python+opencv實現移動偵測(幀差法)
阿新 • • 發佈:2020-03-23
本文例項為大家分享了python+opencv實現移動偵測的具體程式碼,供大家參考,具體內容如下
1.幀差法原理
移動偵測即是根據視訊每幀或者幾幀之間畫素的差異,對差異值設定閾值,篩選大於閾值的畫素點,做掩模圖即可選出視訊中存在變化的楨。幀差法較為簡單的視訊中物體移動偵測,幀差法分為:單幀差、兩楨差、和三楨差。隨著幀數的增加是防止檢測結果的重影。
2.演算法思路
文章以擷取視訊為例進行單幀差法移動偵測
3.python實現程式碼
def threh(video,save_video,thres1,area_threh): cam = cv2.VideoCapture(video)#開啟一個視訊 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)) 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)) gray_lwpCV = cv2.cvtColor(input_image,cv2.COLOR_BGR2GRAY) gray_lwpCV = cv2.GaussianBlur(gray_lwpCV,0) diff = cv2.absdiff(background,gray_lwpCV) outt.append(diff) #跟著影象變換背景 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,255,cv2.THRESH_BINARY)[1] ret,thresh = cv2.threshold(diff.copy(),150,0) contours,hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # contours,hierarchy = cv2.findContours(diff.copy(),cv2.CHAIN_APPROX_SIMPLE) for c in contours: if (cv2.contourArea(c) < area_threh) | (cv2.contourArea(c) >int(512*512*0.3) ) : # 對於矩形區域,只顯示大於給定閾值的輪廓(去除微小的變化等噪點) continue (x,y,w,h) = cv2.boundingRect(c) # 該函式計算矩形的邊界框 cv2.rectangle(input_image,(x,y),(x+w,y+h),(0,0),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``` ##調取函式 outt=threh('new_video.mp4','test6.mp4',25,3000)
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