如何使用opencv检测网格中的最大矩形？

Question

I have some images which are in form of a grid. I have a code that works to find the largest rectangle in the grid. However it works in some images and completely fails in doing so in others, I need help fine-tuning the code to work in all the cases. Ideally I'd like the contours exactly on the border.

The code:

import cv2
import numpy as np

img =  cv2.imread('3.jpg')
frame = cv2.resize(img,(1000,500))

hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_red = np.array([0, 10, 120])
upper_red = np.array([15, 255, 255])
mask = cv2.inRange (hsv, lower_red, upper_red)
cv2.imshow("a",mask)
cv2.waitKey(0)

contours, _ = cv2.findContours(mask.copy(),
                           cv2.RETR_TREE,
                           cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
    red_area = max(contours, key=cv2.contourArea)
    x, y, w, h = cv2.boundingRect(red_area)
    cv2.rectangle(frame,(x, y),(x+w, y+h),(0, 0, 255), 2)

cv2.imshow('frame', frame)
cv2.imshow('mask', mask)

cv2.waitKey(0)

Image in which it works correctly:

Image in which the code does not work:

Answer 1

your problem in finding the max contour after thresolding can be solved simply by identifying the max contour corners:

import cv2
import numpy as np

img =  cv2.imread('2.png')
frame = cv2.resize(img,(1000,500))

hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_red = np.array([0, 10, 120])
upper_red = np.array([15, 255, 255])
mask = cv2.inRange (hsv, lower_red, upper_red)


y0 = np.min(np.where(mask>0)[0])
x0 = np.min(np.where(mask>0)[1])

y1 = np.max(np.where(mask>0)[0])
x1 = np.max(np.where(mask>0)[1])

cv2.rectangle(frame,(x0, y0),(x1, y1),(0, 0, 255), 2)

cv2.imshow('frame', frame)
cv2.imshow('mask', mask)

cv2.waitKey(0)