如何从图像中识别最大的边界矩形并使用 Opencv 和 python 将它们分成单独的图像

Question

I am new to Opencv and python and trying to identify the largest three rectangles as marked in the sample image and extract them into three separate images. I am able to identify contours in the image but all of them are showing up (as shown in second image ) and I am not able to separate out the three largest ones. Code I have written so far:

import cv2

image = cv2.imread('imgpath')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 130, 255, 1)

cnts = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

#largest_contours = sorted(cnts, key=cv2.contourArea)[-3:]
#print(len(largest_contours))
for c in cnts:
    cv2.drawContours(image,[c], 0, (0,255,0), 3)

#cv2.imshow("result", image)
#cv2.drawContours(image, largest_contours, -1, (0,255,0), 3)
cv2.imshow('contours', image)
cv2.waitKey(0)

Answer 1

Sort the contours according to their area and then pick the top three.

    cnts = sorted(cnts, key=lambda c: cv2.contourArea(c), reverse=True)
    for c in cnts[:3]:
        cv2.drawContours(image,[c], 0, (0,255,0), 3)
        (x,y,w,h) = cv2.boundingRect(c)    

(x,y,w,h) represent co-ordinates (x,y), width and height of the contour. These values can be used to crop out the rectangle.