python实现线性滤波和非线性滤波
数字图像处理 | python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
前言
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。
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
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()运行结果
总结
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