Median filter/maximum filter/mean filter python implementation

Meituan two-sided question

  

Briefly introduce three filtering ideas:

What they have in common: The three filtering methods can actually be understood as convolution operations, which only filter for a given Filter. If the kernel size is now 3*3 (if the values ​​are all set to 1), 9 cells will be processed. If there is no padding, it will start from the second row by default.

Median filtering: After element-wise multiplication of the 9 points of the candidate area and the 9 points of the filter, the point at the center of the kernel is assigned the median value of the 9 products. If there is a 3*3 kernel (all values ​​are 1) , then after completing the median filtering, the position of 5 is 18 at this time.

Maximum filtering and mean mean that after multiplying 9 elements element by element, the maximum and average of these 9 values ​​are taken.

The idea of ​​​​coding: considering the motion calculation of padding, stride and sliding window, at the same time, I need to know several functions of numpy. I forgot about it at the time, which was embarrassing.

import numpy as np

def median_filter(input_image,kernel,stride=1,padding=False):
    """
    中值滤波/最大滤波/均值滤波
    :param input_image: 输入图像
    :param filter_size: 滤波器大小
    :return:
    """

    # 填充（默认为1）
    padding_num = 1
    if padding:
        padding_num = int((kernel.shape[0]-1)/2)
        input_image = np.pad(input_image,(padding_num,padding_num),mode="constant",constant_values=0)

    out_image = np.copy(input_image)

    # 填充后的图像大小
    w,h = input_image.shape
    print(input_image.shape,padding_num)

    for i in range(padding_num,w-padding_num,stride):
        for j in range(padding_num,h-padding_num,stride):

            region = input_image[i-padding_num:i+padding_num+1,j-padding_num:j+padding_num+1]
            print(i,j)
            print(region.shape,kernel.shape)
            # 确保 图像提取的局部区域 与 核大小 一致
            assert (region.shape == kernel.shape)
            # 中值滤波np.median，  最大值滤波 np.maximum  均值滤波： np.mean
            out_image[i,j] = np.median(np.dot(region,kernel))



    # 裁剪原图像大小
    if padding:
        out_image = out_image[padding_num:w-padding_num,padding_num:h-padding_num]
    return out_image


if __name__ == '__main__':
    # 随机浮点数, 模仿灰度图
    input_image = np.random.rand(16,16)
    # 标准正态分布
    kernel = np.random.rand(3,3)
    print(input_image.shape,kernel.shape)
    output = median_filter(input_image,kernel)
    print(output.shape)