python车速检测代码

以下是一个简单的Python车速检测代码，使用OpenCV库：

import cv2
import numpy as np

# 读取视频文件
cap = cv2.VideoCapture('video.avi')

# 初始化变量
frame_count = 0
last_speed = 0

# 设置感兴趣区域
roi = [(580, 460), (700, 460), (1040, 720), (0, 720)]

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    
    # 根据ROI裁剪图像
    mask = np.zeros_like(frame)
    cv2.fillPoly(mask, [np.array(roi)], (255, 255, 255))
    cropped_frame = cv2.bitwise_and(frame, mask)
    
    # 转换为灰度图像
    gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
    
    # 应用高斯模糊
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    
    # 应用Canny边缘检测
    edges = cv2.Canny(blurred, 50, 150)
    
    # 应用霍夫变换检测直线
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, minLineLength=50, maxLineGap=10)

    # 计算车速
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            # 判断直线是否在ROI内部
            if y1 >= roi[0][1] and y2 >= roi[0][1]:
                # 计算直线长度
                length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
                # 计算车速（假设每个像素代表0.1米）
                speed = length / 100 * 25 * 60
                # 平滑车速值
                if frame_count == 0:
                    last_speed = speed
                else:
                    last_speed = 0.9 * last_speed + 0.1 * speed
                # 在图像上显示车速值
                cv2.putText(cropped_frame, "Speed: {:.2f} km/h".format(last_speed), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    
    # 显示视频帧
    cv2.imshow('frame', cropped_frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    
    frame_count += 1

# 释放资源
cap.release()
cv2.destroyAllWindows()

这个代码使用了Canny边缘检测和霍夫变换检测直线，然后根据直线长度计算车速。你可以根据自己的需求进行修改。