opencv python 三帧差法

三帧差法是一种基于连续帧之间像素差异的运动检测算法，可以用于视频监控、行人检测等应用场景。在OpenCV中，可以通过以下步骤实现三帧差法：

1. 读取视频帧序列，并将其转化为灰度图像。

import cv2

cap = cv2.VideoCapture('video.mp4')

ret, frame1 = cap.read()
gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)

ret, frame2 = cap.read()
gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)

while True:
    ret, frame3 = cap.read()
    gray3 = cv2.cvtColor(frame3, cv2.COLOR_BGR2GRAY)

2. 计算当前帧与前两帧之间的差值，并进行二值化处理。

    d1 = cv2.absdiff(gray2, gray1)
    d2 = cv2.absdiff(gray3, gray2)
    diff = cv2.bitwise_and(d1, d2)
    
    _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY)

3. 对二值化后的图像进行形态学操作，去除噪声。

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=2)

4. 绘制运动物体的轮廓，并标记出运动方向。

    contours, _ = cv2.findContours(closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    for contour in contours:
        if cv2.contourArea(contour) < 500:
            continue
        
        (x, y, w, h) = cv2.boundingRect(contour)
        cv2.rectangle(frame3, (x, y), (x + w, y + h), (0, 255, 0), 2)
        
        cx, cy = x + w // 2, y + h // 2
        cv2.arrowedLine(frame3, (cx, cy), (cx + 50, cy), (0, 0, 255), 2)

完整代码如下：

import cv2

cap = cv2.VideoCapture('video.mp4')

ret, frame1 = cap.read()
gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)

ret, frame2 = cap.read()
gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)

while True:
    ret, frame3 = cap.read()
    gray3 = cv2.cvtColor(frame3, cv2.COLOR_BGR2GRAY)
    
    d1 = cv2.absdiff(gray2, gray1)
    d2 = cv2.absdiff(gray3, gray2)
    diff = cv2.bitwise_and(d1, d2)
    
    _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY)
    
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=2)
    
    contours, _ = cv2.findContours(closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    for contour in contours:
        if cv2.contourArea(contour) < 500:
            continue
        
        (x, y, w, h) = cv2.boundingRect(contour)
        cv2.rectangle(frame3, (x, y), (x + w, y + h), (0, 255, 0), 2)
        
        cx, cy = x + w // 2, y + h // 2
        cv2.arrowedLine(frame3, (cx, cy), (cx + 50, cy), (0, 0, 255), 2)
        
    cv2.imshow('frame', frame3)
    
    gray1 = gray2
    gray2 = gray3
    
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()