opencv中的模糊方法使用

python
展开代码
import cv2
import numpy as np
def Gaussian(image):
    # 进行高斯模糊
    ksize = (11, 11)  # 模糊核的大小
    sigmaX = 5  # X方向的标准差，设为0表示从ksize计算
    image = cv2.GaussianBlur(image, ksize, sigmaX)
    image = cv2.GaussianBlur(image, ksize, sigmaX)
    return image
def blur(image):
    # 进行均值模糊
    ksize = (11, 11)  # 模糊核的大小
    image = cv2.blur(image, ksize)
    image = cv2.blur(image, ksize)
    return image
def medianBlur(image):
    # 进行中值模糊
    ksize = 11  # 模糊核的大小
    image = cv2.medianBlur(image, ksize)
    image = cv2.medianBlur(image, ksize)
    return image
def bilateralFilter(image):
    # 进行双边滤波
    d = 15  # 领域直径
    sigmaColor = 75  # 色彩空间的标准差
    sigmaSpace = 75  # 坐标空间的标准差
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    return image
def medianBlur(image):
    # 进行双边滤波
    d = 15  # 领域直径
    sigmaColor = 75  # 色彩空间的标准差
    sigmaSpace = 75  # 坐标空间的标准差
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    image = cv2.bilateralFilter(image, d, sigmaColor, sigmaSpace)
    return image
def filter2D(image):
    # 自定义模糊核
    kernel = np.ones((5, 5), dtype=np.float32) / 25  # 5x5的均匀权重的模糊核
    # 进行自定义模糊
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    image = cv2.filter2D(image, -1, kernel)
    return image
# 读取图像
image = cv2.imread('image.jpg')
image = filter2D(image)
# 显示模糊后的图像
cv2.imshow('Gaussian Blur', image)
cv2.waitKey(0)
cv2.destroyAllWindows()