import numpy as np
import cv2
from copy import deepcopy
from PIL import Image
from matplotlib import pyplot as plt
"""制作噪声图像"""
def add_salt_noise(img, snr=0.5):
SNR = snr # 指定信噪比 (默认 0.5)
size = img.size # 获取总共像素个数
# print(type(size))
noiseSize = int(size * (1 - SNR))
for i in range(0, noiseSize):
# 随机获取 某个点
xi = int(np.random.uniform(0, img.shape[1]))
xj = int(np.random.uniform(0, img.shape[0]))
if img.ndim == 2: # 判断是否为 2 维图像 (即灰度图像)
img[xj, xi] = 0 # 设置值为黑点
elif img.ndim == 3:
img[xj, xi] = 0 # 设置值为黑点，也能够设置为红色 255
return img
"""黑白图中值滤波"""
def median_Blur(img, filiter_size=3): # 当输出的图像为彩色图像
image_copy = np.array(img, copy=True).astype(np.float32)
processed = np.zeros_like(image_copy)
middle = int(filiter_size / 2)
r = np.zeros(filiter_size * filiter_size)
g = np.zeros(filiter_size * filiter_size)
b = np.zeros(filiter_size * filiter_size)
for i in range(middle, image_copy.shape[0] - middle):
for j in range(middle, image_copy.shape[1] - middle):
count = 0
# 顺次取出模板中对应的像素值
for m in range(i - middle, i + middle + 1):
for n in range(j - middle, j + middle + 1):
r[count] = image_copy[m][n][0]
g[count] = image_copy[m][n][1]
b[count] = image_copy[m][n][2]
count += 1
r.sort()
g.sort()
b.sort()
processed[i][j][0] = r[int(filiter_size * filiter_size / 2)]
processed[i][j][1] = g[int(filiter_size * filiter_size / 2)]
processed[i][j][2] = b[int(filiter_size * filiter_size / 2)]
processed = np.clip(processed, 0, 255).astype(np.uint8)
return processed
"""灰度图中值滤波"""
def median_Blur_gray(img, filiter_size=3): # 当输出的图像为灰度图像
image_copy = np.array(img, copy=True).astype(np.float32)
processed = np.zeros_like(image_copy)
middle = int(filiter_size / 2)
for i in range(middle, image_copy.shape[0] - middle):
for j in range(middle, image_copy.shape[1] - middle):
temp = []
for m in range(i - middle, i + middle + 1):
for n in range(j - middle, j + middle + 1):
if m - middle < 0 or m + middle + 1 > image_copy.shape[0] or n - middle < 0 or n + middle + 1 > \
image_copy.shape[1]:
temp.append(0)
else:
temp.append(image_copy[m][n])
# count += 1
temp.sort()
processed[i][j] = temp[(int(filiter_size * filiter_size / 2) + 1)]
processed = np.clip(processed, 0, 255).astype(np.uint8)
return processed
if __name__ == "__main__":
img = input("Please input name of image:") # 默认是 string 类型
img = cv2.imread(img)
print("img.shape:", img.shape)
# plt.imshow 显示 cv2.imread 读取的图像蓝与红有区别
b, g, r = cv2.split(img)
src_img = cv2.merge([r, g, b])
img_demo = deepcopy(src_img) #使得两图像不烦扰
snr = float(input("Please input signal noise ratio:"))
img_salt = add_salt_noise(img_demo, snr)
filiter_size = int(input("Please input size of filiter:"))
median_blur = median_Blur(img_salt, filiter_size)
plt.figure("Image processing---MedianBlur") # 图像窗口名称
plt.subplot(1, 3, 1)
plt.imshow(src_img)
plt.axis('off') # 关掉坐标轴
plt.title("src_img")
plt.subplot(1, 3, 2)
plt.imshow(img_salt)
plt.axis('off') # 关掉坐标轴
plt.title("img_salt")
plt.subplot(1, 3, 3)
plt.imshow(median_blur)
plt.axis('off') # 关掉坐标轴
plt.title("median_blur")
plt.tight_layout() # 调整整体空白
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None) # 调整子图间距
plt.savefig("reult.png", dpi=1000)
plt.show()
信噪比为 0.5，卷积核为 3，进行中值滤波失去了这样的比照图：
信噪比为 0.9，卷积核为 3，进行中值滤波失去了这样的比照图：