基于OpenCV监控老鼠蟑螂检测系统[完整源码＆部署教程]

1.视频演示：

[项目分享]Python基于OpenCV监控老鼠蟑螂检测系统[完整源码＆部署教程]_哔哩哔哩_bilibili

2.图片演示：

3.算法原理：

该博客提出的移动侦测即是根据视频每帧或者几帧之间像素的差异，对差异值设置阈值，筛选大于阈值的像素点，做掩模图即可选出视频中存在变化的桢。帧差法较为简单的视频中物体移动侦测，帧差法分为：单帧差、两桢差、和三桢差。随着帧数的增加是防止检测结果的重影。

差分法(Temporal Difference)

由于场景中的目标在运动，目标的影像在不同图像帧中的位置不同。该类算法对时间上连续的两帧或三帧图像进行差分运算，不同帧对应的像素点相减，判断灰度差的绝对值，当绝对值超过一定阈值时，即可判断为运动目标，从而实现目标的检测功能。

4.算法流程图：

5.代码实现：

def threh(video,save_video,thres1,area_threh):cam = cv2.VideoCapture(video)#打开一个视频input_fps = cam.get(cv2.CAP_PROP_FPS)ret_val, input_image = cam.read()index=[]images=[]images.append(input_image)video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT))input_image=cv2.resize(input_image,(512,512))ending_frame = video_lengthfourcc = cv2.VideoWriter_fourcc(*'XVID')out = cv2.VideoWriter(save_video,fourcc, input_fps, (512, 512))gray_lwpCV = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)background=gray_lwpCV# es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4))i = 0 # default is 0outt=[]while(cam.isOpened()) and ret_val == True and i <2999:## if i % 2==1:ret_val, input_image = cam.read()input_image=cv2.resize(input_image,(512,512))gray_lwpCV = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)diff = cv2.absdiff(background, gray_lwpCV)outt.append(diff)#跟着图像变换背景tem_diff=diff.flatten()tem_ds=pd.Series(tem_diff)tem_per=1-len(tem_ds[tem_ds==0])/len(tem_ds)if (tem_per <0.2 )| (tem_per>0.75):background=gray_lwpCVelse:diff = cv2.threshold(diff, thres1, 255, cv2.THRESH_BINARY)[1]ret,thresh = cv2.threshold(diff.copy(),150,255,0)contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)#  contours, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)for c in contours:if (cv2.contourArea(c) < area_threh) | (cv2.contourArea(c) >int(512*512*0.3) ) :  # 对于矩形区域，只显示大于给定阈值的轮廓（去除微小的变化等噪点）continue(x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框cv2.rectangle(input_image, (x, y), (x+w, y+h), (0, 255, 0), 2) index.append(i)#  cv2.imshow('contours', input_image)#  cv2.imshow('dis', diff)out.write(input_image)images.append(input_image)i = i+1out.release()cam.release()return outt,index,images```
##调取函数
outt=threh('new_video.mp4','test6.mp4',25,3000)