Square detection in image

Question

I am trying to detect all the squared shaped dice images so that i can crop them individually and use that for OCR. Below is the Original image:

Here is the code i have got but it is missing some squares.

def find_squares(img):
    img = cv2.GaussianBlur(img, (5, 5), 0)
    squares = []
    for gray in cv2.split(img):
        for thrs in range(0, 255, 26):
            if thrs == 0:
                bin = cv2.Canny(gray, 0, 50, apertureSize=5)
                bin = cv2.dilate(bin, None)
            else:
                _retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
            bin, contours, _hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
            for cnt in contours:
                cnt_len = cv2.arcLength(cnt, True)
                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
                if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
                    cnt = cnt.reshape(-1, 2)
                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in range(4)])
                    #print(cnt)
                    a = (cnt[1][1] - cnt[0][1])

                    if max_cos < 0.1 and a < img.shape[0]*0.8:

                        squares.append(cnt)
    return squares

dice = cv2.imread('img1.png')
squares = find_squares(dice)
cv2.drawContours(dice, squares, -1, (0, 255, 0), 3)

Here are the Output images:

As per my analysis, some squares are missing due to missing canny edges along the dice because of smooth intensity transition between dice and background.

Given the constraint that there will always be 25 dices in square grid pattern (5*5) can we predict the missing square positions based on recognised squares? Or can we modify above algorithm for square detection algorithm?

Answer 1

1. Sharpen square edges. Load the image, convert to grayscale, median blur to smooth, and sharpen to enhance edges.

2. Obtain binary image and remove noise. We threshold to obtain a black/white binary image. Depending on the image, Otsu's thresholding or adaptive thresholding would work. From here we create a rectangular kernel and perform morphological transformations to remove noise and enhance the square contours.

3. Detect and extract squares. Next we find contours and filter using minimum/maximum threshold area. Any contours that pass our filter will be our squares so to extract each ROI, we obtain the bounding rectangle coordinates, crop using Numpy slicing, and save each square image.

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Sharpen image with cv2.filter2D() using a generic sharpening kernel, other kernels can be found here.

Now threshold to get a binary image

There's little particles of noise so to remove them, we perform morphological operations

Next find contours and filter using cv2.contourArea() with minimum/maximum threshold values.

We can crop each desired square region using Numpy slicing and save each ROI like this

x,y,w,h = cv2.boundingRect(c)
ROI = image[y:y+h, x:x+w]
cv2.imwrite('ROI_{}.png'.format(image_number), ROI)

import cv2
import numpy as np

# Load image, grayscale, median blur, sharpen image
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 5)
sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen = cv2.filter2D(blur, -1, sharpen_kernel)

# Threshold and morph close
thresh = cv2.threshold(sharpen, 160, 255, cv2.THRESH_BINARY_INV)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)

# Find contours and filter using threshold area
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

min_area = 100
max_area = 1500
image_number = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area > min_area and area < max_area:
        x,y,w,h = cv2.boundingRect(c)
        ROI = image[y:y+h, x:x+w]
        cv2.imwrite('ROI_{}.png'.format(image_number), ROI)
        cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
        image_number += 1

cv2.imshow('sharpen', sharpen)
cv2.imshow('close', close)
cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

Answer 2

That extra piece of information is absolutely golden. Yes, given the 5x5 matrix of dice, you can nail the positions quite well. The dice you can identify give you the center, size, and orientation of the dice. Simply continue those patterns along both axes. For your second pass, increase the contrast in each "region of interest" where you expect to find the edge of a douse (never say die!). You know within a few pixels where the edges will be: simply attenuate the image until you identify those edges.