cv2.GaussianBlur多种内核大小

Question

目前，我正在尝试使用OpenCV执行运动检测。对于每个新的帧，我使用下面的函数来与以前的帧进行比较：

    def detect(new_frame, kernel_size):
        frame=cv2.cvtColor(new_frame,cv2.COLOR_BGR2GRAY) #Grayscale conversion of the frame
        frame=cv2.GaussianBlur(frame, (kernel_size, kernel_size),0) 
        
        deltaFrame=cv2.absdiff(old_frame, frame)    
        old_frame = frame

        threshFrame=cv2.threshold(deltaFrame, 5, 255, cv2.THRESH_BINARY)[1]
        threshFrame=cv2.dilate(threshFrame, None, iterations=2)
            
        (cnts,_)=cv2.findContours(threshFrame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        return cnts

我的问题是，我必须检测两种类型的对象的运动，每种类型的函数都有自己的核大小参数的有效值(即:5和11)。所以我必须对每个新帧使用该函数2次。但是我的设备有资源限制，所以我想尽可能减少这个过程。我该怎么做呢？

Answer 1

在掩码上尝试按位函数。检测每个像素是否在移动。它很快。

对我来说，诀窍是处理小尺寸的frame图像。

import numpy as np
import cv2 as cv2

fid=0

video_path="videos/example.mp4"
cap = cv2.VideoCapture(video_path)

# Some characteristics from the original video
w_frame, h_frame = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps, num_frames = cap.get(cv2.CAP_PROP_FPS), cap.get(cv2.CAP_PROP_FRAME_COUNT)
print(fps,w_frame,h_frame)
x,y,h,w = 0,0,h_frame,w_frame

fnum=0
while(True):
    
    ret, frame = cap.read()

    if ret == None: pass 

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


    if  fnum==0:
        last_edges = edges.copy() 
        
    ret, mask1 = cv2.threshold(edges, 127, 255, cv2.THRESH_BINARY)
    ret, mask2 = cv2.threshold(last_edges, 127 , 255, cv2.THRESH_BINARY)

    dst1 = cv2.bitwise_and(mask2,mask1)
    dst2 = cv2.bitwise_not(dst1)
    dst4 = cv2.bitwise_and(dst2,dst2,mask=mask1)

    scale_percent = 10 # percent of original size
    width = int(dst4.shape[1] * scale_percent / 100)
    height = int(dst4.shape[0] * scale_percent / 100)
    dim = (width, height)
    
    # resize image
    mini = cv2.resize(dst4, dim, interpolation = cv2.INTER_AREA)

    h,w = mini.shape

    th=30 #my threshold

    points=[]

    for y in range(0, len(mini),4):
        for x in range(0,len(mini[y]),4):
            c1 = mini[y][x] > th and mini[y][x+1] > th and mini[y][x+2] > th and mini[y][x+3] > th  
            c2 = mini[y][x] > th and mini[y+1][x] > th and mini[y+2][x] > th and mini[y+3][x] > th
            if c1 or c2:
                
                start_point=(x*scale_percent,y*scale_percent)

                points.append(start_point)

                color1=(0,0,255)
                color2=(0,255,255)
                thickness=2
                cv2.circle(frame, start_point, 20, color1, thickness) 
    
    if len(points) >= 2:
        cx1 , cy1 = points[0][0] , points[0][1]
        cx2 , cy2 = points[-1][0] , points[-1][1]

        cxmin = min(cx1,cx2)
        cymin = min(cy1,cy2)

        cxmax = max(cx1,cx2)
        cymax = max(cy1,cy2)

        print(cymin,cymax , '--' , cxmin,cxmax)

        cv2.rectangle(frame, (cxmin,cymin) , (cxmax,cymax), color2, thickness)


    
    # Display the resulting frame 
    cv2.imshow('frame4', frame)
    cv2.imshow('framemin', mini)

    last_edges = edges.copy() 
    fnum+=1

    if cv2.waitKey(33) & 0xFF == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

您可以应用您自己的遮罩来检测一个或另一个玩模糊值的对象。