite(frame_name, frame);
imwrite(result_name, result);
}
++nFrame;
if (key > 0)
break;
}
cout << "Processed " << nFrame << " frames" << endl;
}
cout << "Done." << endl;
return 0;
}
|
