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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