python sobel滤波_Sobel滤波器

一. sobel滤波器介绍

sobel滤波器常用来提取灰度图像的水平边缘(水平特征)和竖直边缘(竖直特征)

二. sobel算子

纵向算子，提取图像水平边缘 ↑

横向算子，提取图像竖直边缘 ↑

三. 实验：python实现sobel算子并将算子作用于图像

import cv2

import numpy as np

# Gray scale

def BGR2GRAY(img):

b = img[:, :, 0].copy()

g = img[:, :, 1].copy()

r = img[:, :, 2].copy()

# Gray scale

out = 0.2126 * r + 0.7152 * g + 0.0722 * b

out = out.astype(np.uint8)

return out

# sobel filter

def sobel_filter(img, K_size=3):

if len(img.shape) == 3:

H, W, C = img.shape

else:

H, W = img.shape

# Zero padding

pad = K_size // 2

out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float)

out[pad: pad + H, pad: pad + W] = img.copy().astype(np.float)

tmp = out.copy()

out_v = out.copy()

out_h = out.copy()

## Sobel vertical

Kv = [[1., 2., 1.],[0., 0., 0.], [-1., -2., -1.]]

## Sobel horizontal

Kh = [[1., 0., -1.],[2., 0., -2.],[1., 0., -1.]]

# filtering

for y in range(H):

for x in range(W):

out_v[pad + y, pad + x] = np.sum(Kv * (tmp[y: y + K_size, x: x + K_size]))

out_h[pad + y, pad + x] = np.sum(Kh * (tmp[y: y + K_size, x: x + K_size]))

out_v = np.clip(out_v, 0, 255)

out_h = np.clip(out_h, 0, 255)

out_v = out_v[pad: pad + H, pad: pad + W].astype(np.uint8)

out_h = out_h[pad: pad + H, pad: pad + W].astype(np.uint8)

return out_v, out_h

# Read image

img = cv2.imread(“../paojie.jpg”).astype(np.float)

# grayscale

gray = BGR2GRAY(img)

# sobel filtering

out_v, out_h = sobel_filter(gray, K_size=3)

# Save result

cv2.imwrite(“out_g.jpg”,gray)

cv2.imshow(“result_g”,gray)

cv2.imwrite(“out_v.jpg”, out_v)

cv2.imshow(“result_v”, out_v)

cv2.imwrite(“out_h.jpg”, out_h)

cv2.imshow(“result_h”, out_h)

cv2.waitKey(0)

cv2.destroyAllWindows()

四. 实验结果

原图 ↑

原图转换为灰度图像 ↑

sobel横向算子提取了图像的竖直特征 ↑

sobel纵向算子提取了图像的水平特征 ↑

从本实验结果我们观察到，在提取图像在水平或者垂直方向上的线条或轮廓时，可以使用sobel算子。

五. 参考内容：