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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

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