算法|Canny算法简介

#创建高斯矩阵

sigma1 = sigma2 = 1

sum = 0

gaussian = np.zeros([5, 5])#创建一个5*5的一维矩阵

for i in range(5):

    for j in range(5):

        gaussian[i, j] = math.exp(-1 / 2 * (np.square(i-3) / np.square(sigma1) + (np.square(j-3) / np.square(sigma2)))) / (2 * math.pi * sigma1 * sigma2)

        sum = sum + gaussian[i, j]

gaussian = gaussian / sum

#创建高斯滤波器

w =img.shape[0]

h =img.shape[1]

new_img = np.zeros([w-5,h-5])#创建一个新的数组，用于存储高斯滤波之后的图像

for i in range(w-5):

    for j in range(h-5):

        new_img[i,j] = np.sum(img[i:i+2,j:j+2]*gaussian)#使用卷积的方法进行高斯滤波

w1=new_img.shape[0]

h1=new_img.shape[1]

theta = np.zeros([w1-1, h1-1])

dx = np.zeros([w1-1, h1-1])

dy = np.zeros([w1-1, h1-1])

d = np.zeros([w1-1, h1-1])#创建新数组用于存储得到的梯度幅度图像

for i in range(w1-1):

    for j in range(h1-1):

        dx[i,j] = new_img[i, j+1] - new_img [i, j]

        dy[i,j] = new_img [i+1, j] - new_img [i, j]

        d[i,j]=np.sqrt(np.square(dx[i,j])+ np.square(dy[i,j]))

        #计算梯度方向

         theta[i, j] = math.atan(dx[i, j] / (dy[i, j] + 0.000000001))