用于摄像机馈送的图像边界检测opencv python

Question

我正在使用python在opencv中基于图像处理开发我的项目。

我有一个带有蓝色矩形边框的页面，其内部由相机捕获。我需要检测边界以提取边界内的内容以供进一步处理。我尝试过轮廓，边界Rect以及Hough圆形绘制矩形角落有4个圆圈。

我使用hsv转换来检测蓝色。

我未能准确地获得边界。这是我的代码。 `

import cv2
import numpy as np

def nothing(x):
    pass

img=np.zeros((640,480,3),np.uint8)
cv2.namedWindow('image',cv2.WINDOW_AUTOSIZE)

cv2.createTrackbar('kernel','image',1,10,nothing)
cv2.createTrackbar('iteration','image',1,10,nothing)


cap = cv2.VideoCapture(0)
while(1):
    ret, frame = cap.read()
    frame=cv2.GaussianBlur(frame,(5,5),0)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    ker     = cv2.getTrackbarPos('kernel','image')
    its     = cv2.getTrackbarPos('iteration','image')

    lower_array=np.array([75,100,100])
    upper_array=np.array([110,255,255])

### Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_array, upper_array)
    edges = cv2.Canny(mask,90,200)

## Image Processing to obtain better image    
    kernel = np.ones((ker,ker),np.uint8)

    erosion = cv2.erode(edges,kernel,iterations = its)
    dilation = cv2.dilate(erosion,kernel,iterations = its)
    opening = cv2.morphologyEx(dilation, cv2.MORPH_OPEN, kernel)
    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)

##    ret,thresh = cv2.threshold(closing,127,255,0)
    image, contours, hierarchy = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    contours_poly=[]
    bounding_rect=[]
    hull=[]

    res = cv2.bitwise_and(frame,frame, mask= closing)

##  Find the largest contour
    max_area = 0
    if(contours):
        for cnt in contours:
            area = cv2.contourArea(cnt)
            if area > max_area:
                    max_area = area
                    best_cnt = cnt

    ## Finding centroids of best_cnt and draw a circle there
        M = cv2.moments(best_cnt)
        cx,cy = int(M['m10']/M['m00']), int(M['m01']/M['m00'])
        cv2.circle(frame,(cx,cy),5,(0,255,0),-1)

    ##To approximate the contour
        epsilon = 0.01*cv2.arcLength(best_cnt,True)
        approx = cv2.approxPolyDP(best_cnt,epsilon,True)


    ## Draw the largest contour
    cv2.drawContours(res, [best_cnt], 0, (0,255,0), 3)

        #x,y,w,h = cv2.boundingRect(best_cnt)
        #cv2.rectangle(res,(x,y),(x+w,y+h),(0,255,0),2)

    cv2.imshow('image1',frame)
    cv2.imshow('image2',mask)
    cv2.imshow('image3',dilation)
    cv2.imshow('image6',edges)
    cv2.imshow('image4',closing)
    cv2.imshow('image5',res)


    if cv2.waitKey(1) & 0xFF==ord('q'):
        break
cap.release()
cv2.destroyAllWindows()
`