如何使用 OpenCV 获取数独网格的单元格? [英] How to get the cells of a sudoku grid with OpenCV?

问题描述

过去几天我一直在尝试从图片中获取数独网格，但我一直在努力获取较小的方格.我正在处理下面的图片.我认为用精明的过滤器处理图像会工作正常，但它没有，我无法获得每个正方形的每个轮廓.然后我将自适应阈值、otsu 和经典阈值化用于测试，但每次似乎都无法捕捉到每个小方块.

最终目标是获取包含数字的单元格，并使用 pytorch 识别数字，所以我真的很想拥有一些干净的数字图像，这样识别就不会出错:)

有人知道如何实现这一目标吗?非常感谢！:D

解决方案

这是一个潜在的解决方案:

1. 获取二进制图像. 将图像转换为 .

   I've been trying for the last few days to get a sudoku grid from a picture, and I have been struggling on getting the smaller squares of the grid. I am working on the picture below. I thought processing the image with a canny filter would work fine, but it didn't and I couldn't get every contour of each square. I then put adaptive threshold, otsu, and a classic thresholding to the test, but every time, it just could not seem to capture every small square.

   The final goal is to get the cells containing a number, and recognize the numbers with pytorch, so I would really like to have some clean images of the numbers, so the recognition doesn't screw up :)

   Would anyone have an idea on how to achieve this? Thanks a lot in advance! :D

   解决方案

   Here's a potential solution:

   1. Obtain binary image. Convert image to grayscale and adaptive threshold

   2. Filter out all numbers and noise to isolate only boxes. We filter using contour area to remove the numbers since we only want each individual cell

   3. Fix grid lines. Perform morphological closing with a horizontal and vertical kernel to repair grid lines.

   4. Sort each cell in top-to-bottom and left-to-right order. We organize each cell into a sequential order using imutils.contours.sort_contours() with the top-to-bottom and left-to-right parameter

   ---

   Here's the initial binary image (left) and filtered out numbers + repaired grid lines + inverted image (right)

   Here's a visualization of the iteration of each cell

   The detected numbers in each cell

   Code

   import cv2
   from imutils import contours
   import numpy as np
   
   # Load image, grayscale, and adaptive threshold
   image = cv2.imread('1.png')
   gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
   thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,57,5)
   
   # Filter out all numbers and noise to isolate only boxes
   cnts = cv2.findContours(thresh, cv2.RETR_TREE, 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:
           cv2.drawContours(thresh, [c], -1, (0,0,0), -1)
   
   # Fix horizontal and vertical lines
   vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,5))
   thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, vertical_kernel, iterations=9)
   horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,1))
   thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, horizontal_kernel, iterations=4)
   
   # Sort by top to bottom and each row by left to right
   invert = 255 - thresh
   cnts = cv2.findContours(invert, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   (cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")
   
   sudoku_rows = []
   row = []
   for (i, c) in enumerate(cnts, 1):
       area = cv2.contourArea(c)
       if area < 50000:
           row.append(c)
           if i % 9 == 0:  
               (cnts, _) = contours.sort_contours(row, method="left-to-right")
               sudoku_rows.append(cnts)
               row = []
   
   # Iterate through each box
   for row in sudoku_rows:
       for c in row:
           mask = np.zeros(image.shape, dtype=np.uint8)
           cv2.drawContours(mask, [c], -1, (255,255,255), -1)
           result = cv2.bitwise_and(image, mask)
           result[mask==0] = 255
           cv2.imshow('result', result)
           cv2.waitKey(175)
   
   cv2.imshow('thresh', thresh)
   cv2.imshow('invert', invert)
   cv2.waitKey()
   

   Note: The sorting idea was adapted from an old previous answer in Rubrik cube solver color extraction.

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