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import cv2

import numpy as np

img = cv2.imread('../photos/1.png')

# cv2.imshow('maze',img)

# cv2.waitKey(0)

# cv2.destroyAllWindows()

#对图像进行二值化

# Binary conversion

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#反转tholdolding将给我们一个二进制的图像与白色的墙壁和黑色的背景。

ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)

cv2.imshow('THRESH_BINARY_INV',thresh)

cv2.waitKey(0)

cv2.destroyAllWindows()

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#对二值化处理的图像进行轮廓检测并标注

# Contours

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,

                                       cv2.CHAIN_APPROX_NONE)

print('len(contours):',len(contours))

# dc=cv2.drawContours(thresh, contours, 0, (255, 255, 255), -1)

#用不同颜色来标注

dc=cv2.drawContours(thresh, contours, 0, (255, 255, 255), 5)

# TODO 大迷宫的len(contours): 26

dc=cv2.drawContours(dc, contours, 1, (0, 0, 0), 5)

cv2.imshow('drawContours',dc)

cv2.waitKey(0)

cv2.destroyAllWindows()

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#对图像阈值进行处理

ret, thresh = cv2.threshold(dc, 240, 255, cv2.THRESH_BINARY)

# ret, thresh = cv2.threshold(thresh, 240, 255, cv2.THRESH_BINARY)

cv2.imshow('thresh2',thresh)

cv2.waitKey(0)

cv2.destroyAllWindows()

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# Dilate

ke = 10

# kernel = np.ones((19, 19), np.uint8)

kernel = np.ones((ke, ke), np.uint8)

dilation = cv2.dilate(thresh, kernel, iterations=1)

cv2.imshow('dilation',dilation)

cv2.waitKey(0)

cv2.destroyAllWindows()

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# Erosion

erosion = cv2.erode(dilation, kernel, iterations=1)

cv2.imshow('erosion',erosion)

cv2.waitKey(0)

cv2.destroyAllWindows()

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#找到两个图像的差异

diff = cv2.absdiff(dilation, erosion)

# 分迷宫的通道

b, g, r = cv2.split(img)

mask_inv = cv2.bitwise_not(diff)

# masking out the green and red colour from the solved path

r = cv2.bitwise_and(r, r, mask=mask_inv)

g = cv2.bitwise_and(g, g, mask=mask_inv)

res = cv2.merge((b, g, r))

cv2.imshow('Solved Maze', res)

cv2.waitKey(0)

cv2.destroyAllWindows()

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import cv2

import numpy as np

img = cv2.imread('../photos/1.png')

cv2.imshow('maze',img)

cv2.waitKey(0)

cv2.destroyAllWindows()

#对图像进行二值化

# Binary conversion

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#反转tholdolding将给我们一个二进制的图像与白色的墙壁和黑色的背景。

ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)

cv2.imshow('THRESH_BINARY_INV',thresh)

cv2.waitKey(0)

cv2.destroyAllWindows()

#对二值化处理的图像进行轮廓检测并标注

# Contours

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,

                                       cv2.CHAIN_APPROX_NONE)

print('len(contours):',len(contours))

# dc=cv2.drawContours(thresh, contours, 0, (255, 255, 255), -1)

#用不同颜色来标注

dc=cv2.drawContours(thresh, contours, 0, (255, 255, 255), 5)

# TODO 大迷宫的len(contours): 26

dc=cv2.drawContours(dc, contours, 1, (0, 0, 0), 5)

cv2.imshow('drawContours',dc)

cv2.waitKey(0)

cv2.destroyAllWindows()

#对图像阈值进行处理

ret, thresh = cv2.threshold(dc, 240, 255, cv2.THRESH_BINARY)

# ret, thresh = cv2.threshold(thresh, 240, 255, cv2.THRESH_BINARY)

cv2.imshow('thresh2',thresh)

cv2.waitKey(0)

cv2.destroyAllWindows()

'''

扩张

扩张是数学形态领域的两个基本操作者之一，另一个是侵蚀。它通常应用于二进制图像，但有一些版本可用于灰度图像。

操作者对二进制图像的基本效果是逐渐扩大前景像素区域的边界（通常为白色像素）。因此，前景像素的面积大小增加，而这些区域内的孔变小。

'''

# Dilate

ke = 10

# kernel = np.ones((19, 19), np.uint8)

kernel = np.ones((ke, ke), np.uint8)

dilation = cv2.dilate(thresh, kernel, iterations=1)

cv2.imshow('dilation',dilation)

cv2.waitKey(0)

cv2.destroyAllWindows()

# Erosion

#侵蚀是第二个形态运算符。它也适用于二进制图像。操作者对二进制图像的基本效果是消除前景像素区域的边界（通常为白色像素）。

# 因此，前景像素的面积缩小，并且这些区域内的孔变大。

erosion = cv2.erode(dilation, kernel, iterations=1)

cv2.imshow('erosion',erosion)

cv2.waitKey(0)

cv2.destroyAllWindows()

#找到两个图像的差异

diff = cv2.absdiff(dilation, erosion)

cv2.imshow('diff',diff)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 分迷宫的通道

b, g, r = cv2.split(img)

mask_inv = cv2.bitwise_not(diff)

#为了在原始迷宫图像上显示解决方案，首先将原来的迷宫分割成r，g，b组件。现在通过反转diff图像创建一个掩码。

# 使用在最后一步中创建的掩码的原始迷宫的按位和r和g分量。这一步将从迷宫解决方案的图像部分去除红色和绿色成分。

# 最后一个是合并所有组件，我们将使用蓝色标记的解决方案。

# masking out the green and red colour from the solved path

r = cv2.bitwise_and(r, r, mask=mask_inv)

g = cv2.bitwise_and(g, g, mask=mask_inv)

res = cv2.merge((b, g, r))

cv2.imshow('Solved Maze', res)

cv2.waitKey(0)

cv2.destroyAllWindows()