如何在 OpenCV 中检测数独网格板

Question

我正在使用 python 中的opencv 进行个人项目。想要检测数独网格。

原图为：

到目前为止，我已经创建了这个：

然后尝试选择一个大斑点。结果可能类似于：

结果我得到了一张黑色图像：

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

有什么想法吗？

Answer 1

这是一种方法：

• 将图像转换为灰度，将中值模糊转换为平滑图像
• 获取二值图像的自适应阈值
• 查找轮廓并过滤最大轮廓
• 执行透视变换以获得俯视图

---

转换为灰度和中值模糊后，我们自适应阈值得到二值图像

接下来我们找到轮廓并使用轮廓区域进行过滤。这是检测到的电路板

现在要获得图像的俯视图，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()

Answer 2

这是我的解决方案，它可以推广到任何图像，无论它是否变形。

• 将图像转换为灰度
• 应用自适应阈值将图像转换为二进制 （自适应阈值比普通阈值效果更好，因为原始图像在不同区域可以有不同的光照）
• 识别大正方形的角
• 图像到最终方形图像的透视变换

根据原始图像的偏斜程度，识别出的角可能是乱序的，我们是否需要以正确的顺序排列它们。这里使用的方法是识别大正方形的质心并从那里识别角的顺序

代码如下：

import cv2
import numpy as np



    # Helper functions for getting square image

def euclidian_distance(point1, point2):
    # Calcuates the euclidian distance between the point1 and point2
    #used to calculate the length of the four sides of the square 
    distance = np.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)
    return distance


def order_corner_points(corners):
    # The points obtained from contours may not be in order because of the skewness  of the image, or
    # because of the camera angle. This function returns a list of corners in the right order 
    sort_corners = [(corner[0][0], corner[0][1]) for corner in corners]
    sort_corners = [list(ele) for ele in sort_corners]
    x, y = [], []

    for i in range(len(sort_corners[:])):
        x.append(sort_corners[i][0])
        y.append(sort_corners[i][1])

    centroid = [sum(x) / len(x), sum(y) / len(y)]

    for _, item in enumerate(sort_corners):
        if item[0] < centroid[0]:
            if item[1] < centroid[1]:
                top_left = item
            else:
                bottom_left = item
        elif item[0] > centroid[0]:
            if item[1] < centroid[1]:
                top_right = item
            else:
                bottom_right = item

    ordered_corners = [top_left, top_right, bottom_right, bottom_left]

    return np.array(ordered_corners, dtype="float32")
def image_preprocessing(image, corners):
    # This function undertakes all the preprocessing of the image and return  
    ordered_corners = order_corner_points(corners)
    print("ordered corners: ", ordered_corners)
    top_left, top_right, bottom_right, bottom_left = ordered_corners

    # Determine the widths and heights  ( Top and bottom ) of the image and find the max of them for transform 

    width1 = euclidian_distance(bottom_right, bottom_left)
    width2 = euclidian_distance(top_right, top_left)

    height1 = euclidian_distance(top_right, bottom_right)
    height2 = euclidian_distance(top_left, bottom_right)

    width = max(int(width1), int(width2))
    height = max(int(height1), int(height2))

    # To find the matrix for warp perspective function we need dimensions and matrix parameters
    dimensions = np.array([[0, 0], [width, 0], [width, width],
                           [0, width]], dtype="float32")

    matrix = cv2.getPerspectiveTransform(ordered_corners, dimensions)

    # Return the transformed image
    transformed_image = cv2.warpPerspective(image, matrix, (width, width))

    #Now, chances are, you may want to return your image into a specific size. If not, you may ignore the following line
    transformed_image = cv2.resize(transformed_image, (252, 252), interpolation=cv2.INTER_AREA)

    return transformed_image

    # main function

def get_square_box_from_image(image):
    # This function returns the top-down view of the puzzle in grayscale.
    # 

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.medianBlur(gray, 3)
    adaptive_threshold = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 3)
    corners = cv2.findContours(adaptive_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    corners = corners[0] if len(corners) == 2 else corners[1]
    corners = sorted(corners, key=cv2.contourArea, reverse=True)
    for corner in corners:
        length = cv2.arcLength(corner, True)
        approx = cv2.approxPolyDP(corner, 0.015 * length, True)
        print(approx)

        puzzle_image = image_preprocessing(image, approx)
        break
    return puzzle_image

    # Call the get_square_box_from_image method on any sudoku image to get the top view of the puzzle

original = cv2.imread("large_puzzle.jpg")

sudoku = get_square_box_from_image(original)

以下是给定图像和自定义示例的结果