import cv2
'''
三种滤波的方法
高斯噪声
椒盐噪声
均值噪声
'''
## 中值滤波器
def median_blur(image, type, channel):
'''
:param image: 原始图像
:param type: 中值滤波的样式 3 3 1十字形 2 方形 3 ×形
:param channel: 频道数 2为 灰色 3为 彩色
:return: new_image
'''
new_image = image.copy()
if channel == 2: ##灰色
if type == 1: ##十字形 3 3
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
np = [new_image[row - 1][col],
new_image[row][col - 1], new_image[row][col], new_image[row][col + 1],
new_image[row + 1][col]]
np.sort()
new_image[row][col] = np[2]
return new_image
elif type == 2:
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
np = [new_image[row - 1][col - 1], new_image[row - 1][col], new_image[row - 1][col + 1],
new_image[row][col - 1], new_image[row][col], new_image[row][col + 1],
new_image[row + 1][col - 1], new_image[row + 1][col], new_image[row + 1][col + 1]]
np.sort()
new_image[row][col] = np[4]
return new_image
elif type == 3:
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
np = [new_image[row - 1][col - 1], new_image[row - 1][col + 1],
new_image[row][col],
new_image[row + 1][col - 1], new_image[row + 1][col + 1]]
np.sort()
new_image[row][col] = np[2]
return new_image
else:
print("type 参数输入错误！！！")
elif channel == 3:
if type == 2: #方形
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
for ch in range(new_image.shape[2]):
np = [new_image[row - 1][col - 1][ch],new_image[row - 1][col][ch],new_image[row - 1][col + 1][ch],
new_image[row][col - 1][ch], new_image[row][col][ch], new_image[row][col + 1][ch],
new_image[row + 1][col - 1][ch], new_image[row + 1][col][ch],new_image[row + 1][col + 1][ch]]
np.sort()
new_image[row][col][ch] = np[4]
return new_image
elif type == 1: ##十字形
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
for ch in range(new_image.shape[2]):
np = [new_image[row - 1][col][ch],
new_image[row][col - 1][ch], new_image[row][col][ch], new_image[row][col + 1][ch],
new_image[row + 1][col][ch]]
np.sort()
new_image[row][col][ch] = np[2]
return new_image
elif type == 3: # ×形
for row in range(1, new_image.shape[0] - 2):
for col in range(1, new_image.shape[1] - 2):
for ch in range(new_image.shape[2]):
np = [
new_image[row - 2][col - 2][ch], new_image[row - 2][col + 2][ch],
new_image[row - 1][col - 1][ch], new_image[row - 1][col + 1][ch],
new_image[row][col][ch],
new_image[row + 1][col - 1][ch], new_image[row + 1][col + 1][ch],
new_image[row + 2][col - 2][ch], new_image[row + 2][col + 2][ch],
]
np.sort()
new_image[row][col][ch] = np[4]
return new_image
else:
print("type 参数输入错误！！！")
else:
print("channel 参数输入错误！！！")
def cv_median_blur(image, dst):
'''
:param image: 原始图像
:param dst: 卷积核大小
:return:new_image
'''
new_image = cv2.medianBlur(image, dst)
return new_image
## 加权均值滤波器
def quan_meanfiltering(image, channel):
new_image = image.copy()
if channel == 2: ##灰色
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
np = [new_image[row - 1][col - 1], 2 * new_image[row - 1][col], new_image[row - 1][col + 1],
2 * new_image[row][col - 1], 4 * new_image[row][col], 2 * new_image[row][col + 1],
new_image[row + 1][col - 1], 2 * new_image[row + 1][col], new_image[row + 1][col + 1]]
new_image[row][col] = sum(np) / 16
return new_image
elif channel == 3:
for row in range(1, new_image.shape[0] - 1):
for col in range(1, new_image.shape[1] - 1):
for ch in range(new_image.shape[2]):
np = [new_image[row - 1][col - 1][ch], 4 * new_image[row - 1][col][ch],new_image[row - 1][col + 1][ch],
4 * new_image[row][col - 1][ch], 8 * new_image[row][col][ch], 4 * new_image[row][col + 1][ch],
new_image[row + 1][col - 1][ch], 4 * new_image[row + 1][col][ch],new_image[row + 1][col + 1][ch]]
new_image[row][col][ch] = sum(np) / 28
return new_image
else:
print("channel 参数输入错误！！！")
if __name__ == '__main__':
file_path = 'test.png'
image = cv2.imread(file_path)
new_image = median_blur(image,3,3)
new1_image = quan_meanfiltering(new_image,3)
cv2.imshow('for.png',new1_image)
cv2.waitKey(0)
