Face Detection on Custom Dataset with Detectron2 and PyTorch using ... Step 4: Using the trained classifier, classify the detected faces. We read and get each frame from video and process each from for detection. Mediapipe works with RGB images and OpenCV reads images in BGR format, so we convert the image to RGB format using the cv2.cvtCOLOR () function. Detection: The most popular datasets used for face detection are WIDER FACE [39], FDDB [11], and IJB-A [13]. 32203. From self-driving cars to facial recognition technology—computer vision applications are the face of new tech. . Lets dive into code for this. • Load the pre-trained model and weights. This work proposes a technique that will draw bounding boxes (red or green . • Check some samples of metadata. Step 3: Detect faces while testing data using SSD face detector. Classes. Facial landmarks (up to 34 per face) Facial orientation (roll, pan, and tilt angles) Detection and landmarking confidence scores. To train face mask model, we used Face Detection model. • Load the dataset and create the metadata. Prophesee's GEN1 Automotive Detection Dataset is the largest Event-Based Dataset to date. Face mask detection in street camera video streams using AI: behind the ... • Build distance metrics for identifying the distance between two given images. draw_boxes() draw_boxes () function accepts the augmented image, the augmented bounding boxes, and the bounding box data format as parameters. P-Net is your traditional 12-Net: It takes a 12x12 pixel image as an input and outputs a matrix result telling you whether or not a there is a face — and if there is, the coordinates of the bounding boxes and facial landmarks for each face. Great you are ready to implement a hands on project " Face Mask Detection "Requirements Windows or Linux CMake >= 3.12 CUDA 10.0 OpenCV >= 2.4 GPU with CC >= 3.0.