python canny算法_Python实现Canny及Hough算法代码实例解析

任务说明：编写一个钱币定位系统，其不仅能够检测出输入图像中各个钱币的边缘，同时，还能给出各个钱币的圆心坐标与半径。

效果

代码实现

Canny边缘检测：

# Author: Ji Qiu (BUPT)

# filename: my_canny.py

import cv2

import numpy as np

class Canny:

def __init__(self, Guassian_kernal_size, img, HT_high_threshold, HT_low_threshold):

'''

:param Guassian_kernal_size: 高斯滤波器尺寸

:param img: 输入的图片，在算法过程中改变

:param HT_high_threshold: 滞后阈值法中的高阈值

:param HT_low_threshold: 滞后阈值法中的低阈值

'''

self.Guassian_kernal_size = Guassian_kernal_size

self.img = img

self.y, self.x = img.shape[0:2]

self.angle = np.zeros([self.y, self.x])

self.img_origin = None

self.x_kernal = np.array([[-1, 1]])

self.y_kernal = np.array([[-1], [1]])

self.HT_high_threshold = HT_high_threshold

self.HT_low_threshold = HT_low_threshold

def Get_gradient_img(self):

'''

计算梯度图和梯度方向矩阵。

:return: 生成的梯度图

'''

print ('Get_gradient_img')

new_img_x = np.zeros([self.y, self.x], dtype=np.float)

new_img_y = np.zeros([self.y, self.x], dtype=np.float)

for i in range(0, self.x):

for j in range(0, self.y):

if j == 0:

new_img_y[j][i] = 1

else:

new_img_y[j][i] = np.sum(np.array([[self.img[j - 1][i]], [self.img[j][i]]]) * self.y_kernal)

if i == 0:

new_img_x[j][i] = 1

else:

new_img_x[j][i] = np.sum(np.array([self.img[j][i - 1], self.img[j][i]]) * self.x_kernal)

gradient_img, self.angle = cv2.cartToPolar(new_img_x, new_img_y)#返回幅值和相位

self.angle = np.tan(self.angle)

self.img = gradient_img.astype(np.uint8)

return self.img

def Non_maximum_suppression (self):

'''

对生成的梯度图进行非极大化抑制，将tan值的大小与正负结合，确定离散中梯度的方向。

:return: 生成的非极大化抑制结果图

'''

print ('Non_maximum_suppression')

result = np.zeros([self.y, self.x])

for i in range(1, self.y - 1):

for j in range(1, self.x - 1):

if abs(self.img[i][j]) <= 4:

result[i][j] = 0

continue

elif abs(self.angle[i][j]) > 1:

gradient2 = self.img[i - 1][j]

gradient4 = self.img[i + 1][j]

# g1 g2

# C

# g4 g3

if self.angle[i][j] > 0:

gradient1 = self.img[i - 1][j - 1]

gradient3 = self.img[i + 1][j + 1]

# g2 g1

# C

# g3 g4

else:

gradient1 = self.img[i - 1][j + 1]

gradient3 = self.img[i + 1][j - 1]

else:

gradient2 = self.img[i][j - 1]

gradient4 = self.img[i][j + 1]

# g1

# g2 C g4

# g3

if self.angle[i][j] > 0:

gradient1 = self.img[i - 1][j - 1]

gradient3 = self.img[i + 1][j + 1]

# g3

# g2 C g4

# g1

else:

gradient3 = self.img[i - 1][j + 1]

gradient1 = self.img[i + 1][j - 1]

temp1 = abs(self.angle[i][j]) * gradient1 + (1 - abs(self.angle[i][j])) * gradient2

temp2 = abs(self.angle[i][j]) * gradient3 + (1 - abs(self.angle[i][j])) * gradient4

if self.img[i][j] >= temp1 and self.img[i][j] >= temp2:

result[i][j] = self.img[i][j]

else:

result[i][j] = 0

self.img = result

return self.img

def Hysteresis_thresholding(self):

'''

对生成的非极大化抑制结果图进行滞后阈值法，用强边延伸弱边，这里的延伸方向为梯度的垂直方向，

将比低阈值大比高阈值小的点置为高阈值大小，方向在离散点上的确定与非极大化抑制相似。

:return: 滞后阈值法结果图

'''

print ('Hysteresis_thresholding')

for i in range(1, self.y - 1):

for j in range(1, self.x - 1):

if self.img[i][j] >= self.HT_high_threshold:

if abs(self.angle[i][j]) < 1:

if self.img_origin[i - 1][j] > self.HT_low_threshold:

self.img[i - 1][j] = self.HT_high_threshold

if self.img_origin[i + 1][j] > self.HT_low_threshold:

self.img[i + 1][j] = self.HT_high_threshold

# g1 g2

# C

# g4 g3

if self.angle[i][j] < 0:

if self.img_origin[i - 1][j - 1] > self.HT_low_threshold:

self.img[i - 1][j - 1] = self.HT_high_threshold

if self.img_origin[i + 1][j + 1] > self.HT_low_threshold:

self.img[i + 1][j + 1] = self.HT_high_threshold

# g2 g1

# C

# g3 g4

else:

if self.img_origin[i - 1][j + 1] > self.HT_low_threshold:

self.img[i - 1][j + 1] = self.HT_high_threshold

if self.img_origin[i + 1][j - 1] > self.HT_low_threshold:

self.img[i + 1][j - 1] = self.HT_high_threshold

else:

if self.img_origin[i][j - 1] > self.HT_low_threshold:

self.img[i][j - 1] = self.HT_high_threshold

if self.img_origin[i][j + 1] > self.HT_low_threshold:

self.img[i][j + 1] = self.HT_high_threshold

# g1

# g2 C g4

# g3

if self.angle[i][j] < 0:

if self.img_origin[i - 1][j - 1] > self.HT_low_threshold:

self.img[i - 1][j - 1] = self.HT_high_threshold

if self.img_origin[i + 1][j + 1] > self.HT_low_threshold:

self.img[i + 1][j + 1] = self.HT_high_threshold

# g3

# g2 C g4

# g1

else:

if self.img_origin[i - 1][j + 1] > self.HT_low_threshold:

self.img[i + 1][j - 1] = self.HT_high_threshold

if self.img_origin[i + 1][j - 1] > self.HT_low_threshold:

self.img[i + 1][j - 1] = self.HT_high_threshold

return self.img

def canny_algorithm(self):

'''

按照顺序和步骤调用以上所有成员函数。

:return: Canny 算法的结果

'''

self.img = cv2.GaussianBlur(self.img, (self.Guassian_kernal_size, self.Guassian_kernal_size), 0)

self.Get_gradient_img()

self.img_origin = self.img.copy()

self.Non_maximum_suppression()

self.Hysteresis_thresholding()

return self.img

Hough变换

# Author: Ji Qiu (BUPT)

# filename: my_hough.py

import numpy as np

import math

class Hough_transform:

def __init__(self, img, angle, step=5, threshold=135):

'''

:param img: 输入的图像

:param angle: 输入的梯度方向矩阵

:param step: Hough 变换步长大小

:param threshold: 筛选单元的阈值

'''

self.img = img

self.angle = angle

self.y, self.x = img.shape[0:2]

self.radius = math.ceil(math.sqrt(self.y**2 + self.x**2))

self.step = step

self.vote_matrix = np.zeros([math.ceil(self.y / self.step), math.ceil(self.x / self.step), math.ceil(self.radius / self.step)])

self.threshold = threshold

self.circles = []

def Hough_transform_algorithm(self):

'''

按照 x,y,radius 建立三维空间，根据图片中边上的点沿梯度方向对空间中的所有单

元进行投票。每个点投出来结果为一折线。

:return: 投票矩阵

'''

print ('Hough_transform_algorithm')

for i in range(1, self.y - 1):

for j in range(1, self.x - 1):

if self.img[i][j] > 0:

y = i

x = j

r = 0

while y < self.y and x < self.x and y >= 0 and x >= 0:

self.vote_matrix[math.floor(y / self.step)][math.floor(x / self.step)][math.floor(r / self.step)] += 1

y = y + self.step * self.angle[i][j]

x = x + self.step

r = r + math.sqrt((self.step * self.angle[i][j])**2 + self.step**2)

y = i - self.step * self.angle[i][j]

x = j - self.step

r = math.sqrt((self.step * self.angle[i][j])**2 + self.step**2)

while y < self.y and x < self.x and y >= 0 and x >= 0:

self.vote_matrix[math.floor(y / self.step)][math.floor(x / self.step)][math.floor(r / self.step)] += 1

y = y - self.step * self.angle[i][j]

x = x - self.step

r = r + math.sqrt((self.step * self.angle[i][j])**2 + self.step**2)

return self.vote_matrix

def Select_Circle(self):

'''

按照阈值从投票矩阵中筛选出合适的圆，并作极大化抑制，这里的非极大化抑制我采

用的是邻近点结果取平均值的方法，而非单纯的取极大值。

:return: None

'''

print ('Select_Circle')

houxuanyuan = []

for i in range(0, math.ceil(self.y / self.step)):

for j in range(0, math.ceil(self.x / self.step)):

for r in range(0, math.ceil(self.radius / self.step)):

if self.vote_matrix[i][j][r] >= self.threshold:

y = i * self.step + self.step / 2

x = j * self.step + self.step / 2

r = r * self.step + self.step / 2

houxuanyuan.append((math.ceil(x), math.ceil(y), math.ceil(r)))

if len(houxuanyuan) == 0:

print("No Circle in this threshold.")

return

x, y, r = houxuanyuan[0]

possible = []

middle = []

for circle in houxuanyuan:

if abs(x - circle[0]) <= 20 and abs(y - circle[1]) <= 20:

possible.append([circle[0], circle[1], circle[2]])

else:

result = np.array(possible).mean(axis=0)

middle.append((result[0], result[1], result[2]))

possible.clear()

x, y, r = circle

possible.append([x, y, r])

result = np.array(possible).mean(axis=0)

middle.append((result[0], result[1], result[2]))

def takeFirst(elem):

return elem[0]

middle.sort(key=takeFirst)

x, y, r = middle[0]

possible = []

for circle in middle:

if abs(x - circle[0]) <= 20 and abs(y - circle[1]) <= 20:

possible.append([circle[0], circle[1], circle[2]])

else:

result = np.array(possible).mean(axis=0)

print("Circle core: (%f, %f) Radius: %f" % (result[0], result[1], result[2]))

self.circles.append((result[0], result[1], result[2]))

possible.clear()

x, y, r = circle

possible.append([x, y, r])

result = np.array(possible).mean(axis=0)

print("Circle core: (%f, %f) Radius: %f" % (result[0], result[1], result[2]))

self.circles.append((result[0], result[1], result[2]))

def Calculate(self):

'''

按照算法顺序调用以上成员函数

:return: 圆形拟合结果图，圆的坐标及半径集合

'''

self.Hough_transform_algorithm()

self.Select_Circle()

return self.circles

调用

# Author: Ji Qiu (BUPT)

# filename: main.py

import cv2

import math

from my_hough import Hough_transform

from my_canny import Canny

# np.set_printoptions(threshold=np.inf)

Path = "picture_source/picture.jpg"

Save_Path = "picture_result/"

Reduced_ratio = 2

Guassian_kernal_size = 3

HT_high_threshold = 25

HT_low_threshold = 6

Hough_transform_step = 6

Hough_transform_threshold = 110

if __name__ == '__main__':

img_gray = cv2.imread(Path, cv2.IMREAD_GRAYSCALE)

img_RGB = cv2.imread(Path)

y, x = img_gray.shape[0:2]

img_gray = cv2.resize(img_gray, (int(x / Reduced_ratio), int(y / Reduced_ratio)))

img_RGB = cv2.resize(img_RGB, (int(x / Reduced_ratio), int(y / Reduced_ratio)))

# canny takes about 40 seconds

print ('Canny ...')

canny = Canny(Guassian_kernal_size, img_gray, HT_high_threshold, HT_low_threshold)

canny.canny_algorithm()

cv2.imwrite(Save_Path + "canny_result.jpg", canny.img)

# hough takes about 30 seconds

print ('Hough ...')

Hough = Hough_transform(canny.img, canny.angle, Hough_transform_step, Hough_transform_threshold)

circles = Hough.Calculate()

for circle in circles:

cv2.circle(img_RGB, (math.ceil(circle[0]), math.ceil(circle[1])), math.ceil(circle[2]), (28, 36, 237), 2)

cv2.imwrite(Save_Path + "hough_result.jpg", img_RGB)

print ('Finished!')

运行效果

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