features/README.md

Feature extractors

One script per family. All read data/artwork_metadata.csv + data/images/, write to data/features/, checkpoint regularly, and resume if re-run.

family	script	dims	what it measures
hand-crafted	extract_handcrafted.py	380	color (HSV joint + grey-world + per-channel histograms), light (brightness/contrast/darkness/edge density), symmetry, flatness (SLIC superpixel LAB uniformity), texture (FFT radial bands + 59-bin nri-uniform LBP), lines (Hough count/density, straight ratio, edge-angle histogram)
CLIP	extract_clip.py	512 + 27	ViT-B/16 image embedding + zero-shot cosine scores against 27 hand-written religious-attribute prompts
DINOv2	extract_dino.py	768	self-supervised CLS token — visual style/appearance similarity
faces	extract_faces.py	39	YuNet detections summarized: count, coverage, sizes, positions, tilt/frontality, arrangement, spatial histograms
pose	extract_pose.py	39	YOLOv8-pose, largest figure only: torso-normalised 17-keypoint skeleton + figure area + detection-quality columns + person count

Notes:

• Hand-crafted features use the corrected preprocessing pipeline: images are border-cropped, guardedly background-masked (see preprocessing/), and capped at 1024 px on the longest side so texture/edge features measure the artwork rather than the museum's scan resolution.
• The pose vector stores quality indicators (main_kpt_conf, main_torso_px) because photo-trained detectors are unreliable on stylised bodies — filter on them before interpreting posture.