305 lines
10 KiB
Python
305 lines
10 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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OpenCV后端性能测试脚本
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测试不同OpenCV后端（DirectShow vs MSMF）的性能差异
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"""
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import sys
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import os
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import time
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import logging
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import cv2
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# 添加项目路径
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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def test_opencv_backend(backend_name, backend_id, device_index=0, width=1280, height=720, fps=30):
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"""
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测试指定OpenCV后端的性能
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Args:
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backend_name: 后端名称
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backend_id: 后端ID
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device_index: 设备索引
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width: 宽度
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height: 高度
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fps: 帧率
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Returns:
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dict: 性能数据
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"""
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print(f"\n{'='*70}")
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print(f"测试 {backend_name} 后端 (ID: {backend_id})")
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print(f"分辨率: {width}x{height}, FPS: {fps}")
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print(f"{'='*70}")
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result = {
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'backend_name': backend_name,
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'backend_id': backend_id,
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'success': False,
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'init_time': -1,
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'config_time': -1,
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'first_frame_time': -1,
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'total_time': -1,
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'actual_resolution': 'N/A',
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'error': None
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}
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cap = None
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try:
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# 1. 测试相机初始化时间
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print(f"📷 初始化相机 (后端: {backend_name})...")
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init_start = time.time()
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# 创建VideoCapture对象并指定后端
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cap = cv2.VideoCapture(device_index, backend_id)
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if not cap.isOpened():
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print(f"❌ 无法打开相机 (后端: {backend_name})")
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result['error'] = f"无法打开相机 (后端: {backend_name})"
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return result
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init_time = (time.time() - init_start) * 1000
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result['init_time'] = init_time
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print(f"✅ 相机初始化成功: {init_time:.1f}ms")
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# 2. 测试配置时间
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print(f"⚙️ 配置相机参数...")
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config_start = time.time()
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# 设置分辨率和帧率
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
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cap.set(cv2.CAP_PROP_FPS, fps)
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# 设置缓冲区大小
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# 性能优化设置
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try:
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cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0.25) # 手动曝光
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cap.set(cv2.CAP_PROP_AUTO_WB, 0) # 禁用自动白平衡
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cap.set(cv2.CAP_PROP_EXPOSURE, -6) # 设置曝光值
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except Exception as e:
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print(f"⚠️ 性能优化设置警告: {e}")
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config_time = (time.time() - config_start) * 1000
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result['config_time'] = config_time
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print(f"✅ 配置完成: {config_time:.1f}ms")
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# 3. 获取实际分辨率
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actual_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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actual_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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actual_fps = cap.get(cv2.CAP_PROP_FPS)
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result['actual_resolution'] = f"{actual_width}x{actual_height}@{actual_fps:.1f}fps"
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print(f"🎯 实际参数: {actual_width}x{actual_height}, FPS: {actual_fps:.1f}")
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# 4. 测试首帧获取时间
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print(f"🖼️ 获取首帧...")
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frame_start = time.time()
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ret, frame = cap.read()
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if ret and frame is not None:
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first_frame_time = (time.time() - frame_start) * 1000
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result['first_frame_time'] = first_frame_time
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print(f"✅ 首帧获取成功: {first_frame_time:.1f}ms, 帧大小: {frame.shape}")
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else:
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print(f"❌ 首帧获取失败")
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result['error'] = "首帧获取失败"
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return result
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# 5. 计算总时间
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total_time = init_time + config_time + first_frame_time
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result['total_time'] = total_time
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result['success'] = True
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print(f"📊 总耗时: {total_time:.1f}ms ({total_time/1000:.2f}秒)")
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# 6. 测试连续帧获取性能
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print(f"🎬 测试连续帧获取性能...")
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frame_times = []
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test_frames = 10
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for i in range(test_frames):
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frame_start = time.time()
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ret, frame = cap.read()
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if ret:
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frame_time = (time.time() - frame_start) * 1000
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frame_times.append(frame_time)
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else:
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break
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if frame_times:
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avg_frame_time = sum(frame_times) / len(frame_times)
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max_frame_time = max(frame_times)
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min_frame_time = min(frame_times)
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print(f"📈 连续帧性能: 平均 {avg_frame_time:.1f}ms, 最快 {min_frame_time:.1f}ms, 最慢 {max_frame_time:.1f}ms")
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result['avg_frame_time'] = avg_frame_time
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result['max_frame_time'] = max_frame_time
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result['min_frame_time'] = min_frame_time
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return result
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except Exception as e:
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print(f"❌ 测试异常: {e}")
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result['error'] = str(e)
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return result
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finally:
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if cap:
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cap.release()
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print(f"🧹 相机资源已释放")
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def get_available_backends():
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"""
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获取可用的OpenCV后端
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Returns:
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list: 可用后端列表
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"""
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backends = [
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('DirectShow', cv2.CAP_DSHOW),
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('MSMF', cv2.CAP_MSMF),
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('V4L2', cv2.CAP_V4L2),
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('GStreamer', cv2.CAP_GSTREAMER),
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('Any', cv2.CAP_ANY)
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]
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available_backends = []
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for name, backend_id in backends:
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try:
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# 尝试创建VideoCapture对象
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cap = cv2.VideoCapture(0, backend_id)
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if cap.isOpened():
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available_backends.append((name, backend_id))
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cap.release()
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except Exception:
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pass
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return available_backends
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def main():
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"""
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主测试函数
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"""
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print("🚀 OpenCV后端性能测试")
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print(f"OpenCV版本: {cv2.__version__}")
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# 获取可用后端
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print("\n🔍 检测可用的OpenCV后端...")
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available_backends = get_available_backends()
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if not available_backends:
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print("❌ 未找到可用的相机后端")
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return
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print(f"✅ 找到 {len(available_backends)} 个可用后端:")
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for name, backend_id in available_backends:
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print(f" - {name} (ID: {backend_id})")
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# 测试参数
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test_params = {
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'device_index': 0,
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'width': 1280,
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'height': 720,
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'fps': 30
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}
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print(f"\n📋 测试参数: {test_params['width']}x{test_params['height']}@{test_params['fps']}fps")
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# 执行测试
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results = []
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for backend_name, backend_id in available_backends:
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result = test_opencv_backend(
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backend_name,
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backend_id,
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**test_params
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)
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results.append(result)
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# 等待一下，避免设备冲突
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time.sleep(2)
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# 输出汇总结果
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print(f"\n{'='*90}")
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print("📈 OpenCV后端性能测试汇总")
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print(f"{'='*90}")
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print(f"{'后端':<12} {'状态':<8} {'初始化':<10} {'配置':<10} {'首帧':<10} {'总计':<10} {'实际分辨率':<20}")
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print("-" * 90)
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successful_results = []
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for result in results:
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status = "✅成功" if result['success'] else "❌失败"
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init_time = f"{result['init_time']:.1f}ms" if result['init_time'] > 0 else "N/A"
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config_time = f"{result['config_time']:.1f}ms" if result['config_time'] > 0 else "N/A"
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frame_time = f"{result['first_frame_time']:.1f}ms" if result['first_frame_time'] > 0 else "N/A"
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total_time = f"{result['total_time']:.1f}ms" if result['total_time'] > 0 else "N/A"
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print(f"{result['backend_name']:<12} {status:<8} {init_time:<10} {config_time:<10} {frame_time:<10} {total_time:<10} {result['actual_resolution']:<20}")
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if result['success']:
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successful_results.append(result)
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# 性能分析
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if len(successful_results) >= 2:
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print(f"\n📊 性能分析:")
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# 找到最快和最慢的后端
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fastest = min(successful_results, key=lambda x: x['total_time'])
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slowest = max(successful_results, key=lambda x: x['total_time'])
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print(f"🏆 最快后端: {fastest['backend_name']} - {fastest['total_time']:.1f}ms")
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print(f"🐌 最慢后端: {slowest['backend_name']} - {slowest['total_time']:.1f}ms")
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if slowest['total_time'] > fastest['total_time']:
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improvement = ((slowest['total_time'] - fastest['total_time']) / slowest['total_time']) * 100
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print(f"💡 性能提升: {improvement:.1f}% (使用最快后端)")
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# 详细对比
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print(f"\n📋 详细性能对比:")
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for result in successful_results:
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if result != fastest:
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if result['total_time'] > fastest['total_time']:
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slower = ((result['total_time'] - fastest['total_time']) / fastest['total_time']) * 100
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print(f" {result['backend_name']}: 比最快后端慢 {slower:.1f}% ({result['total_time']:.1f}ms vs {fastest['total_time']:.1f}ms)")
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elif len(successful_results) == 1:
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result = successful_results[0]
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print(f"\n📊 只有一个后端测试成功: {result['backend_name']} - {result['total_time']:.1f}ms")
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# 推荐建议
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print(f"\n🎯 建议:")
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if successful_results:
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fastest = min(successful_results, key=lambda x: x['total_time'])
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print(f"✅ 推荐使用 {fastest['backend_name']} 后端以获得最佳性能")
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print(f"📝 配置建议: 在相机初始化时指定后端 cv2.VideoCapture(device_index, cv2.CAP_{fastest['backend_name'].upper()})")
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if fastest['total_time'] < 3000:
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print(f"🚀 性能评级: 优秀 (< 3秒)")
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elif fastest['total_time'] < 5000:
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print(f"⚡ 性能评级: 良好 (< 5秒)")
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else:
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print(f"⚠️ 性能评级: 需要优化 (> 5秒)")
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else:
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print(f"❌ 所有后端测试都失败了，请检查相机连接和驱动")
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print(f"\n{'='*90}")
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print("测试完成")
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print(f"{'='*90}")
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if __name__ == "__main__":
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main() |