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Merge pull request #20957 from sturkmen72:update-documentation

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Update documentation

* Update DNN-based Face Detection And Recognition tutorial

* samples(dnn/face): update face_detect.cpp

* final changes

Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
This commit is contained in:
Suleyman TURKMEN
2021-11-28 15:56:28 +03:00
committed by GitHub
parent b594ed99b8
commit a97f21ba4e
5 changed files with 333 additions and 275 deletions
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samples/dnn/face_detect.cpp
+201 -54
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@@ -8,125 +8,272 @@
using namespace cv;
using namespace std;
static Mat visualize(Mat input, Mat faces, int thickness=2)
static
void visualize(Mat& input, int frame, Mat& faces, double fps, int thickness = 2)
{
Mat output = input.clone();
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++)
{
// Print results
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: " << faces.at<float>(i, 14) << "\n";
<< "score: " << cv::format("%.2f", faces.at<float>(i, 14))
<< endl;
// Draw bounding box
rectangle(output, 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);
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);
// Draw landmarks
circle(output, Point2i(int(faces.at<float>(i, 4)), int(faces.at<float>(i, 5))), 2, Scalar(255, 0, 0), thickness);
circle(output, Point2i(int(faces.at<float>(i, 6)), int(faces.at<float>(i, 7))), 2, Scalar( 0, 0, 255), thickness);
circle(output, Point2i(int(faces.at<float>(i, 8)), int(faces.at<float>(i, 9))), 2, Scalar( 0, 255, 0), thickness);
circle(output, Point2i(int(faces.at<float>(i, 10)), int(faces.at<float>(i, 11))), 2, Scalar(255, 0, 255), thickness);
circle(output, Point2i(int(faces.at<float>(i, 12)), int(faces.at<float>(i, 13))), 2, Scalar( 0, 255, 255), 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);
}
return output;
putText(input, fpsString, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 2);
}
int main(int argc, char ** argv)
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv,
"{help h | | Print this message.}"
"{input i | | Path to the input image. Omit for detecting on default camera.}"
"{model m | yunet.onnx | Path to the model. Download yunet.onnx in https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.}"
"{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.}"
"{vis v | true | Set true to open a window for result visualization. This flag is invalid when using camera.}"
"{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 | yunet.onnx | Path to the model. Download yunet.onnx in https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx }"
"{fr_model fr | face_recognizer_fast.onnx | Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view}"
"{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 (argc == 1 || parser.has("help"))
if (parser.has("help"))
{
parser.printMessage();
return -1;
return 0;
}
String modelPath = parser.get<String>("model");
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");
bool vis = parser.get<bool>("vis");
double cosine_similar_thresh = 0.363;
double l2norm_similar_thresh = 1.128;
//! [initialize_FaceDetectorYN]
// Initialize FaceDetectorYN
Ptr<FaceDetectorYN> detector = FaceDetectorYN::create(modelPath, "", Size(320, 320), scoreThreshold, nmsThreshold, topK);
Ptr<FaceDetectorYN> detector = FaceDetectorYN::create(fd_modelPath, "", Size(320, 320), scoreThreshold, nmsThreshold, topK);
//! [initialize_FaceDetectorYN]
TickMeter tm;
// If input is an image
if (parser.has("input"))
if (parser.has("image1"))
{
String input = parser.get<String>("input");
Mat image = imread(input);
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;
}
tm.start();
//! [inference]
// Set input size before inference
detector->setInputSize(image.size());
detector->setInputSize(image1.size());
// Inference
Mat faces;
detector->detect(image, faces);
Mat faces1;
detector->detect(image1, faces1);
if (faces1.rows < 1)
{
std::cerr << "Cannot find a face in " << input1 << std::endl;
return 1;
}
//! [inference]
tm.stop();
// Draw results on the input image
Mat result = visualize(image, faces);
visualize(image1, -1, faces1, tm.getFPS());
// Save results if save is true
if(save)
if (save)
{
cout << "Results saved to result.jpg\n";
imwrite("result.jpg", result);
cout << "Saving result.jpg...\n";
imwrite("result.jpg", image1);
}
// Visualize results
if (vis)
imshow("image1", image1);
pollKey(); // handle UI events to show content
if (parser.has("image2"))
{
namedWindow(input, WINDOW_AUTOSIZE);
imshow(input, result);
waitKey(0);
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();
//! [initialize_FaceRecognizerSF]
// Initialize FaceRecognizerSF
Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(fr_modelPath, "");
//! [initialize_FaceRecognizerSF]
//! [facerecognizer]
// Aligning and cropping facial image through the first face of faces detected.
Mat aligned_face1, aligned_face2;
faceRecognizer->alignCrop(image1, faces1.row(0), aligned_face1);
faceRecognizer->alignCrop(image2, faces2.row(0), aligned_face2);
// Run feature extraction with given aligned_face
Mat feature1, feature2;
faceRecognizer->feature(aligned_face1, feature1);
feature1 = feature1.clone();
faceRecognizer->feature(aligned_face2, feature2);
feature2 = feature2.clone();
//! [facerecognizer]
//! [match]
double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_COSINE);
double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_NORM_L2);
//! [match]
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 deviceId = 0;
VideoCapture cap;
cap.open(deviceId, CAP_ANY);
int frameWidth = int(cap.get(CAP_PROP_FRAME_WIDTH));
int frameHeight = int(cap.get(CAP_PROP_FRAME_HEIGHT));
int frameWidth, frameHeight;
float scale = parser.get<float>("scale");
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)); // keep GStreamer pipelines
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));
Mat frame;
TickMeter tm;
String msg = "FPS: ";
while(waitKey(1) < 0) // Press any key to exit
cout << "Press 'SPACE' to save frame, any other key to exit..." << endl;
int nFrame = 0;
for (;;)
{
// Get frame
if (!cap.read(frame))
Mat frame;
if (!capture.read(frame))
{
cerr << "No frames grabbed!\n";
cerr << "Can't grab frame! Stop\n";
break;
}
resize(frame, frame, Size(frameWidth, frameHeight));
// Inference
Mat faces;
tm.start();
detector->detect(frame, faces);
tm.stop();
Mat result = frame.clone();
// Draw results on the input image
Mat result = visualize(frame, faces);
putText(result, msg + to_string(tm.getFPS()), Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
visualize(result, nFrame, faces, tm.getFPS());
// Visualize results
imshow("Live", result);
tm.reset();
int key = waitKey(1);
bool saveFrame = save;
if (key == ' ')
{
saveFrame = true;
key = 0; // handled
}
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;
}