最大值滤波器和最小值滤波器的python代码

最大值滤波器代码：

import cv2
import numpy as np

def max_filter(img, kernel_size):
    # padding the image with zeros
    pad_size = kernel_size // 2
    padded_img = np.pad(img, ((pad_size, pad_size), (pad_size, pad_size), (0, 0)), mode='constant')
    
    # create the kernel
    kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
    
    # apply the maximum filter
    max_filtered = np.zeros_like(img)
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            max_val = np.max(padded_img[i:i+kernel_size, j:j+kernel_size])
            max_filtered[i, j] = max_val
    
    return max_filtered

# load the image
img = cv2.imread('image.jpg')

# apply the maximum filter
max_filtered = max_filter(img, 5)

# display the original and filtered images
cv2.imshow('Original Image', img)
cv2.imshow('Maximum Filtered Image', max_filtered)
cv2.waitKey(0)

最小值滤波器代码：

import cv2
import numpy as np

def min_filter(img, kernel_size):
    # padding the image with zeros
    pad_size = kernel_size // 2
    padded_img = np.pad(img, ((pad_size, pad_size), (pad_size, pad_size), (0, 0)), mode='constant')
    
    # create the kernel
    kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
    
    # apply the minimum filter
    min_filtered = np.zeros_like(img)
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            min_val = np.min(padded_img[i:i+kernel_size, j:j+kernel_size])
            min_filtered[i, j] = min_val
    
    return min_filtered

# load the image
img = cv2.imread('image.jpg')

# apply the minimum filter
min_filtered = min_filter(img, 5)

# display the original and filtered images
cv2.imshow('Original Image', img)
cv2.imshow('Minimum Filtered Image', min_filtered)
cv2.waitKey(0)