In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizerSF` are presented in [Usage](#Usage).
In this section, we introduce cv::FaceDetectorYN class for face detection and cv::FaceRecognizerSF class for face recognition.
## Models
There are two models (ONNX format) pre-trained and required for this module:
"{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}"
@ -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.')
Alexander Alekhin
3 years ago