Canny算子的Python实现

1.引言

Canny算子是一种较为好用的边缘检测算子，边缘检测效果良好。利用Python实现，原速度约为5分钟以上，不能够满足使用需要。

本文利用Python实现，并注意使用numba支持的数据类型及写法进行，故而可以利用numba进行加速，对5000*3000像素图像提取边缘，整体流程用时13秒，满足使用需要。

2.预备功能函数

1.卷积函数

def convolution(image, kernel):"""This function is used for convolution.Args:image: image input.kerbel: the filter."""kernel_height, kernel_width = kernel.shapeheight, width = image.shapekernel_size = kernel_heighthalf_size = np.floor(kernel_size/2)@numba.jit(nopython = True)def conv(image, kernel, half_size, height, width):result = np.zeros((height, width), dtype = np.float32)for row in range(half_size, height - half_size):for col in range(half_size, width - half_size):sum_var = 0for v_row in range(-1*half_size, half_size+1):for v_col in range(-1*half_size, half_size+1):sum_var = sum_var + image[row+v_row, col+v_col] * kernel[v_row, v_col]result[row, col] = sum_varreturn resultresult = conv(image, kernel, half_size, height, width)return result

2.高斯模板函数

用于提供一个高斯滤波核

def generate_Guassian_template(kernel_size = 3, sigma = 1):template = np.zeros((kernel_size, kernel_size), \dtype = np.float32)halfsize = np.floor(kernel_size/2).astype(np.int16)@numba.jit(nopython = True)def gaussian2d(x, y, sigma):result = np.exp(-(np.power(x, 2)+np.power(y, 2))/(2*np.power(sigma, 2)))return result@numba.jit(nopython = True)def generate(template, halfsize, sigma):for v_row in range(-1*halfsize, halfsize+1):for v_col in range(-1*halfsize, halfsize+1):template[v_row+halfsize, v_col+halfsize] = gaussian2d(v_row, v_col, sigma)element_sum = np.sum(template)template = template/element_sumreturn templatere = generate(template, halfsize, sigma)return re

3. 梯度类

暂时只实现了两种梯度，后面可能会增加Sobel梯度。

# -*- coding: utf-8 -*-
# @author: Dasheng Fan
# @email: fandasheng1999@163.comimport numpy as np
import numba
import syssys.path.append('../')
from utils.gray_processing import gray_processingclass RobertsGradient(object):"""Roberts is a kind of gradient of the image."""def __init__(self):""""""def calculate(self, image):"""Culculate the Roberts gradient gx and gy.Args:image: image to input.Return:gx, gy"""if len(image.shape) != 2:image = gray_processing(image)@numba.jit(nopython = True)def run(image):height, width = image.shapegradient_gy_img = np.zeros((height, width), dtype = np.float32)gradient_gx_img = np.zeros((height, width), dtype = np.float32)for row in range(1, height-1):for col in range(1, width-1):gradient_gx_img[row, col] = image[row, col] - image[row+1, col+1]gradient_gy_img[row, col] = image[row+1, col] - image[row, col+1]return gradient_gx_img, gradient_gy_imggx, gy = run(image)return gx, gyclass Gradient(object):def __init__(self):passdef calculate(self, image):@numba.jit(nopython=True)def run(image):height, width = image.shapegx = np.zeros((height, width), dtype = np.float32)gy = np.zeros((height, width), dtype = np.float32)for row in range(1, height-1):for col in range(1, width-1):gx[row, col] = image[row, col+1] - image[row, col]gy[row, col] = image[row+1, col] - image[row, col]return gx, gygx, gy = run(image)return gx, gy

4.灰度化函数

# -*- coding: utf-8 -*-
# @author: Dasheng Fan
# @email: fandasheng1999@163.com"""
Gray processing.
"""import numpy as np
from matplotlib import pyplot as plt
import sys
import osdef gray_processing(image):"""For gray processing.Args:image: A 3 channels image.Reture:A gray image.Reference:"https://baike.baidu.com/item/%E7%81%B0%E5%BA%A6%E5%8C%96/3206969?fr=aladdin""""if len(image.shape) != 3:raise Exception("The channel is wrong.")u = np.power(image[:, :, 0], 2.2) + np.power(1.5*image[:, :, 1], 2.2) + \np.power(0.6*image[:, :, 2], 2.2)d = 1 + np.power(1.5, 2.2) + np.power(0.6, 2.2)gray = np.power(u/d, 1/2.2)return grayif __name__ == '__main__':pass

3.实现

1.高斯滤波，参考：
https://www.jianshu.com/p/73e6ccbd8f3f
2.梯度计算：无参考
3.极大值抑制（Canny的很复杂）
主要参考：https://blog.csdn.net/kezunhai/article/details/11620357
4.双阈值检测，抑制孤立低阈值点
参考：https://www.cnblogs.com/techyan1990/p/7291771.html

代码：

# -*- coding: utf-8 -*-
# @author: Dasheng Fan
# @email: fandasheng1999@163.com"""
Canny operator.
"""
import numpy as np
import numba
from operators.diptools import generate_Guassian_template, convolution
from operators.gradient import RobertsGradient,Gradient
from utils.gray_processing import gray_processing
from utils.display import display, doubledisplayclass Canny(object):"""Canny operator is used for line detecting."""def __init__(self, Guassian_kernel_size = 3, Guassian_sigma = 1, high_threshhold_ratio = 0.15,low_threshhold_ratio = 0.08):self._G_kernel_size = Guassian_kernel_sizeself._G_sigma = Guassian_sigmaself._h_thresh_r = high_threshhold_ratioself._l_thresh_r = low_threshhold_ratiodef detect(self, image):if len(image.shape) != 2:image = gray_processing(image)height, width = image.shapeg_template = generate_Guassian_template()image = convolution(image, g_template)gradient = Gradient()gx, gy = gradient.calculate(image)@numba.jit(nopython = True)def nonmaxsuppress(gx,gy):"""Reference:https://blog.csdn.net/kezunhai/article/details/11620357"""g = np.sqrt(np.add(np.power(gx, 2), np.power(gy, 2)))angle = np.arctan(gy/gx)height, width = g.shapeflags = np.zeros((height, width), dtype = np.float32)for row in range(1, height-1):for col in range(1, width - 1):local_g = g[row, col]if np.abs(gy[row, col])>np.abs(gx[row, col]):if gy[row, col] == 0:weight = 1else:weight = np.abs(gx[row,col]/gy[row, col])g2 = g[row-1, col]g4 = g[row+1, col]if np.multiply(gx[row,col], gy[row, col])>0:g1 = g[row-1, col-1]g3 = g[row+1, col+1]else:g1 = g[row-1, col+1]g3 = g[row+1, col-1]else:if gx[row, col] == 0:weight = 1else:weight = np.abs(gy[row, col]/gx[row, col])g2 = g[row, col-1]g4 = g[row, col+1]if np.multiply(gx[row, col],gy[row,col])>0:g1 = g[row-1, col-1]g3 = g[row+1, col+1]else:g1 = g[row+1, col-1]g3 = g[row-1, col+1]inter_g1 = weight*g1 + (1-weight)*g2inter_g2 = weight*g3 + (1-weight)*g4 local_g = g[row, col]if local_g >=inter_g1 and local_g>=inter_g2:flags[row, col] = 1return flagsflags1 = nonmaxsuppress(gx, gy)@numba.jit(nopython = True)def _double_threshhold_suppress(high_threshhold_ratio, low_threshhold_ratio, gx, gy):"""The theory can be found here:https://www.cnblogs.com/techyan1990/p/7291771.htmlOnly the theory is refered, the codes are different."""g = np.sqrt(np.add(np.power(gx, 2), np.power(gy, 2)))height, width = g.shapemax_g = np.max(g)high_thresh = max_g * high_threshhold_ratiolow_thresh = max_g * low_threshhold_ratioflags = np.zeros((height, width), dtype = np.float32)for row in range(1, height-1):for col in range(1, width - 1):if g[row, col] >= high_thresh:flags[row, col] = 1elif g[row, col] > low_thresh and g[row, col] < high_thresh:# 不洋嚯了，写汉语# 这里检查一下八邻域， 如果有强边缘就认为弱边缘是边缘点for var_y in range(-1, 2):for var_x in range(-1, 2):if g[row+var_y, row+var_x] > high_thresh:flags[row, col] = 1breakelse:flags[row, col] = 0return flagsflags2 = _double_threshhold_suppress(self._h_thresh_r, self._l_thresh_r, gx, gy)flags = np.multiply(flags1, flags2)return flags

返回的flags是标志矩阵，矩阵中为1的元素对应的点是边缘点，为0则不是。

4.效果

校医院还是要检测一个的。

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