{"library":"retinaface-py","title":"RetinaFace","description":"A Python implementation of RetinaFace, a single-stage dense face localisation model for face detection and landmark localisation in the wild. Current version 0.0.2, requires Python >=3.6, uses PyTorch. The library wraps a pre-trained ResNet50-based RetinaFace model. Release cadence: low (last update likely 2021).","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install retinaface-py"],"cli":null},"imports":["from RetinaFace import RetinaFace"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"\nfrom RetinaFace import RetinaFace\nimport cv2\n\n# Load image (assuming opencv-python is installed)\nimg = cv2.imread('face.jpg')\nimg_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n# Detect faces\ndetections = RetinaFace.detect_faces(img_rgb)\n\n# Print results\nfor face_id, face_data in detections.items():\n    print(f\"{face_id}: score={face_data['score']}, box={face_data['facial_area']}\")\n","lang":"python","description":"Detect faces in an image using RetinaFace. The input image should be a numpy array (RGB). Returns a dictionary of detections.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}