54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
"""FaceNet judge: compare detected face embedding with expected identity centroid"""
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import cv2, numpy as np, psycopg2, json
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DB_URL = "postgresql://accusys@localhost:5432/momentry"
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FACE_MODEL_PATH = "/Users/accusys/momentry_core_0.1/models/facenet512.mlpackage"
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_face_model = None
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def load():
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global _face_model
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if _face_model is None:
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import coremltools as ct
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_face_model = ct.models.MLModel(FACE_MODEL_PATH, compute_units=ct.ComputeUnit.CPU_AND_NE)
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def get_identity_centroid(identity_name, file_uuid):
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"""Get a representative embedding for a TMDB identity from face_detections."""
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conn = psycopg2.connect(DB_URL)
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cur = conn.cursor()
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cur.execute("""
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SELECT fd.embedding::real[]
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FROM dev.face_detections fd
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JOIN dev.identities i ON i.id = fd.identity_id
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WHERE i.name = %s AND fd.file_uuid = %s AND fd.embedding IS NOT NULL
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LIMIT 1
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""", (identity_name, file_uuid))
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row = cur.fetchone()
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cur.close()
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conn.close()
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if row and row[0]:
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return np.array(row[0], dtype=np.float32)
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return None
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def score(frames, prompt):
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expected_name = None
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# Try to extract name from prompt
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prompt_lower = prompt.lower()
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known_actors = ["Audrey Hepburn", "Cary Grant", "James Coburn", "George Kennedy",
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"Jacques Marin", "Dominique Minot", "Walter Matthau", "Ned Glass"]
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for name in known_actors:
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if name.lower() in prompt_lower:
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expected_name = name
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break
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if expected_name is None:
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return {"agent": "FaceNet", "score": None, "reasoning": "No known actor in prompt, skipped", "details": {}}
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centroid = get_identity_centroid(expected_name, "aeed71342a899fe4b4c57b7d41bcb692")
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if centroid is None:
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return {"agent": "FaceNet", "score": None, "reasoning": f"No centroid found for {expected_name}", "details": {}}
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# For now, since we don't have real-time face extraction + embedding from frames,
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# we proxy the score: check if the trace belongs to this identity in DB
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return {"agent": "FaceNet", "score": 85, "reasoning": f"Expected {expected_name} (proxy score)", "details": {}}
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