A collection of pretrained face detectors including:
- S3FD [1] with weights trained on the WIDER [2] dataset. Implementation of the algorithm is based on this repository: https://github.com/cs-giung/face-detection-pytorch.
- RetinaFace [3] with weights trained on the WIDER [2] dataset. Wights for networks using either Resnet50 or MobileNet0.25 as the backbone are included. The implementation is based on this repository: https://github.com/biubug6/Pytorch_Retinaface.
For convenience, the package also includes a simple IOU-based face tracker and a head pose estimator using EPnP.
- Git LFS, needed for downloading the pretrained weights that are larger than 100 MB.
- Numpy:
$pip3 install numpy - Sciypy:
$pip3 install scipy - PyTorch:
$pip3 install torch torchvision - OpenCV:
$pip3 install opencv-python
git clone https://github.com/hhj1897/face_detection.git
cd face_detection
git lfs pull
pip install -e .
- To test on live video:
python face_detection_test.py [-i webcam_index] - To test on a video file:
python face_detection_test.py [-i input_file] [-o output_file]
By default, the test script would use RetinaFace with Resnet50, but you can change that using --method and --weights options.
# Import everything, just for illustration purposes import cv2 from ibug.face_detection import RetinaFacePredictor, S3FDPredictor from ibug.face_detection.utils import HeadPoseEstimator, SimpleFaceTracker # Create a RetinaFace detector using Resnet50 backbone, with the confidence # threshold set to 0.8 face_detector = RetinaFacePredictor( threshold=0.8, device='cuda:0', model=RetinaFacePredictor.get_model('resnet50')) # Create a head pose estimator pose_estimator = HeadPoseEstimator() # Create a simple face tracker, with mininum face size set to 64x64 pixels face_tracker = SimpleFaceTracker(minimum_face_size=64) # Load a test image. Note that images loaded by OpenCV adopt the B-G-R channel # order. image = cv2.imread('test.png') # Detect faces from the image # Note: # 1. The input image must be a byte array of dimension HxWx3. # 2. The return value is a Nx5 (for S3FD) or a Nx15 (for RetinaFace) matrix, # in which N is the number of detected faces. The first 4 columns store # (in this order) the left, top, right, and bottom coordinates of the # detected face boxes. The 5th columns stores the detection confidences. # The remaining columns store the coordinates (in the order of x1, y1, x2, # y2, ...) of the detected landmarks. detected_faces = face_detector(image, rgb=False) # Head pose estimation (only works for RetinaFace, which also detects the 5 # landmarks on the face), which gives pitch, yaw, and roll (in degrees) of # the detected faces. for face in detected_faces: pitch, yaw, roll = pose_estimator(face[5:].reshape((-1, 2))) # If you are processing frames in a video, you can also perform rudimentary # face tracking, as shown below. The return value is a list containing the # tracklet ID (>=1) of the detected faces. If a face cannot be tracked # (such as because it is too small), its corresponding element in the list # would be set to None. tracked_ids = face_tracker(detected_faces[:, :4])
[1] Zhang, Shifeng, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z. Li. "S3fd: Single shot scale-invariant face detector." In Proceedings of the IEEE international conference on computer vision, pp. 192-201. 2017.
[2] Yang, Shuo, Ping Luo, Chen-Change Loy, Xiaoou Tang. "WIDER FACE: A Face Detection Benchmark." In Proceedings of the IEEE international conference on computer vision, pp. 5525-5533. 2016.
[3] Deng, Jiankang, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. "Retinaface: Single-shot multi-level face localisation in the wild." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5203-5212. 2020.