executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
face-alignmentAlign face identity folders with MTCNN detection, landmark localization, affine crops, and resize-before-align helpers.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the request is about face.evoLVe's MTCNN-based face detection, landmark localization, affine alignment, crop-size scaling, or resize-before-align preprocessing.
Do not use this sub-skill for low-shot balancing or ImageFolder validation; route those to data-preparation. Do not use it for checkpoint feature extraction or verification; route those to feature-extraction-verification. PaddlePaddle duplicate alignment parity belongs with paddle-workflows.
references/alignment-workflows.md when you need the expected identity-folder input/output layout, detector parameters, crop-size scaling, or validation checklist.references/troubleshooting.md when imports fail, landmarks are missing, multiple faces appear, corrupt images are skipped, colors look wrong, crop sizes are surprising, non-JPEG inputs are converted, or SyntaxWarnings appear.scripts/align_faces.py to batch-align a source_root/<identity>/<image> tree into a separate aligned destination tree.scripts/resize_faces.py before alignment when raw face images are very large and MTCNN detection is too slow.scripts/align_faces.py requires --repo-root pointing to a local face.evoLVe checkout that contains the MTCNN helper code and applications/align/{pnet,rnet,onet}.npy weights; the weights are not bundled here and no downloads are attempted..DS_Store-style files, and normalize aligned or resized outputs to .jpg.