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Update documentation

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This commit is contained in:
Suleyman TURKMEN
2022-01-07 16:51:21 +03:00
parent a1143c4ea0
commit fe2a259eb1
6 changed files with 61 additions and 42 deletions
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samples/dnn/face_detect.cpp
+6 -3
View File
@@ -44,8 +44,8 @@ int main(int argc, char** argv)
"{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}"
"{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}"
@@ -65,6 +65,7 @@ int main(int argc, char** argv)
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;
@@ -87,6 +88,9 @@ int main(int argc, char** argv)
return 2;
}
int imageWidth = int(image1.cols * scale);
int imageHeight = int(image1.rows * scale);
resize(image1, image1, Size(imageWidth, imageHeight));
tm.start();
//! [inference]
@@ -199,7 +203,6 @@ int main(int argc, char** argv)
else
{
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]))
samples/dnn/face_detect.py
+7 -3
View File
@@ -16,8 +16,8 @@ parser.add_argument('--image1', '-i1', type=str, help='Path to the input image1.
parser.add_argument('--image2', '-i2', type=str, help='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.')
parser.add_argument('--video', '-v', type=str, help='Path to the input video.')
parser.add_argument('--scale', '-sc', type=float, default=1.0, help='Scale factor used to resize input video frames.')
parser.add_argument('--face_detection_model', '-fd', type=str, default='yunet.onnx', help='Path to the face detection model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.')
parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognizer_fast.onnx', help='Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.')
parser.add_argument('--face_detection_model', '-fd', type=str, default='face_detection_yunet_2021dec.onnx', help='Path to the face detection model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet')
parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognition_sface_2021dec.onnx', help='Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface')
parser.add_argument('--score_threshold', type=float, default=0.9, help='Filtering out faces of score < score_threshold.')
parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
@@ -56,11 +56,15 @@ if __name__ == '__main__':
# If input is an image
if args.image1 is not None:
img1 = cv.imread(cv.samples.findFile(args.image1))
img1Width = int(img1.shape[1]*args.scale)
img1Height = int(img1.shape[0]*args.scale)
img1 = cv.resize(img1, (img1Width, img1Height))
tm.start()
## [inference]
# Set input size before inference
detector.setInputSize((img1.shape[1], img1.shape[0]))
detector.setInputSize((img1Width, img1Height))
faces1 = detector.detect(img1)
## [inference]