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| import face_recognition | |
| import json | |
| import annoy | |
| from typing import Tuple | |
| EMBEDDING_DIMENSION=128 | |
| ANNOY_INDEX_FILE = "models/actors_annoy_index.ann" | |
| ANNOY_METADATA_FILE = "models/actors_annoy_metadata.json" | |
| ANNOY_MAPPING_FILE = "models/actors_mapping.json" | |
| def load_annoy_index( | |
| index_file = ANNOY_INDEX_FILE, | |
| metadata_file = ANNOY_METADATA_FILE, | |
| mapping_file = ANNOY_MAPPING_FILE | |
| ) -> Tuple[annoy.AnnoyIndex, dict]: | |
| """Load annoy index and associated mapping file""" | |
| with open(metadata_file) as f: | |
| annoy_index_metadata = json.load(f) | |
| annoy_index = annoy.AnnoyIndex(f=EMBEDDING_DIMENSION, **annoy_index_metadata) | |
| annoy_index.load(index_file) | |
| with open(mapping_file) as f: | |
| mapping = json.load(f) | |
| mapping = {int(k): v for k, v in mapping.items()} | |
| return annoy_index, mapping | |
| def analyze_image(image, annoy_index, n_matches: int = 1, num_jitters: int = 1, model: str = "large"): | |
| """Extract face location, embeddings, and top n_matches matches""" | |
| face_locations = face_recognition.face_locations(image) | |
| embeddings = face_recognition.face_encodings(image, num_jitters=num_jitters, model=model, known_face_locations=face_locations) | |
| matches = [] | |
| distances = [] | |
| for emb in embeddings: | |
| m, d = annoy_index.get_nns_by_vector(emb, n_matches, include_distances=True) | |
| matches.append(m) | |
| distances.append(d) | |
| return [dict(embeddings=e, matches=m, distances=d, face_locations=f) for e,m,d,f in zip(embeddings, matches, distances, face_locations)] |