备份一些py文件
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backup/cuda_t2.py
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200
backup/cuda_t2.py
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# final_gpu_test.py
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import torch
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import onnxruntime as ort
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import insightface
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import cv2
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import numpy as np
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import time
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import os
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from typing import List, Dict
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def comprehensive_gpu_test():
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"""全面的GPU测试"""
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print("=" * 60)
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print("最终GPU加速测试")
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print("=" * 60)
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# 1. 环境验证
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print("1. 环境验证:")
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print(f"✅ PyTorch: {torch.__version__}")
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print(f"✅ PyTorch CUDA: {torch.cuda.is_available()}")
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print(f"✅ GPU设备: {torch.cuda.get_device_name(0)}")
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print(f"✅ ONNX Runtime GPU支持: {'CUDAExecutionProvider' in ort.get_available_providers()}")
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print(f"✅ OpenCV: {cv2.__version__}")
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print(f"✅ InsightFace: {insightface.__version__}")
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# 2. 创建测试数据
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print("\n2. 创建测试数据...")
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os.makedirs("test_data", exist_ok=True)
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# 创建目标人脸
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target_face = np.random.randint(0, 255, (400, 400, 3), dtype=np.uint8)
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cv2.rectangle(target_face, (150, 150), (250, 250), (255, 255, 255), -1) # 简单的人脸模拟
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cv2.imwrite("test_data/target_face.jpg", target_face)
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# 创建测试图像（多个人脸）
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test_image = np.random.randint(0, 255, (800, 600, 3), dtype=np.uint8)
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# 添加多个人脸区域
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cv2.rectangle(test_image, (100, 100), (200, 200), (255, 255, 255), -1)
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cv2.rectangle(test_image, (300, 150), (400, 250), (255, 255, 255), -1)
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cv2.rectangle(test_image, (500, 200), (600, 300), (255, 255, 255), -1)
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cv2.imwrite("test_data/multi_face_test.jpg", test_image)
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print("✅ 测试数据创建完成")
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# 3. GPU加速的人脸识别系统
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print("\n3. 初始化GPU人脸识别系统...")
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class FastGPUFaceRecognition:
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def __init__(self):
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self.app = insightface.app.FaceAnalysis(name='buffalo_l')
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self.app.prepare(
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ctx_id=0, # GPU 0
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det_thresh=0.3, # 检测阈值
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det_size=(640, 640)
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)
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self.target_embedding = None
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print("✅ GPU人脸识别系统初始化完成")
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def set_target_face(self, image_path: str):
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"""设置目标人脸"""
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img = cv2.imread(image_path)
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faces = self.app.get(img)
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if faces:
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self.target_embedding = faces[0].embedding
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print(f"✅ 目标人脸特征提取完成")
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return True
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return False
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def process_image(self, image_path: str, output_path: str):
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"""处理图像并返回结果"""
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start_time = time.time()
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img = cv2.imread(image_path)
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if img is None:
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return None
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# GPU推理
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faces = self.app.get(img)
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results = []
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for i, face in enumerate(faces):
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similarity = 0.0
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if self.target_embedding is not None:
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# 计算相似度
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emb1 = face.embedding / np.linalg.norm(face.embedding)
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emb2 = self.target_embedding / np.linalg.norm(self.target_embedding)
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similarity = float(np.dot(emb1, emb2))
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results.append({
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'face_index': i,
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'bbox': face.bbox.astype(int).tolist(),
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'similarity': similarity,
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'det_score': face.det_score
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})
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# 绘制结果
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bbox = face.bbox.astype(int)
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color = (0, 255, 0) if similarity > 0.6 else (0, 0, 255)
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cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
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cv2.putText(img, f"{similarity:.3f}", (bbox[0], bbox[1] - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
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# 保存结果
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cv2.imwrite(output_path, img)
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processing_time = (time.time() - start_time) * 1000
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return {
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'faces_detected': len(faces),
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'processing_time_ms': processing_time,
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'results': results
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}
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# 初始化系统
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system = FastGPUFaceRecognition()
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# 4. 性能测试
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print("\n4. 性能测试:")
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# 设置目标人脸
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if system.set_target_face("test_data/target_face.jpg"):
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# 处理测试图像
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result = system.process_image("test_data/multi_face_test.jpg", "test_data/gpu_result.jpg")
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if result:
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print(f"✅ 处理完成:")
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print(f" 检测到人脸数: {result['faces_detected']}")
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print(f" 处理时间: {result['processing_time_ms']:.1f}ms")
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for face in result['results']:
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status = "匹配" if face['similarity'] > 0.6 else "不匹配"
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print(f" 人脸 {face['face_index'] + 1}: 相似度 {face['similarity']:.3f} ({status})")
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# 5. 批量性能测试
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print("\n5. 批量性能测试:")
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# 创建多个测试图像
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test_images = []
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for i in range(5):
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test_img = np.random.randint(0, 255, (600, 800, 3), dtype=np.uint8)
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cv2.rectangle(test_img, (100, 100), (200, 200), (255, 255, 255), -1)
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img_path = f"test_data/batch_test_{i}.jpg"
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cv2.imwrite(img_path, test_img)
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test_images.append(img_path)
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total_time = 0
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total_faces = 0
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for i, img_path in enumerate(test_images):
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result = system.process_image(img_path, f"test_data/batch_result_{i}.jpg")
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if result:
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total_time += result['processing_time_ms']
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total_faces += result['faces_detected']
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print(f" 图像 {i + 1}: {result['processing_time_ms']:.1f}ms, {result['faces_detected']}张人脸")
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if len(test_images) > 0:
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avg_time = total_time / len(test_images)
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print(f" 平均处理时间: {avg_time:.1f}ms/张")
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print(f" 总检测人脸: {total_faces}张")
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# 6. 与CPU性能对比（可选）
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print("\n6. GPU vs CPU 性能对比:")
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try:
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# GPU测试
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gpu_times = []
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test_img = cv2.imread("test_data/multi_face_test.jpg")
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for _ in range(10):
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start = time.time()
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faces = system.app.get(test_img)
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gpu_times.append((time.time() - start) * 1000)
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# CPU测试（创建新的CPU实例）
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cpu_app = insightface.app.FaceAnalysis(name='buffalo_l')
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cpu_app.prepare(ctx_id=-1) # CPU
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cpu_times = []
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for _ in range(10):
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start = time.time()
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faces = cpu_app.get(test_img)
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cpu_times.append((time.time() - start) * 1000)
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avg_gpu = np.mean(gpu_times)
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avg_cpu = np.mean(cpu_times)
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print(f" GPU平均推理时间: {avg_gpu:.1f}ms")
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print(f" CPU平均推理时间: {avg_cpu:.1f}ms")
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print(f" GPU加速比: {avg_cpu / avg_gpu:.1f}x")
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except Exception as e:
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print(f" 性能对比测试跳过: {e}")
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print("\n" + "=" * 60)
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print("🎉 GPU加速测试完成！")
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print("✅ 现在您的人脸识别系统正在使用GPU加速")
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print("📁 结果图像保存在 test_data/ 目录中")
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print("=" * 60)
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if __name__ == "__main__":
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comprehensive_gpu_test()
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