Opencv Python: How to detect filled, rectangular shapes on picture

Here's a simple approach using thresholding + morphological operations.

1. Obtain binary image. Load image, convert to grayscale, then adaptive threshold

2. Fill rectangular contours. Find contours and fill the contours to create filled rectangular blocks.

3. Perform morph open. We create a rectangular structuring element and morph open to remove the lines

4. Draw rectangles around largest rectangles Find contours and draw bounding rectangles around rectangles with an area above a certain treshold.

Here's each step visualized:

Obtain binary image

Adaptive treshold

Filled rectangular contours

Perform morph open

Draw rectangles around largest rectangles

In code:

import numpy as np 
import cv2

#load the image
image = cv2.imread("mtF6y.jpg")

# grayscale
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

# adaptive threshold
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)

# Fill rectangular contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(thresh, [c], -1, (255,255,255), -1)

# Morph open
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=4)

# Draw rectangles, the 'area_treshold' value was determined empirically
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
area_treshold = 4000
for c in cnts:
    if cv2.contourArea(c) > area_treshold :
      x,y,w,h = cv2.boundingRect(c)
      cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 3)

cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image)
cv2.waitKey()