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# 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
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WYXOGO
