使用 OpenCV 进行视频中的行人检测

在计算机视觉领域，行人检测是一个重要的研究方向，它在视频监控、自动驾驶、人机交互等领域都有着广泛的应用。本文将介绍如何使用 OpenCV 库来实现视频中的行人检测。

环境准备

首先，我们需要安装 OpenCV 库。可以通过以下命令来安装：

pip install opencv-python

代码实现

以下是实现视频中行人检测的代码：

import cv2
import os
import numpy as np
import timedef detect_people():current_dir = os.path.dirname(os.path.abspath(__file__))video_path = os.path.join(current_dir, 'walk2.mp4')if not os.path.exists(video_path):print(f"Error: 视频文件不存在，请确认文件路径: {video_path}")return# 创建HOG检测器hog = cv2.HOGDescriptor()hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())cap = cv2.VideoCapture(video_path)if not cap.isOpened():print("Error: 无法打开视频文件")return# 设置视频捕获的分辨率cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)# 用于控制检测频率frame_count = 0detection_interval = 3  # 每隔3帧进行一次检测last_boxes = []while True:start_time = time.time()ret, frame = cap.read()if not ret:break# 降低分辨率frame = cv2.resize(frame, (640, 480))# 每隔几帧进行一次检测if frame_count % detection_interval == 0:# 检测人boxes, weights = hog.detectMultiScale(frame, winStride=(8, 8),padding=(4, 4),scale=1.1)last_boxes = boxeselse:boxes = last_boxes# 在图像上绘制边界框for (x, y, w, h) in boxes:cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)# 显示人数people_count = len(boxes)cv2.putText(frame, f'People Count: {people_count}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)# 计算和显示FPSfps = 1.0 / (time.time() - start_time)cv2.putText(frame, f'FPS: {int(fps)}', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)# 显示结果cv2.imshow('People Detection', frame)frame_count += 1# 减小等待时间，提高帧率if cv2.waitKey(1) & 0xFF == ord('q'):breakcap.release()cv2.destroyAllWindows()if __name__ == '__main__':detect_people()