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 co

Answer 1

Here's an approach

• Convert image to grayscale and median blur to smooth image
• Sharpen image to enhance edges
• Threshold
• Perform morphological transformations
• Find contours and filter using minimum/maximum threshold area
• Crop and save ROI

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Sharpen image with cv2.filter2D(). We use a generic sharpen kernel, other kernels can be found here

Now threshold to get a binary image

Perform morphological operations

From here we find contours and filter using cv2.contourArea() with minimum/maximum threshold areas.

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+h]
cv2.imwrite('ROI_{}.png'.format(image_number), ROI)

import cv2
import numpy as np

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)

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)

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+h]
        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()