opencv的滤波实现方法：均值滤波，双边滤波，高斯滤波，中值滤波，快速引导滤波，最小值滤波。

import numpy as npimport cv2path = "./058.jpg"img = cv2.imread(path)# 1.均值滤波img_mean = cv2.blur(img, (3, 3))# 2.双边滤波img_bilater = cv2.bilateralFilter(img, 9, 75, 75)# 3.高斯滤波img_Guassian = cv2.Gaussian

IT-DanZF

1170人浏览 · 2021-10-19 16:41:36

IT-DanZF · 2021-10-19 16:41:36 发布

import numpy as np
import cv2

path = "./058.jpg"
img = cv2.imread(path)

# 1.均值滤波
img_mean = cv2.blur(img, (3, 3))

# 2.双边滤波
img_bilater = cv2.bilateralFilter(img, 9, 75, 75)

# 3.高斯滤波
img_Guassian = cv2.GaussianBlur(img, (5, 5), 0)

# 4.中值滤波
img_median = cv2.medianBlur(img, 5)


# 5.快速引导滤波
def guidedfilter(I, p, r, eps):
    height, width = I.shape
    m_I = cv2.boxFilter(I, -1, (r, r))
    m_p = cv2.boxFilter(p, -1, (r, r))
    m_Ip = cv2.boxFilter(I * p, -1, (r, r))
    cov_Ip = m_Ip - m_I * m_p
    m_II = cv2.boxFilter(I * I, -1, (r, r))
    var_I = m_II - m_I * m_I
    a = cov_Ip / (var_I + eps)
    b = m_p - a * m_I
    m_a = cv2.boxFilter(a, -1, (r, r))
    m_b = cv2.boxFilter(b, -1, (r, r))
    return m_a * I + m_b


imd = guidedfilter(np.double(cv2.imread(path, 0)) / 255.0, img, 4 * 7, 0.001)

# 6.最小值滤波
image = cv2.erode(img, np.ones((14, 14)))