opencv: Creating a binary mask from the image-CodePudding

I have this picture and I want to make a binary mask from it. The main (the biggest) rectangle should be white and other parts of the picture shound be black. Like this:

To solve this problem I want to find a contour of main rectangle. My plan is to find all external contours. Then get the contour with the highest area. Firstly, I tried to find all contours and plot them as a binary image. My attempt:

import numpy as np 
import cv2
import matplotlib.pyplot as plt
from skimage import data, io

im = cv2.imread('train_1.jpg', 0)
contours, hierarchy = cv2.findContours(im.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
out = np.zeros_like(im)
cv2.drawContours(out, contours, -1, 255, 10)
io.imshow(out)
plt.show()

But even this simple code didn't find any contours. What's wrong with it? And how to get the contour with the highest area? Maybe I should apply Canny algorithm here? Please, help me.

CodePudding user response：

You should the logic of taking the max area of rectangles as shown below. You can use this snippet to get the mask image of the area:

import cv2
import numpy as np

image = cv2.imread("jigsaw.jpg")

image = cv2.resize(image, (image.shape[1] // 4, image.shape[0] // 4))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)

thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

max_area = 0
best_cnt = None
for counter in contours:
    area = cv2.contourArea(counter)
    if area > 1000:
        if area > max_area:
            max_area = area
            best_cnt = counter

mask = np.zeros((gray.shape), np.uint8)

cv2.drawContours(mask, [best_cnt], 0, 255, -1)
cv2.drawContours(mask, [best_cnt], 0, 0, 2)

cv2.imwrite('mask_jigsaw.jpg', mask)

cv2.imshow("Image Mask", mask)
cv2.waitKey(0)

Input image:

Output image: