python+opencv實現(xiàn)移動偵測（幀差法）

更新時間：2020年03月20日 17:25:14 作者：Chenyj0109

這篇文章主要為大家詳細介紹了python+opencv實現(xiàn)移動偵測，文中示例代碼介紹的非常詳細，具有一定的參考價值，感興趣的小伙伴們可以參考一下

本文實例為大家分享了python+opencv實現(xiàn)移動偵測的具體代碼，供大家參考，具體內(nèi)容如下

1.幀差法原理

移動偵測即是根據(jù)視頻每幀或者幾幀之間像素的差異，對差異值設(shè)置閾值，篩選大于閾值的像素點，做掩模圖即可選出視頻中存在變化的楨。幀差法較為簡單的視頻中物體移動偵測，幀差法分為：單幀差、兩楨差、和三楨差。隨著幀數(shù)的增加是防止檢測結(jié)果的重影。

2.算法思路

文章以截取視頻為例進行單幀差法移動偵測

3.python實現(xiàn)代碼

def threh(video,save_video,thres1,area_threh):
 cam = cv2.VideoCapture(video)#打開一個視頻
 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_length
 fourcc = 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 0
 outt=[]
 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)
  #跟著圖像變換背景
  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_lwpCV
  else:
   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) ) :  # 對于矩形區(qū)域，只顯示大于給定閾值的輪廓（去除微小的變化等噪點）
     continue
    (x, y, w, h) = cv2.boundingRect(c) # 該函數(shù)計算矩形的邊界框
    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+1
 out.release()
 cam.release()
 return outt,index,images```
##調(diào)取函數(shù)
outt=threh('new_video.mp4','test6.mp4',25,3000)

以上就是本文的全部內(nèi)容，希望對大家的學習有所幫助，也希望大家多多支持腳本之家。

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