python实现运动探测

要实现运动探测，可以借助电脑摄像头、OpenCV 库和Python语言编写程序。具体步骤如下：

1. 导入OpenCV库和numpy库

import cv2
import numpy as np

2. 打开电脑摄像头

cap = cv2.VideoCapture(0)

3. 定义背景图片

background = None

4. 不断读取摄像头的每一帧图像，进行处理

while True:
    ret, frame = cap.read()
    if not ret:
        break

5. 将读取到的图像转换为灰度图

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

6. 对第一帧图像进行处理，得到背景图片

if background is None:
    background = gray
    continue

7. 计算当前帧图像与背景图片的差异

diff = cv2.absdiff(background, gray)

8. 对差异图像进行阈值处理，可以设置一个阈值来筛选出运动物体

thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]

9. 对阈值图像进行膨胀处理，可以去掉一些小的噪点

thresh = cv2.dilate(thresh, None, iterations=2)

10. 在差异图像中找到轮廓，利用轮廓信息来确定运动物体的位置

contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
    if cv2.contourArea(contour) < 1000:
        continue
    (x, y, w, h) = cv2.boundingRect(contour)
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

11. 显示处理后的图像

cv2.imshow("frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
    break

完整代码如下：

import cv2
import numpy as np

cap = cv2.VideoCapture(0)
background = None

while True:
    ret, frame = cap.read()

    if not ret:
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (21, 21), 0)

    if background is None:
        background = gray
        continue

    diff = cv2.absdiff(background, gray)
    thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
    thresh = cv2.dilate(thresh, None, iterations=2)

    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for contour in contours:
        if cv2.contourArea(contour) < 1000:
            continue
        (x, y, w, h) = cv2.boundingRect(contour)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow("frame", frame)
    key = cv2.waitKey(1) & 0xFF

    if key == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

这样就可以实现简单的运动探测了。