python实现Canny与Hough算法

文章首发：xmoon.info

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

学习资源：北京邮电大学计算机视觉——鲁鹏

本文地址：https://blog.csdn.net/moonoa/article/details/107669000