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landmark-detection【效率与协作】Routes face landmark prediction on single images, batches, and directories with FaceAlignment.
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Use this sub-skill when a user wants to detect facial landmarks from an image path, NumPy array, torch tensor, batch tensor, or directory of images. It covers 2D, 2.5D, and 3D landmark outputs, get_landmarks / get_landmarks_from_image, and the device / compile / batch-size knobs that affect inference.
detect face landmarksrun face_alignment on an imageprocess a directory of facescompare 2D vs 3D landmarksreturn landmark scoresreturn bounding boxesdisable compilereduce batch sizereferences/workflows.md for end-to-end image, batch, and directory recipes.references/troubleshooting.md when inference fails, no faces are detected, compile slows startup, or batch size causes memory pressure.../../references/api-reference.md for the exact constructor and method signatures.scripts/detect_landmarks.py for a reusable smoke / demo helper.LandmarksType.TWO_D, LandmarksType.TWO_HALF_D, or LandmarksType.THREE_D.device='cpu', device='cuda', or device='mps' explicitly.flip_input=True for accuracy and compile=False when you want fast startup or a simpler smoke path.max_batch_size if the detector finds many faces or the host is memory constrained.get_landmarks_from_image for one image and get_landmarks_from_directory for image trees; get_landmarks is only a deprecated alias.(68, 2).(68, 3).None or (None, None, None) depending on return_bboxes and return_landmark_score.If the user is choosing or troubleshooting a backend, route to ../detectors/ instead of changing landmark logic here.