Detect rectangles in an image with Python

Question

I have a large collection of binary images like these ones:

On each image I need to detect the white rectangle. The rectangles have different dimensions and orientations and sometimes they are interrupted by a black line (see image 2).

I think the problem is easy to solve if one could remove the noisy background. Thus, I tried first using OpenCV's filter2D function:

import cv2

img = cv2.imread(file_name)
mean_filter_kernel = np.ones((5,5),np.float32)/(5*5)
filtered_image = cv2.filter2D(image,-1,mean_filter_kernel)

But this doesn't seem to have any effect, probably because I'm not dealing with a gray scale image.

Next I thought about detecting contours and filling all contours black which have a small size:

import cv2

img = cv2.imread(file_name)
blurred = cv2.GaussianBlur(img, (5, 5), 0)
canny = cv2.Canny(blurred, 100, 50)
contours, _ = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
    area = cv2.contourArea(cnt)
    if area > 1000:
        continue
    cv2.drawContours(img, [cnt], -1, 0, -1)

This helps a bit to remove the noise but it is far from perfect.

Does anybody have an idea how to improve my approach or is there a way to directly detect rectangles without removing the background noise?

clean = cv2.fastNlMeansDenoising(input,h=20) - opencv has denoising method. Check here : link — Hihikomori
– Hihikomori, Commented Dec 23, 2020 at 11:59
I had almost the same problem, so after denoisng I used dlib shape predictor, which I trained by myself on randomly generated dataset. Check here : link — Hihikomori
– Hihikomori, Commented Dec 23, 2020 at 12:04
I would try morphological opening to remove the speckles while maintaining the rectangles. the rectangles look suitably solid, apart from the hairline fracture in the second picture which you can fix with morph. closing. — Christoph Rackwitz
– Christoph Rackwitz, Commented Dec 23, 2020 at 22:05

Bilal · Accepted Answer · 2020-12-24 11:32:58Z

3

Applying multi filtering and edge detection and thresholding will give you an acceptable results, you can improve the results using morphology or some math to determine the corners and the angle of your rectangle.

import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread
from skimage.filters import median, gaussian, threshold_otsu, sobel
from skimage.morphology import binary_erosion

orig = imread('4.png',as_gray=True)
img = orig.copy()
img[img<1] = 0

gauss = gaussian(img, sigma=3)

SE = np.ones((7,7))
med = median(gauss, selem=SE)

edges = sobel(med)

thresh = threshold_otsu(edges)
binary = edges > thresh

SE2 = np.ones((3,3))
result = binary_erosion(binary, selem=SE2)

plt.subplot(121)
plt.imshow(orig, cmap='gray')
plt.axis('off')
plt.subplot(122)
plt.imshow(result, cmap='gray')
plt.axis('off')
plt.show()

answered Dec 24, 2020 at 11:32

Bilal

4,1256 gold badges33 silver badges70 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

fmw42 Over a year ago

If your canny edges are closed after erosion, you can get the contour and then get the rotated bounding box from cv2.minAreaRect(). That will give you the dimensions and orientation.

Collectives™ on Stack Overflow

Detect rectangles in an image with Python

1 Answer 1

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related