数字图像处理 | python实现线性滤波和非线性滤波

python实现线性滤波和非线性滤波

• 数字图像处理 | python实现线性滤波和非线性滤波
• 前言
• 一、线性滤波是什么？
• 二、非线性滤波是什么？
• 三、python代码实现
• • 1.python实现
  • 运行结果
• 总结

这是数字图像处理课程上的作业，内容是使用非线性滤波和线性滤波处理一张图片。里面内容包含理论和代码，近期太忙，关于理论的讲解以后再补上。

代码如下（示例）：

import cv2 as cv
import numpy as np

convolution_kernel = np.array([
    [-1, 0, 1],
    [-2, 0, 2],
    [-1, 0, 1]
])


def linear_conv(input_image, input_conv):
    height = input_image.shape[0]
    width = input_image.shape[1]
    channels = input_image.shape[2]
    conv_height = input_conv.shape[0]
    matrix = np.zeros([height + 2, width + 2, channels], np.uint8)
    matrix[1:height+1, 1:width+1, :] = input_image
    new_image = []
    for cow in range(height):
        for col in range(width):
            for channel in range(channels):
                new_image.append(
                    np.sum(np.multiply(input_conv, matrix[cow:cow + conv_height, col:col + conv_height,  channel])))
    new_image = np.array(new_image).reshape([height, width, channels])/255.0
    cv.imshow("linear_conv_result", new_image)


def nonlinear_conv(input_image, shape):
    height = input_image.shape[0]
    width = input_image.shape[1]
    channels = input_image.shape[2]
    matrix = np.zeros([height + 2, width + 2, channels], np.uint8)
    matrix[1:height+1, 1:width+1, :] = input_image
    new_image = []
    for cow in range(height):
        for col in range(width):
            for channel in range(channels):
                new_image.append(np.max(matrix[cow:cow + shape, col:col + shape, channel]))
    new_image = np.array(new_image).reshape([height, width, channels])
    cv.imshow("nonlinear_conv_result", new_image)


# 取图片
src = cv.imread("1.jfif")   # blue,green,red
print(type(src))
# 窗口
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
# 展示
cv.imshow("input image", src)
# 线性
linear_conv(src, convolution_kernel)
# 非线性
nonlinear_conv(src, 3)
# 暂停
cv.waitKey(0)
cv.destroyAllWindows()

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