In the image below, I am using opencv harris corner detector to detect only the corners for the squares (and the smaller squares within the outer squares). However, I am also getting corners detected for the numbers on the side of the image. How do I get this to focus only on the squares and not the numbers? The code, input image and output image are below:

import cv2 as cv
img = cv.imread(filename)
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv.cornerHarris(gray, 2, 3, 0.04)
dst = cv.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]
cv.imshow('dst', img)

Input image

Output from Harris corner detector

CodePudding user response：

Here's a potential approach using traditional image processing:

1. Obtain binary image. We

   Detected horizontal lines -> horizontal mask

   

   Detected vertical lines -> vertical mask

   

   Bitwise-and both masks -> detected intersection points -> corners -> cleaned up corners

   

   The results aren't perfect but it's pretty close. The problem comes from the noise on the vertical mask due to the slanted image. If the image was centered without an angle, the results would be ideal. You can probably fine tune the kernel sizes or iterations to get better results.

   Code

   import cv2
   import numpy as np
   
   # Load image, create horizontal/vertical masks, Gaussian blur, Adaptive threshold
   image = cv2.imread('1.png')
   original = image.copy()
   horizontal_mask = np.zeros(image.shape, dtype=np.uint8)
   vertical_mask = np.zeros(image.shape, dtype=np.uint8)
   gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
   blur = cv2.GaussianBlur(gray, (3,3), 0)
   thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 23, 7)
   
   # Remove small noise on thresholded image
   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:
       area = cv2.contourArea(c)
       if area < 150:
           cv2.drawContours(thresh, [c], -1, 0, -1)
   
   # Detect horizontal lines
   dilate_horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,1))
   dilate_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, dilate_horizontal_kernel, iterations=1)
   horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
   detected_lines = cv2.morphologyEx(dilate_horizontal, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)
   cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   for c in cnts:
       cv2.drawContours(image, [c], -1, (36,255,12), 2)
       cv2.drawContours(horizontal_mask, [c], -1, (255,255,255), 2)
   
   # Remove extra horizontal lines using contour area filtering
   horizontal_mask = cv2.cvtColor(horizontal_mask,cv2.COLOR_BGR2GRAY)
   cnts = cv2.findContours(horizontal_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   for c in cnts:
       area = cv2.contourArea(c)
       if area > 1000 or area < 100:
           cv2.drawContours(horizontal_mask, [c], -1, 0, -1)
   
   # Detect vertical 
   dilate_vertical_kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (1,7))
   dilate_vertical = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, dilate_vertical_kernel, iterations=1)
   vertical_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,2))
   detected_lines = cv2.morphologyEx(dilate_vertical, cv2.MORPH_OPEN, vertical_kernel, iterations=4)
   cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   for c in cnts:
       cv2.drawContours(image, [c], -1, (36,255,12), 2)
       cv2.drawContours(vertical_mask, [c], -1, (255,255,255), 2)
   
   # Find intersection points
   vertical_mask = cv2.cvtColor(vertical_mask,cv2.COLOR_BGR2GRAY)
   combined = cv2.bitwise_and(horizontal_mask, vertical_mask)
   kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
   combined = cv2.morphologyEx(combined, cv2.MORPH_OPEN, kernel, iterations=1)
   
   # Highlight corners
   cnts = cv2.findContours(combined, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   for c in cnts:
       # Find centroid and draw center point
       try:
           M = cv2.moments(c)
           cx = int(M['m10']/M['m00'])
           cy = int(M['m01']/M['m00'])
           cv2.circle(original, (cx, cy), 3, (36,255,12), -1)
       except ZeroDivisionError:
           pass
   
   cv2.imshow('thresh', thresh)
   cv2.imshow('horizontal_mask', horizontal_mask)
   cv2.imshow('vertical_mask', vertical_mask)
   cv2.imshow('combined', combined)
   cv2.imshow('original', original)
   cv2.imshow('image', image)
   cv2.waitKey()