如何在OpenCV中检测Sudoku网格板

Question

我正在使用opencvpython 进行个人项目。要检测一个数独网格。

原始图像是：

到目前为止，我已经创建了这个：

然后尝试选择一个大斑点。结果可能与此类似：

结果是黑色图像：

代码是：

import cv2
import numpy as np

def find_biggest_blob(outerBox):
   max = -1
   maxPt = (0, 0)

   h, w = outerBox.shape[:2]
   mask = np.zeros((h + 2, w + 2), np.uint8)

   for y in range(0, h):
     for x in range(0, w):

       if outerBox[y, x] >= 128:

         area = cv2.floodFill(outerBox, mask, (x, y), (0, 0, 64))


   #cv2.floodFill(outerBox, mask, maxPt, (255, 255, 255))

   image_path = 'Images/Results/sudoku-find-biggest-blob.jpg'

   cv2.imwrite(image_path, outerBox)

   cv2.imshow(image_path, outerBox)


 def main():
   image = cv2.imread('Images/Test/sudoku-grid-detection.jpg', 0)

   find_biggest_blob(image)

   cv2.waitKey(0)

   cv2.destroyAllWindows() 


if __name__ == '__main__':
   main()

repl中的代码是：https : //repl.it/@gmunumel/SudokuSolver

任何的想法？

Answer 1

这是一种方法：

• 将图像转换为灰度并将中值模糊转换为平滑图像
• 自适应阈值获取二值图像
• 查找轮廓并过滤最大轮廓
• 执行透视变换以获得自上而下的视图

转换为灰度和中值模糊后，我们自适应阈值以获得二值图像

接下来我们找到轮廓并使用轮廓区域进行过滤。这是检测到的板

现在为了获得图像的自顶向下视图，我们执行透视变换。这是结果

import cv2
import numpy as np

def perspective_transform(image, corners):
    def order_corner_points(corners):
        # Separate corners into individual points
        # Index 0 - top-right
        #       1 - top-left
        #       2 - bottom-left
        #       3 - bottom-right
        corners = [(corner[0][0], corner[0][1]) for corner in corners]
        top_r, top_l, bottom_l, bottom_r = corners[0], corners[1], corners[2], corners[3]
        return (top_l, top_r, bottom_r, bottom_l)

    # Order points in clockwise order
    ordered_corners = order_corner_points(corners)
    top_l, top_r, bottom_r, bottom_l = ordered_corners

    # Determine width of new image which is the max distance between 
    # (bottom right and bottom left) or (top right and top left) x-coordinates
    width_A = np.sqrt(((bottom_r[0] - bottom_l[0]) ** 2) + ((bottom_r[1] - bottom_l[1]) ** 2))
    width_B = np.sqrt(((top_r[0] - top_l[0]) ** 2) + ((top_r[1] - top_l[1]) ** 2))
    width = max(int(width_A), int(width_B))

    # Determine height of new image which is the max distance between 
    # (top right and bottom right) or (top left and bottom left) y-coordinates
    height_A = np.sqrt(((top_r[0] - bottom_r[0]) ** 2) + ((top_r[1] - bottom_r[1]) ** 2))
    height_B = np.sqrt(((top_l[0] - bottom_l[0]) ** 2) + ((top_l[1] - bottom_l[1]) ** 2))
    height = max(int(height_A), int(height_B))

    # Construct new points to obtain top-down view of image in 
    # top_r, top_l, bottom_l, bottom_r order
    dimensions = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1], 
                    [0, height - 1]], dtype = "float32")

    # Convert to Numpy format
    ordered_corners = np.array(ordered_corners, dtype="float32")

    # Find perspective transform matrix
    matrix = cv2.getPerspectiveTransform(ordered_corners, dimensions)

    # Return the transformed image
    return cv2.warpPerspective(image, matrix, (width, height))

image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 3)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,3)

cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.015 * peri, True)
    transformed = perspective_transform(original, approx)
    break

cv2.imshow('transformed', transformed)
cv2.imwrite('board.png', transformed)
cv2.waitKey()