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- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
399 lines
12 KiB
Python
Executable File
399 lines
12 KiB
Python
Executable File
#!/opt/homebrew/bin/python3.11
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"""
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TMDB Identity Integration Script
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Purpose:
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1. Fetch person images from TMDB /person/:id/images endpoint
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2. Download multiple images (different angles/shots)
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3. Extract ArcFace embeddings using InsightFace
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4. Store embeddings to reference_data JSONB
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5. Register Identity to PostgreSQL database
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Usage:
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python3 scripts/tmdb_identity_integration.py --tmdb-id 1234 --name "Maggie Cheung"
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python3 scripts/tmdb_identity_integration.py --search "張曼玉"
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"""
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import os
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import sys
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import json
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import argparse
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import requests
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import psycopg2
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from pathlib import Path
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from datetime import datetime
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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TMDB_API_KEY = os.getenv("TMDB_API_KEY")
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if not TMDB_API_KEY:
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print("⚠️ TMDB_API_KEY not found.")
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print("👉 Please set: export TMDB_API_KEY='your_api_key'")
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sys.exit(1)
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TMDB_BASE_URL = "https://api.themoviedb.org/3"
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TMDB_IMG_BASE_URL = "https://image.tmdb.org/t/p/original"
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DATABASE_URL = os.getenv("DATABASE_URL", "postgres://accusys@localhost:5432/momentry?options=-c%20search_path=dev")
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TEMP_DIR = Path("data/tmdb_images")
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TEMP_DIR.mkdir(parents=True, exist_ok=True)
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def search_person(query: str) -> dict | None:
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"""Search TMDB person by name"""
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url = f"{TMDB_BASE_URL}/search/person"
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params = {"query": query, "api_key": TMDB_API_KEY, "language": "zh-TW"}
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try:
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resp = requests.get(url, params=params)
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resp.raise_for_status()
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data = resp.json()
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if data.get("results"):
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return data["results"][0]
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return None
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except Exception as e:
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print(f"❌ Search failed: {e}")
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return None
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def get_person_details(tmdb_id: int) -> dict:
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"""Get TMDB person details"""
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url = f"{TMDB_BASE_URL}/person/{tmdb_id}"
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params = {"api_key": TMDB_API_KEY, "language": "zh-TW"}
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try:
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resp = requests.get(url, params=params)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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print(f"❌ Failed to get person details: {e}")
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return {}
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def get_person_images(tmdb_id: int) -> list[dict]:
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"""Get TMDB person images (multiple photos)"""
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url = f"{TMDB_BASE_URL}/person/{tmdb_id}/images"
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params = {"api_key": TMDB_API_KEY}
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try:
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resp = requests.get(url, params=params)
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resp.raise_for_status()
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data = resp.json()
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return data.get("profiles", [])
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except Exception as e:
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print(f"❌ Failed to get person images: {e}")
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return []
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def download_image(image_url: str, save_path: Path) -> bool:
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"""Download image from TMDB"""
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try:
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resp = requests.get(image_url, timeout=30)
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resp.raise_for_status()
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save_path.parent.mkdir(parents=True, exist_ok=True)
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with open(save_path, "wb") as f:
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f.write(resp.content)
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return True
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except Exception as e:
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print(f"❌ Download failed: {e}")
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return False
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def load_insightface():
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"""Load InsightFace model"""
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try:
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from insightface.app import FaceAnalysis
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print("🔧 Loading InsightFace buffalo_l...")
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app = FaceAnalysis(name="buffalo_l", providers=["CPUExecutionProvider"])
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app.prepare(ctx_id=0, det_size=(320, 320))
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print("✅ InsightFace loaded")
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return app
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except Exception as e:
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print(f"❌ Failed to load InsightFace: {e}")
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return None
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def extract_face_embedding(app, image_path: Path) -> dict | None:
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"""Extract ArcFace embedding from image"""
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try:
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import cv2
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img = cv2.imread(str(image_path))
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if img is None:
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print(f"❌ Cannot read image: {image_path}")
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return None
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faces = app.get(img)
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if not faces:
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print(f"⚠️ No face detected in: {image_path.name}")
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return None
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face = faces[0]
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embedding = face.embedding.tolist() if hasattr(face, "embedding") else None
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if not embedding:
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print(f"⚠️ No embedding in: {image_path.name}")
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return None
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bbox = face.bbox.astype(int)
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det_score = float(face.det_score) if hasattr(face, "det_score") else 0.9
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angle = detect_face_angle(bbox, img.shape)
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quality_score = evaluate_face_quality(face, img.shape)
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return {
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"embedding": embedding,
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"image_path": str(image_path),
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"image_url": f"{TMDB_IMG_BASE_URL}/{image_path.name}",
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"angle": angle,
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"quality_score": quality_score,
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"det_score": det_score,
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}
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except Exception as e:
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print(f"❌ Extraction failed: {e}")
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return None
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def detect_face_angle(bbox: np.ndarray, img_shape: tuple) -> str:
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"""Detect face angle (frontal, profile_left, profile_right, three_quarter)"""
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img_w = img_shape[1]
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face_center_x = (bbox[0] + bbox[2]) / 2
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left_dist = face_center_x
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right_dist = img_w - face_center_x
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ratio = left_dist / right_dist
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if ratio > 1.5:
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return "profile_right"
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elif ratio < 0.67:
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return "profile_left"
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elif ratio > 1.2 or ratio < 0.83:
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return "three_quarter"
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else:
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return "frontal"
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def evaluate_face_quality(face, img_shape: tuple) -> float:
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"""Evaluate face quality score (0.0-1.0)"""
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det_score = float(face.det_score) if hasattr(face, "det_score") else 0.9
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bbox = face.bbox.astype(int)
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face_size = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
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img_size = img_shape[0] * img_shape[1]
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size_ratio = face_size / img_size
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size_score = min(1.0, size_ratio * 20)
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quality = det_score * 0.7 + size_score * 0.3
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return min(1.0, max(0.0, quality))
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def calculate_centroid(embeddings: list[list[float]]) -> list[float]:
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"""Calculate centroid (average) of embeddings"""
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if not embeddings:
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return []
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embeddings_array = np.array(embeddings)
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centroid = np.mean(embeddings_array, axis=0)
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return centroid.tolist()
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def register_identity_to_db(
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name: str,
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tmdb_id: int,
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tmdb_profile: str,
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face_embeddings: list[dict],
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centroid: list[float],
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schema: str = "dev",
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) -> str | None:
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"""Register Identity to PostgreSQL"""
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conn = psycopg2.connect(DATABASE_URL)
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cur = conn.cursor()
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try:
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reference_data = {
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"face_embeddings": [
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{
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"embedding": emb["embedding"],
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"source": "tmdb_images",
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"image_url": emb["image_url"],
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"angle": emb["angle"],
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"quality_score": emb["quality_score"],
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"created_at": datetime.now().isoformat(),
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}
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for emb in face_embeddings
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],
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"image_urls": [emb["image_url"] for emb in face_embeddings],
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}
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sql = f"""
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INSERT INTO {schema}.identities (
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name, identity_type, source, status,
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face_embedding, reference_data, tmdb_id, tmdb_profile,
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created_at, updated_at
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) VALUES (
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%s, %s, %s, %s,
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%s, %s, %s, %s,
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NOW(), NOW()
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)
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ON CONFLICT (name) DO UPDATE SET
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face_embedding = EXCLUDED.face_embedding,
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reference_data = EXCLUDED.reference_data,
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tmdb_id = EXCLUDED.tmdb_id,
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tmdb_profile = EXCLUDED.tmdb_profile,
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updated_at = NOW()
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RETURNING uuid;
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"""
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embedding_str = "[" + ",".join(str(x) for x in centroid) + "]"
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cur.execute(
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sql,
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(
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name,
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"people",
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"tmdb",
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"confirmed",
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embedding_str,
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json.dumps(reference_data),
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tmdb_id,
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tmdb_profile,
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),
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)
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uuid = cur.fetchone()[0]
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conn.commit()
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print(f"✅ Identity registered: {name} (UUID: {uuid})")
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return uuid
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except Exception as e:
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print(f"❌ Database error: {e}")
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conn.rollback()
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return None
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finally:
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cur.close()
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conn.close()
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def main():
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parser = argparse.ArgumentParser(description="TMDB Identity Integration")
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parser.add_argument("--tmdb-id", type=int, help="TMDB Person ID (e.g., 1234)")
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parser.add_argument("--name", help="Person name (for registration)")
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parser.add_argument("--search", help="Search person by name")
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parser.add_argument("--limit", type=int, default=10, help="Max images to process")
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parser.add_argument("--schema", default="dev", help="Database schema (dev/public)")
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args = parser.parse_args()
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if not args.tmdb_id and not args.search:
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print("❌ Please provide --tmdb-id or --search")
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sys.exit(1)
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if args.search:
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print(f"🔍 Searching TMDB for: '{args.search}'")
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person = search_person(args.search)
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if not person:
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print("❌ Person not found")
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sys.exit(1)
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tmdb_id = person["id"]
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name = args.name or person["name"]
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print(f"✅ Found: {name} (TMDB ID: {tmdb_id})")
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else:
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tmdb_id = args.tmdb_id
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name = args.name
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if not name:
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print("🔧 Fetching person details...")
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details = get_person_details(tmdb_id)
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name = details.get("name", f"Person_{tmdb_id}")
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print(f"✅ Name: {name}")
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print(f"\n🔧 Fetching images for: {name} (TMDB ID: {tmdb_id})")
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images = get_person_images(tmdb_id)
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if not images:
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print("❌ No images found")
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sys.exit(1)
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print(f"✅ Found {len(images)} images")
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app = load_insightface()
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if not app:
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sys.exit(1)
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person_dir = TEMP_DIR / str(tmdb_id)
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person_dir.mkdir(parents=True, exist_ok=True)
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face_embeddings = []
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print(f"\n🔧 Processing images (limit: {args.limit})...")
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for i, img_data in enumerate(images[:args.limit]):
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file_path = img_data.get("file_path")
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if not file_path:
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continue
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image_url = f"{TMDB_IMG_BASE_URL}{file_path}"
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local_path = person_dir / Path(file_path).name
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print(f" [{i+1}/{min(len(images), args.limit)}] {file_path}")
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if not local_path.exists():
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print(" 🔧 Downloading...")
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if not download_image(image_url, local_path):
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continue
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print(" 🔧 Extracting embedding...")
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result = extract_face_embedding(app, local_path)
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if result:
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face_embeddings.append(result)
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print(f" ✅ Success: angle={result['angle']}, quality={result['quality_score']:.2f}")
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else:
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print(" ⚠️ Failed")
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if not face_embeddings:
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print("❌ No valid face embeddings extracted")
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sys.exit(1)
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print(f"\n✅ Extracted {len(face_embeddings)} embeddings")
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centroid = calculate_centroid([emb["embedding"] for emb in face_embeddings])
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details = get_person_details(tmdb_id)
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tmdb_profile = f"{TMDB_IMG_BASE_URL}{details.get('profile_path')}" if details.get("profile_path") else None
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print(f"\n🔧 Registering Identity to database (schema: {args.schema})...")
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uuid = register_identity_to_db(
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name=name,
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tmdb_id=tmdb_id,
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tmdb_profile=tmdb_profile,
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face_embeddings=face_embeddings,
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centroid=centroid,
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schema=args.schema,
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)
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if uuid:
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print("\n🎉 Integration completed!")
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print(f" Identity: {name}")
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print(f" UUID: {uuid}")
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print(f" TMDB ID: {tmdb_id}")
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print(f" Embeddings: {len(face_embeddings)}")
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print(f" Centroid dimension: {len(centroid)}")
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else:
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print("\n❌ Integration failed")
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if __name__ == "__main__":
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main() |