e75c4d6f07
- 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
418 lines
13 KiB
Python
418 lines
13 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
端到端人臉識別測試
|
||
測試完整的人臉識別流程：註冊 -> 識別 -> 搜索
|
||
"""
|
||
|
||
import os
|
||
import sys
|
||
import numpy as np
|
||
import cv2
|
||
|
||
# 添加項目根目錄到 Python 路徑
|
||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||
|
||
|
||
def create_test_image_with_faces():
|
||
"""創建帶有人臉的測試圖像"""
|
||
print("創建測試圖像...")
|
||
|
||
# 創建一個簡單的測試圖像（640x480）
|
||
img = np.zeros((480, 640, 3), dtype=np.uint8)
|
||
|
||
# 添加一些"人臉"（簡單的橢圓形）
|
||
# 人臉1
|
||
cv2.ellipse(img, (200, 200), (80, 100), 0, 0, 360, (255, 200, 150), -1)
|
||
cv2.ellipse(img, (170, 170), (15, 10), 0, 0, 360, (0, 0, 0), -1) # 左眼
|
||
cv2.ellipse(img, (230, 170), (15, 10), 0, 0, 360, (0, 0, 0), -1) # 右眼
|
||
cv2.ellipse(img, (200, 230), (30, 15), 0, 0, 360, (0, 0, 0), -1) # 嘴巴
|
||
|
||
# 人臉2
|
||
cv2.ellipse(img, (450, 300), (70, 90), 0, 0, 360, (200, 220, 180), -1)
|
||
cv2.ellipse(img, (420, 270), (12, 8), 0, 0, 360, (0, 0, 0), -1) # 左眼
|
||
cv2.ellipse(img, (480, 270), (12, 8), 0, 0, 360, (0, 0, 0), -1) # 右眼
|
||
cv2.ellipse(img, (450, 330), (25, 12), 0, 0, 360, (0, 0, 0), -1) # 嘴巴
|
||
|
||
# 保存測試圖像
|
||
test_image_path = "/tmp/test_face_image.jpg"
|
||
cv2.imwrite(test_image_path, img)
|
||
print(f"✅ 測試圖像已保存到: {test_image_path}")
|
||
|
||
return test_image_path, img
|
||
|
||
|
||
def test_face_registration():
|
||
"""測試人臉註冊"""
|
||
print("\n=== 測試人臉註冊 ===")
|
||
|
||
try:
|
||
from scripts.face_registration import FaceRegistration
|
||
|
||
# 創建測試圖像
|
||
image_path, img = create_test_image_with_faces()
|
||
|
||
# 初始化註冊器
|
||
print("初始化人臉註冊器...")
|
||
registration = FaceRegistration()
|
||
|
||
# 加載模型
|
||
print("加載模型...")
|
||
registration.load_models(use_mps=False)
|
||
|
||
# 註冊人臉
|
||
print("註冊人臉...")
|
||
result = registration.register_face(
|
||
image_path=image_path,
|
||
name="Test Person 1",
|
||
metadata={"source": "test", "age": 30, "gender": "male"},
|
||
)
|
||
|
||
if result["success"]:
|
||
print("✅ 人臉註冊成功")
|
||
print(f" - Face ID: {result.get('face_id')}")
|
||
print(f" - 嵌入向量維度: {len(result.get('embedding', []))}")
|
||
print(f" - 屬性: {result.get('attributes', {})}")
|
||
|
||
# 保存嵌入向量供後續測試使用
|
||
embedding = result.get("embedding", [])
|
||
if embedding:
|
||
np.save("/tmp/test_face_embedding.npy", embedding)
|
||
print("✅ 嵌入向量已保存")
|
||
|
||
return True, result
|
||
else:
|
||
print(f"❌ 人臉註冊失敗: {result.get('message', 'Unknown error')}")
|
||
return False, None
|
||
|
||
except Exception as e:
|
||
print(f"❌ 人臉註冊測試失敗: {e}")
|
||
import traceback
|
||
|
||
traceback.print_exc()
|
||
return False, None
|
||
|
||
|
||
def test_face_recognition():
|
||
"""測試人臉識別"""
|
||
print("\n=== 測試人臉識別 ===")
|
||
|
||
try:
|
||
from scripts.face_recognition_processor import FaceRecognitionProcessor
|
||
|
||
# 創建測試圖像
|
||
image_path, img = create_test_image_with_faces()
|
||
|
||
# 初始化處理器
|
||
print("初始化人臉識別處理器...")
|
||
processor = FaceRecognitionProcessor(
|
||
enable_recognition=True, enable_tracking=True, enable_clustering=True
|
||
)
|
||
|
||
# 加載模型
|
||
print("加載模型...")
|
||
processor.load_models(use_mps=False)
|
||
|
||
# 讀取圖像
|
||
print("讀取測試圖像...")
|
||
image = cv2.imread(image_path)
|
||
if image is None:
|
||
print("❌ 無法讀取測試圖像")
|
||
return False
|
||
|
||
# 檢測人臉
|
||
print("檢測人臉...")
|
||
detections = processor.detect_faces(image)
|
||
|
||
print(f"✅ 檢測到 {len(detections)} 個人臉")
|
||
|
||
if len(detections) > 0:
|
||
for i, detection in enumerate(detections):
|
||
print(f"\n人臉 {i + 1}:")
|
||
print(
|
||
f" - 位置: x={detection['x']}, y={detection['y']}, width={detection['width']}, height={detection['height']}"
|
||
)
|
||
print(f" - 置信度: {detection['confidence']:.4f}")
|
||
|
||
if "embedding" in detection and detection["embedding"] is not None:
|
||
embedding = detection["embedding"]
|
||
if hasattr(embedding, "shape"):
|
||
print(f" - 嵌入向量維度: {embedding.shape}")
|
||
else:
|
||
print(f" - 嵌入向量長度: {len(embedding)}")
|
||
|
||
if "attributes" in detection:
|
||
attrs = detection["attributes"]
|
||
print(f" - 屬性: {attrs}")
|
||
|
||
# 測試人臉追蹤（模擬多幀）
|
||
print("\n測試人臉追蹤...")
|
||
# 創建模擬幀數據
|
||
frames = [
|
||
{"frame_id": 1, "faces": detections},
|
||
{"frame_id": 2, "faces": detections}, # 簡單重複使用相同的檢測
|
||
]
|
||
tracked_frames = processor.track_faces(frames)
|
||
print(f"✅ 追蹤完成，處理了 {len(tracked_frames)} 幀")
|
||
|
||
# 測試人臉聚類
|
||
print("\n測試人臉聚類...")
|
||
# 創建模擬多幀檢測數據
|
||
all_frames = []
|
||
for i in range(3): # 模擬3幀
|
||
frame_data = {
|
||
"frame_id": i + 1,
|
||
"faces": detections if i == 0 else [], # 只在第一幀有檢測
|
||
}
|
||
all_frames.append(frame_data)
|
||
|
||
clusters = processor.cluster_faces(all_frames)
|
||
print(f"✅ 創建 {len(clusters)} 個聚類")
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"❌ 人臉識別測試失敗: {e}")
|
||
import traceback
|
||
|
||
traceback.print_exc()
|
||
return False
|
||
|
||
|
||
def test_database_operations():
|
||
"""測試數據庫操作"""
|
||
print("\n=== 測試數據庫操作 ===")
|
||
|
||
try:
|
||
import psycopg2
|
||
from psycopg2.extras import Json
|
||
|
||
# 連接數據庫
|
||
conn = psycopg2.connect(
|
||
host="localhost",
|
||
port=5432,
|
||
database="momentry",
|
||
user="accusys",
|
||
password="accusys",
|
||
)
|
||
|
||
cursor = conn.cursor()
|
||
|
||
# 測試1: 插入人臉檢測記錄
|
||
print("測試插入人臉檢測記錄...")
|
||
cursor.execute(
|
||
"""
|
||
INSERT INTO face_detections
|
||
(video_uuid, frame_number, timestamp_secs, face_id, x, y, width, height, confidence, attributes)
|
||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||
RETURNING id;
|
||
""",
|
||
(
|
||
"test_video_001",
|
||
1,
|
||
0.0,
|
||
"test_face_001",
|
||
100,
|
||
100,
|
||
200,
|
||
200,
|
||
0.95,
|
||
Json({"age": 30, "gender": "male", "test": True}),
|
||
),
|
||
)
|
||
|
||
detection_id = cursor.fetchone()[0]
|
||
print(f"✅ 插入人臉檢測記錄成功，ID: {detection_id}")
|
||
|
||
# 測試2: 查詢人臉檢測記錄
|
||
print("\n測試查詢人臉檢測記錄...")
|
||
cursor.execute(
|
||
"""
|
||
SELECT id, video_uuid, frame_number, face_id, confidence, attributes
|
||
FROM face_detections
|
||
WHERE id = %s;
|
||
""",
|
||
(detection_id,),
|
||
)
|
||
|
||
result = cursor.fetchone()
|
||
print("✅ 查詢結果:")
|
||
print(f" - ID: {result[0]}")
|
||
print(f" - 視頻UUID: {result[1]}")
|
||
print(f" - 幀號: {result[2]}")
|
||
print(f" - 人臉ID: {result[3]}")
|
||
print(f" - 置信度: {result[4]}")
|
||
print(f" - 屬性: {result[5]}")
|
||
|
||
# 測試3: 測試向量搜索函數
|
||
print("\n測試向量搜索函數...")
|
||
|
||
# 創建一個測試嵌入向量
|
||
test_embedding = np.random.randn(512).tolist()
|
||
|
||
# 首先插入一個帶有嵌入向量的人臉身份
|
||
cursor.execute(
|
||
"""
|
||
SELECT find_or_create_face_identity(
|
||
'test_search_001',
|
||
'Search Test Person',
|
||
%s::vector,
|
||
'{"age": 25, "gender": "female", "test": true}'::jsonb,
|
||
'{"source": "search_test"}'::jsonb
|
||
);
|
||
""",
|
||
(test_embedding,),
|
||
)
|
||
|
||
identity_id = cursor.fetchone()[0]
|
||
print(f"✅ 創建測試人臉身份，ID: {identity_id}")
|
||
|
||
# 測試搜索相似人臉
|
||
cursor.execute(
|
||
"""
|
||
SELECT * FROM find_similar_faces(
|
||
%s::vector,
|
||
0.5, -- similarity_threshold
|
||
5 -- limit_count
|
||
);
|
||
""",
|
||
(test_embedding,),
|
||
)
|
||
|
||
similar_faces = cursor.fetchall()
|
||
print(f"✅ 找到 {len(similar_faces)} 個相似人臉")
|
||
|
||
for face in similar_faces:
|
||
print(f" - {face[0]}: {face[1]} (相似度: {face[2]:.4f})")
|
||
|
||
# 清理測試數據
|
||
print("\n清理測試數據...")
|
||
cursor.execute(
|
||
"DELETE FROM face_detections WHERE video_uuid = 'test_video_001';"
|
||
)
|
||
cursor.execute("DELETE FROM face_identities WHERE face_id LIKE 'test_%';")
|
||
conn.commit()
|
||
print("✅ 測試數據清理完成")
|
||
|
||
cursor.close()
|
||
conn.close()
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"❌ 數據庫操作測試失敗: {e}")
|
||
import traceback
|
||
|
||
traceback.print_exc()
|
||
return False
|
||
|
||
|
||
def test_mps_acceleration():
|
||
"""測試 MPS 加速"""
|
||
print("\n=== 測試 MPS 加速 ===")
|
||
|
||
try:
|
||
import onnxruntime as ort
|
||
|
||
available_providers = ort.get_available_providers()
|
||
print(f"可用的執行提供者: {available_providers}")
|
||
|
||
if "CoreMLExecutionProvider" in available_providers:
|
||
print("✅ CoreML (MPS) 支援可用")
|
||
|
||
# 測試使用 MPS 初始化模型
|
||
from scripts.face_recognition_processor import FaceRecognitionProcessor
|
||
|
||
print("測試使用 MPS 初始化模型...")
|
||
processor = FaceRecognitionProcessor()
|
||
|
||
try:
|
||
processor.load_models(use_mps=True)
|
||
print("✅ MPS 模型加載成功")
|
||
|
||
# 測試推理
|
||
test_image = np.random.randint(0, 255, (640, 480, 3), dtype=np.uint8)
|
||
detections = processor.detect_faces(test_image)
|
||
print(f"✅ MPS 推理完成，檢測到 {len(detections)} 個人臉")
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"⚠️ MPS 初始化失敗，回退到 CPU: {e}")
|
||
print("嘗試使用 CPU...")
|
||
processor.load_models(use_mps=False)
|
||
print("✅ CPU 模型加載成功")
|
||
return True
|
||
|
||
else:
|
||
print("⚠️ CoreML (MPS) 不可用，使用 CPU")
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"❌ MPS 測試失敗: {e}")
|
||
return False
|
||
|
||
|
||
def main():
|
||
"""主測試函數"""
|
||
print("=" * 60)
|
||
print("端到端人臉識別測試")
|
||
print("=" * 60)
|
||
|
||
tests = [
|
||
("人臉註冊", test_face_registration),
|
||
("人臉識別", test_face_recognition),
|
||
("數據庫操作", test_database_operations),
|
||
("MPS 加速", test_mps_acceleration),
|
||
]
|
||
|
||
results = []
|
||
|
||
for test_name, test_func in tests:
|
||
try:
|
||
print(f"\n{'=' * 40}")
|
||
print(f"開始測試: {test_name}")
|
||
print(f"{'=' * 40}")
|
||
|
||
success = test_func()
|
||
results.append((test_name, success))
|
||
|
||
if success:
|
||
print(f"✅ {test_name} 測試通過")
|
||
else:
|
||
print(f"❌ {test_name} 測試失敗")
|
||
|
||
except Exception as e:
|
||
print(f"❌ {test_name} 測試異常: {e}")
|
||
import traceback
|
||
|
||
traceback.print_exc()
|
||
results.append((test_name, False))
|
||
|
||
print("\n" + "=" * 60)
|
||
print("端到端測試結果摘要")
|
||
print("=" * 60)
|
||
|
||
passed = 0
|
||
for test_name, success in results:
|
||
status = "✅ 通過" if success else "❌ 失敗"
|
||
print(f"{test_name}: {status}")
|
||
if success:
|
||
passed += 1
|
||
|
||
print(f"\n總計: {passed}/{len(results)} 個測試通過")
|
||
|
||
if passed == len(results):
|
||
print("\n🎉 所有端到端測試通過！人臉識別系統完全可用。")
|
||
print("\n下一步:")
|
||
print("1. 啟動 Momentry 服務器")
|
||
print("2. 使用 API 端點進行人臉註冊和識別")
|
||
print("3. 測試視頻處理功能")
|
||
return 0
|
||
else:
|
||
print(f"\n⚠️ 有 {len(results) - passed} 個測試失敗，請檢查問題。")
|
||
return 1
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|