feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
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Warren
367
scripts/test_face_recognition_integration.py
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367
scripts/test_face_recognition_integration.py
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#!/usr/bin/env python3
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"""
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測試人臉識別完整集成流程
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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 numpy as np
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from pathlib import Path
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# 添加項目根目錄到 Python 路徑
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# 測試數據庫連接
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try:
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import psycopg2
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from psycopg2.extras import Json
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print("✅ psycopg2 已安裝")
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except ImportError:
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print("❌ psycopg2 未安裝，請運行: pip install psycopg2-binary")
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sys.exit(1)
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# 測試 InsightFace
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try:
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import insightface
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print("✅ insightface 已安裝")
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except ImportError:
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print("❌ insightface 未安裝，請運行: pip install insightface")
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sys.exit(1)
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# 測試 ONNX Runtime
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try:
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import onnxruntime as ort
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print("✅ onnxruntime 已安裝")
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# 檢查可用的執行提供者
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available_providers = ort.get_available_providers()
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print(f"✅ 可用的執行提供者: {available_providers}")
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# 檢查 MPS 支援
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if "CoreMLExecutionProvider" in available_providers:
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print("✅ CoreML (MPS) 支援可用")
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elif "CUDAExecutionProvider" in available_providers:
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print("✅ CUDA 支援可用")
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else:
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print("⚠️ 僅 CPU 支援可用")
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except ImportError:
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print("❌ onnxruntime 未安裝，請運行: pip install onnxruntime")
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sys.exit(1)
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def test_database_connection():
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"""測試數據庫連接"""
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print("\n=== 測試數據庫連接 ===")
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try:
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conn = psycopg2.connect(
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host="localhost",
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port=5432,
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database="momentry",
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user="accusys",
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password="accusys",
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)
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cursor = conn.cursor()
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# 檢查表是否存在
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cursor.execute("""
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SELECT table_name
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FROM information_schema.tables
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WHERE table_schema = 'public'
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AND table_name LIKE 'face_%'
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ORDER BY table_name;
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""")
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tables = cursor.fetchall()
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print(f"✅ 找到 {len(tables)} 個人臉相關表:")
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for table in tables:
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print(f" - {table[0]}")
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# 檢查函數是否存在
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cursor.execute("""
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SELECT proname
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FROM pg_proc
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WHERE proname LIKE '%face%'
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ORDER BY proname;
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""")
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functions = cursor.fetchall()
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print(f"✅ 找到 {len(functions)} 個人臉相關函數:")
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for func in functions:
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print(f" - {func[0]}")
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# 測試插入和查詢
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cursor.execute("""
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SELECT find_or_create_face_identity(
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'integration_test_001',
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'Integration Test Person',
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NULL,
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'{"age": 25, "gender": "female", "test": true}'::jsonb,
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'{"source": "integration_test"}'::jsonb
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) AS identity_id;
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""")
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identity_id = cursor.fetchone()[0]
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print(f"✅ 成功創建人臉身份，ID: {identity_id}")
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# 檢查插入的數據
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cursor.execute("""
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SELECT id, face_id, name, attributes->>'gender' as gender,
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attributes->>'age' as age, attributes->>'test' as test
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FROM face_identities
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WHERE face_id = 'integration_test_001';
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""")
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result = cursor.fetchone()
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print(
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f"✅ 查詢結果: ID={result[0]}, FaceID={result[1]}, Name={result[2]}, Gender={result[3]}, Age={result[4]}, Test={result[5]}"
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)
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# 清理測試數據
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cursor.execute(
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"DELETE FROM face_identities WHERE face_id = 'integration_test_001';"
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)
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conn.commit()
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print("✅ 清理測試數據完成")
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cursor.close()
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conn.close()
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return True
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except Exception as e:
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print(f"❌ 數據庫連接測試失敗: {e}")
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return False
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def test_insightface_model():
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"""測試 InsightFace 模型"""
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print("\n=== 測試 InsightFace 模型 ===")
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try:
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# 創建測試圖像（隨機數據）
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test_image = np.random.randint(0, 255, (640, 480, 3), dtype=np.uint8)
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print(f"✅ 創建測試圖像: {test_image.shape}")
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# 初始化模型
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print("正在初始化 InsightFace 模型...")
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model = insightface.app.FaceAnalysis(name="buffalo_l")
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model.prepare(ctx_id=-1) # -1 表示 CPU
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print("✅ InsightFace 模型初始化成功")
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# 測試模型推理（使用隨機圖像）
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print("正在進行模型推理測試...")
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faces = model.get(test_image)
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print(f"✅ 模型推理完成，檢測到 {len(faces)} 個人臉")
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if len(faces) > 0:
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face = faces[0]
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print(f"✅ 人臉屬性:")
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print(f" - 邊界框: {face.bbox}")
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print(f" - 置信度: {face.det_score:.4f}")
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print(
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f" - 嵌入向量維度: {face.embedding.shape if hasattr(face, 'embedding') else 'N/A'}"
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)
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if hasattr(face, "age"):
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print(f" - 年齡: {face.age}")
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if hasattr(face, "gender"):
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print(f" - 性別: {face.gender}")
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if hasattr(face, "pose"):
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print(f" - 姿態: {face.pose}")
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return True
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except Exception as e:
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print(f"❌ InsightFace 模型測試失敗: {e}")
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return False
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def test_face_processor():
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"""測試人臉處理器"""
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print("\n=== 測試人臉處理器 ===")
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try:
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# 導入處理器
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from scripts.face_recognition_processor import FaceRecognitionProcessor
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# 初始化處理器
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print("正在初始化人臉識別處理器...")
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processor = FaceRecognitionProcessor()
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# 加載模型（不使用 MPS）
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print("正在加載模型...")
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processor.load_models(use_mps=False)
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print(f"✅ 處理器初始化成功")
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print(f" - 啟用識別: {processor.enable_recognition}")
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print(f" - 啟用追蹤: {processor.enable_tracking}")
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print(f" - 啟用聚類: {processor.enable_clustering}")
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# 創建測試視頻數據
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test_video_data = {
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"video_path": "/tmp/test_video.mp4", # 虛擬路徑
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"video_uuid": "test_video_001",
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"frame_count": 100,
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"fps": 30.0,
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}
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print(f"✅ 創建測試視頻數據: {test_video_data['video_uuid']}")
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# 測試處理器方法
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print("測試處理器方法...")
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# 測試人臉檢測
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test_image = np.random.randint(0, 255, (640, 480, 3), dtype=np.uint8)
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detections = processor.detect_faces(test_image)
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print(f"✅ 人臉檢測測試完成，檢測到 {len(detections)} 個人臉")
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if len(detections) > 0:
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detection = detections[0]
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print(f"✅ 檢測結果示例:")
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print(
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f" - 位置: x={detection['x']}, y={detection['y']}, width={detection['width']}, height={detection['height']}"
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)
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print(f" - 置信度: {detection['confidence']:.4f}")
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if "embedding" in detection and detection["embedding"] is not None:
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embedding = detection["embedding"]
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if hasattr(embedding, "shape"):
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print(f" - 嵌入向量: {embedding.shape}")
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else:
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print(f" - 嵌入向量長度: {len(embedding)}")
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else:
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print(f" - 嵌入向量: N/A")
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return True
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except Exception as e:
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print(f"❌ 人臉處理器測試失敗: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_api_endpoints():
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"""測試 API 端點配置"""
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print("\n=== 測試 API 端點配置 ===")
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try:
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# 檢查 Rust 代碼編譯
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print("檢查 Rust 代碼編譯狀態...")
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# 讀取 API 代碼
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api_file = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
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"src",
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"api",
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"face_recognition.rs",
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)
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if os.path.exists(api_file):
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with open(api_file, "r") as f:
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content = f.read()
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# 檢查關鍵函數是否存在（根據實際代碼）
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endpoints = [
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"register_face_api",
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"recognize_faces",
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"search_faces",
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"get_face_details",
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"list_faces",
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"delete_face",
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"get_recognition_results",
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"store_recognition_results",
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]
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found_endpoints = []
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for endpoint in endpoints:
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if endpoint in content:
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found_endpoints.append(endpoint)
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print(f"✅ 找到 {len(found_endpoints)}/{len(endpoints)} 個 API 端點:")
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for endpoint in found_endpoints:
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print(f" - {endpoint}")
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if len(found_endpoints) == len(endpoints):
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print("✅ 所有 API 端點都已定義")
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else:
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missing = set(endpoints) - set(found_endpoints)
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print(f"⚠️ 缺少端點: {missing}")
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# 檢查路由配置
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server_file = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
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"src",
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"api",
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"server.rs",
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)
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if os.path.exists(server_file):
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with open(server_file, "r") as f:
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content = f.read()
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if "face_recognition" in content and "merge" in content:
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print("✅ API 路由已正確配置")
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else:
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print("⚠️ API 路由配置可能不完整")
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return True
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except Exception as e:
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print(f"❌ API 端點測試失敗: {e}")
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return False
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def main():
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"""主測試函數"""
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print("=" * 60)
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print("人臉識別集成測試")
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print("=" * 60)
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tests = [
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("數據庫連接", test_database_connection),
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("InsightFace 模型", test_insightface_model),
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("人臉處理器", test_face_processor),
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("API 端點配置", test_api_endpoints),
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]
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results = []
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for test_name, test_func in tests:
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try:
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success = test_func()
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results.append((test_name, success))
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except Exception as e:
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print(f"❌ {test_name} 測試異常: {e}")
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results.append((test_name, False))
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print("\n" + "=" * 60)
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print("測試結果摘要")
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print("=" * 60)
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passed = 0
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for test_name, success in results:
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status = "✅ 通過" if success else "❌ 失敗"
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print(f"{test_name}: {status}")
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if success:
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passed += 1
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print(f"\n總計: {passed}/{len(results)} 個測試通過")
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if passed == len(results):
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print("\n🎉 所有測試通過！人臉識別集成準備就緒。")
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return 0
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else:
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print(f"\n⚠️ 有 {len(results) - passed} 個測試失敗，請檢查問題。")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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