Python实现Canny及Hough算法代码实例解析
作者:Moon小木 发布时间:2022-10-15 14:23:37
标签:Python,Canny,Hough,算法
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
来源:https://www.cnblogs.com/moonspace/p/13403099.html
0
投稿
猜你喜欢
- WordPress 本身以及主题和插件通常需要加载一些 JavaScript 来实现某些特殊功能。为了最大限度地保证兼容性,不至于出现 Ja
- Background高斯噪声,顾名思义是指服从高斯分布(正态分布)的一类噪声。有的时候我们需要向标准数据中加入合适的高斯噪声让数据更加符合实
- 最近写了一个软件屡屡打包不能正常运行,而直接在cmd命令行窗口运行却都能正常输出,没有任何报错,一开始怀疑程序里面sys.args[1]使用
- 昨天在做一道CTF题的时候碰到了一个图片异或的问题,操作大概如下:将一个图片读入,然后每字节进行异或操作,核心代码可简化为以下:#codin
- 代码如下:---1.平均销售等待时间 ---有一张Sales表,其中有销售日期与顾客两列,现在要求使用一条SQL语句实现计算 --每个顾客的
- 1、引言小丝:鱼哥,还记得上次写的把数据库的查询结果写入到excel这个脚本不。小鱼:嗯… 可以说不记得吗小丝:我猜你
- MenuEverywhere 是Mac OS X上的一款小程序,前一阵刚为其完成了程序图标设计。© 2011 IconMo
- 与大多数可以面向对象的编程语言不一样, PHP 是同时支持面向过程和面向对象的编程方式, PHP 开发者可以在面向过程和面向对象二者中自由选
- 一、FBVFBV(function base views) 就是在视图里使用函数处理请求。二、CBVCBV(class base views
- 这几天在QQ群里知道了几个比较好的优化方面的站,感觉看高手的文章简直就是一种享受。和很多现在正在阅读这篇文章的站长一样,我即将毕业,但是还没
- python中,为了方便字符串的大小写转换,为我们提供了三种方法:title()lower()upper()python title()方法
- 回顾在前面的系列章节中,我们创建了一个数据库并且学着用用户和邮件来填充,但是到现在我们还没能够植入到我们的程序中。 两章之前,我
- 正则表达式的使用想要学习 Python 爬虫 , 首先需要了解一下正则表达式的使用,下面我们就来看看如何使用。. 的使用这个时候的点就相当于
- Python矩阵的基本用法mat()函数将目标数据的类型转化成矩阵(matrix)1,mat()函数和array()函数的区别Numpy函数
- 冒泡排序冒泡排序(英语:Bubble Sort)是一种简单的排序算法。它重复地遍历要排序的数列,一次比较两个元素,如果他们的顺序错误就把他们
- 读写文件首先看一个例子:f = open('thefile.txt','w') #以写方式打开,
- 本文实例讲述了phpmyadmin中禁止外网使用的方法。分享给大家供大家参考。具体方法如下:首先,在phpmyadmin文件夹中找到 php
- Yahoo发布了一款基于FireFox的插件,名叫YSlow,这个插件可以分析网站的页面,并告诉你为了提高网站性能,如何基于某些规则而进行优
- 在日常的工作中,有时候会有这样的需求,需要一个常驻任务,持续的监听一个目录下文件的变化,对此作出回应.pyinotify就是这样的一个pyt
- y = wx +b通过meshgrid 得到两个二维矩阵关键理解:plot_surface需要的xyz是二维np数组这里提前准备meshgr