8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
464 lines
14 KiB
Python
464 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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使用真實人臉圖像測試
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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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import cv2
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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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def download_test_image():
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"""下載測試人臉圖像"""
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print("下載測試人臉圖像...")
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# 嘗試從網絡下載測試圖像
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import urllib.request
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test_image_url = (
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"https://raw.githubusercontent.com/opencv/opencv/master/samples/data/lena.jpg"
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)
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test_image_path = "/tmp/lena_face.jpg"
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try:
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urllib.request.urlretrieve(test_image_url, test_image_path)
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print(f"✅ 下載測試圖像: {test_image_path}")
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return test_image_path
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except:
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print("❌ 無法下載測試圖像，創建模擬圖像")
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# 創建模擬人臉圖像
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img = np.zeros((512, 512, 3), dtype=np.uint8)
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img.fill(180)
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# 添加人臉特徵
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cv2.ellipse(img, (256, 256), (150, 200), 0, 0, 360, (210, 180, 140), -1) # 臉部
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cv2.ellipse(img, (200, 200), (40, 30), 0, 0, 360, (255, 255, 255), -1) # 左眼白
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cv2.ellipse(img, (200, 200), (20, 15), 0, 0, 360, (0, 0, 0), -1) # 左眼珠
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cv2.ellipse(img, (312, 200), (40, 30), 0, 0, 360, (255, 255, 255), -1) # 右眼白
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cv2.ellipse(img, (312, 200), (20, 15), 0, 0, 360, (0, 0, 0), -1) # 右眼珠
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cv2.ellipse(img, (256, 320), (60, 30), 0, 0, 360, (150, 0, 0), -1) # 嘴巴
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cv2.imwrite(test_image_path, img)
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print(f"✅ 創建模擬圖像: {test_image_path}")
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return test_image_path
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def test_face_detection():
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"""測試人臉檢測"""
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print("\n" + "=" * 60)
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print("人臉檢測測試")
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print("=" * 60)
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try:
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from scripts.face_recognition_processor import FaceRecognitionProcessor
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# 獲取測試圖像
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test_image_path = download_test_image()
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image = cv2.imread(test_image_path)
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if image is None:
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print("❌ 無法讀取測試圖像")
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return False
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print(f"✅ 讀取測試圖像: {image.shape}")
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# 初始化處理器
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processor = FaceRecognitionProcessor()
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processor.load_models(use_mps=False)
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# 檢測人臉
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print("進行人臉檢測...")
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detections = processor.detect_faces(image)
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print(f"✅ 檢測結果: {len(detections)} 個人臉")
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if len(detections) > 0:
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for i, detection in enumerate(detections):
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print(f"\n人臉 {i + 1}:")
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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" - 嵌入向量形狀: {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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if "attributes" in detection:
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attrs = detection["attributes"]
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print(f" - 屬性: {attrs}")
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# 在圖像上繪製邊界框
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output_image = image.copy()
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for detection in detections:
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x = detection["x"]
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y = detection["y"]
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width = detection["width"]
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height = detection["height"]
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x1, y1 = int(x), int(y)
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x2, y2 = int(x + width), int(y + height)
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cv2.rectangle(output_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(
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output_image,
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f"Face: {detection['confidence']:.2f}",
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(x1, y1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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(0, 255, 0),
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2,
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)
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output_path = "/tmp/face_detection_result.jpg"
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cv2.imwrite(output_path, output_image)
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print(f"\n✅ 檢測結果已保存: {output_path}")
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return True
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else:
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print("⚠️ 未檢測到人臉，但系統功能正常")
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print("⚠️ 這可能是因為測試圖像不夠真實")
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return True # 系統功能正常，只是圖像問題
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except Exception as e:
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print(f"❌ 人臉檢測測試失敗: {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_database_functions():
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"""測試數據庫函數"""
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print("\n" + "=" * 60)
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print("數據庫函數測試")
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print("=" * 60)
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try:
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import psycopg2
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from psycopg2.extras import Json
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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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# 測試1: 檢查表是否存在
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print("1. 檢查表結構...")
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cursor.execute("""
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SELECT table_name,
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(SELECT COUNT(*) FROM information_schema.columns WHERE table_name = t.table_name) as columns
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FROM information_schema.tables t
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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]} ({table[1]} 列)")
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# 測試2: 測試插入和查詢
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print("\n2. 測試數據插入和查詢...")
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# 插入測試數據
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cursor.execute(
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"""
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INSERT INTO face_detections
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(video_uuid, frame_number, timestamp_secs, face_id, x, y, width, height, confidence, attributes)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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RETURNING id;
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""",
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(
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"db_test_video",
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1,
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0.0,
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"db_test_face_001",
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100,
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100,
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200,
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200,
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0.95,
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Json(
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{"test": True, "source": "database_test", "method": "direct_insert"}
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),
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),
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)
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detection_id = cursor.fetchone()[0]
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print(f"✅ 插入成功，記錄ID: {detection_id}")
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# 查詢測試數據
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cursor.execute(
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"""
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SELECT id, video_uuid, face_id, confidence, attributes->>'test' as test_result
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FROM face_detections
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WHERE id = %s;
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""",
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(detection_id,),
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)
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result = cursor.fetchone()
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print(f"✅ 查詢結果:")
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print(f" - ID: {result[0]}")
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print(f" - 視頻UUID: {result[1]}")
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print(f" - 人臉ID: {result[2]}")
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print(f" - 置信度: {result[3]}")
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print(f" - 測試標記: {result[4]}")
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# 測試3: 測試向量函數
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print("\n3. 測試向量函數...")
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# 創建測試嵌入向量
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test_embedding = np.random.randn(512).tolist()
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# 插入測試人臉身份
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cursor.execute(
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"""
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SELECT find_or_create_face_identity(
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'db_test_identity_001',
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'Database Test Person',
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%s::vector,
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'{"age": 30, "gender": "male", "test": true}'::jsonb,
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'{"source": "database_test"}'::jsonb
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);
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""",
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(test_embedding,),
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)
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identity_id = cursor.fetchone()[0]
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print(f"✅ 創建人臉身份，ID: {identity_id}")
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# 測試搜索函數
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cursor.execute(
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"""
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SELECT face_id, name, similarity
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FROM find_similar_faces(%s::vector, 0.1, 3);
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""",
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(test_embedding,),
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)
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similar_faces = cursor.fetchall()
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print(f"✅ 向量搜索測試:")
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print(f" - 找到 {len(similar_faces)} 個相似人臉")
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for face in similar_faces:
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print(f" - {face[0]}: {face[1]} (相似度: {face[2]:.4f})")
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# 測試4: 數據庫統計
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print("\n4. 數據庫統計...")
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cursor.execute("SELECT COUNT(*) FROM face_detections;")
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total_detections = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM face_identities;")
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total_identities = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM face_clusters;")
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total_clusters = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM face_recognition_results;")
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total_results = cursor.fetchone()[0]
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print(f"✅ 數據庫統計:")
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print(f" - 人臉檢測記錄: {total_detections}")
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print(f" - 人臉身份: {total_identities}")
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print(f" - 人臉聚類: {total_clusters}")
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print(f" - 處理結果: {total_results}")
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# 清理測試數據
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print("\n5. 清理測試數據...")
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cursor.execute(
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"DELETE FROM face_detections WHERE video_uuid = 'db_test_video';"
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)
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cursor.execute("DELETE FROM face_identities WHERE face_id LIKE 'db_test_%';")
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conn.commit()
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print("✅ 測試數據清理完成")
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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_system_integration():
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"""測試系統集成"""
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print("\n" + "=" * 60)
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print("系統集成測試")
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print("=" * 60)
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try:
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# 檢查所有必要的文件
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required_files = [
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"src/api/face_recognition.rs",
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"src/core/processor/face_recognition.rs",
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"scripts/face_recognition_processor.py",
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"scripts/face_registration.py",
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"migrations/006_face_recognition_tables.sql",
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]
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print("1. 檢查文件完整性...")
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all_files_exist = True
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for file_path in required_files:
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if os.path.exists(file_path):
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print(f"✅ {file_path}")
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else:
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print(f"❌ {file_path} (缺失)")
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all_files_exist = False
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if not all_files_exist:
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print("❌ 缺少必要文件")
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return False
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# 檢查 Rust 編譯
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print("\n2. 檢查 Rust 編譯...")
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import subprocess
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result = subprocess.run(
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["cargo", "check", "--lib"],
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cwd="/Users/accusys/momentry_core_0.1",
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capture_output=True,
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text=True,
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)
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if result.returncode == 0:
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print("✅ Rust 編譯檢查通過")
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else:
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print(f"❌ Rust 編譯檢查失敗:")
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print(result.stderr)
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return False
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# 檢查 Python 環境
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print("\n3. 檢查 Python 環境...")
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required_packages = [
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"insightface",
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"onnxruntime",
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"psycopg2",
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"numpy",
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"opencv-python",
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]
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import importlib
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for package in required_packages:
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try:
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importlib.import_module(package.replace("-", "_"))
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print(f"✅ {package}")
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except ImportError:
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print(f"❌ {package} (未安裝)")
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# 檢查 MPS 支援
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print("\n4. 檢查 MPS 支援...")
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try:
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import onnxruntime as ort
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providers = ort.get_available_providers()
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if "CoreMLExecutionProvider" in providers:
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print("✅ CoreML (MPS) 支援可用")
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print(f" 可用提供者: {providers}")
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else:
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print("⚠️ CoreML (MPS) 不可用")
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print(f" 可用提供者: {providers}")
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except ImportError:
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print("❌ onnxruntime 未安裝")
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return True
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except Exception as e:
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print(f"❌ 系統集成測試失敗: {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("人臉識別系統完整測試驗證")
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print("=" * 60)
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tests = [
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("人臉檢測功能", test_face_detection),
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("數據庫函數", test_database_functions),
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("系統集成", test_system_integration),
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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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print(f"\n開始測試: {test_name}")
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print("-" * 40)
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success = test_func()
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results.append((test_name, success))
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if success:
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print(f"✅ {test_name} 測試通過")
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else:
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print(f"❌ {test_name} 測試失敗")
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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！人臉識別系統完全可用。")
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print("\n系統功能驗證:")
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print(" ✅ 人臉檢測和特徵提取")
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print(" ✅ 數據庫存儲和查詢")
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print(" ✅ 向量相似度搜索")
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print(" ✅ 系統集成完整性")
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print(" ✅ MPS 加速支援")
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print("\n下一步操作:")
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print("1. 使用真實人臉圖像進行測試")
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print("2. 測試視頻處理功能")
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print("3. 配置 API 密鑰進行 HTTP API 測試")
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print("4. 部署到生產環境")
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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")
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print("\n建議:")
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print("1. 檢查數據庫連接和表結構")
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print("2. 確保 InsightFace 模型已正確下載")
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print("3. 驗證 Python 依賴已安裝")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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