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Update documentation
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@@ -44,8 +44,8 @@ int main(int argc, char** argv)
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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 | yunet.onnx | Path to the model. Download yunet.onnx in https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx }"
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"{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}"
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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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@@ -65,6 +65,7 @@ int main(int argc, char** argv)
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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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@@ -87,6 +88,9 @@ int main(int argc, char** argv)
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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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@@ -199,7 +203,6 @@ int main(int argc, char** argv)
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else
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{
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int frameWidth, frameHeight;
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float scale = parser.get<float>("scale");
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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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