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72 lines
2.5 KiB
72 lines
2.5 KiB
import numpy as np
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import cv2
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import math
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def noise_desc():
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image = cv2.imread("image/test.png", cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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# 遍历图像，获取叠加噪声后的图像
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for i in range(image.shape[0]):
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for j in range(image.shape[1]):
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if image[i][j]<100:
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# 添加食盐噪声
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output[i][j]=255
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elif image[i][j]>200:
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# 添加胡椒噪声
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output[i][j]=0
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else:
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# 不添加噪声
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output[i][j]=image[i][j]
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cv2.imwrite("image/result.jpg",output)
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print(np.sum(output))
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def aver_filter():
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image = cv2.imread("image/test.png", cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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# 遍历图像，进行均值滤波
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for i in range(image.shape[0]):
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for j in range(image.shape[1]):
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# 计算均值,完成对图片src的几何均值滤波
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ji = 1.0
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for n in range(-1,2):
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# 防止越界
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if 0 <= j + n < image.shape[1]:
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ji *= image[i][j + n]
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output[i][j] = int(math.pow(ji,1/3))
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# 滤波器的大小为1*3
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######### End #########
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# 展示均值滤波后的图片
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cv2.imwrite("image/result.jpg", output)
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def sort_filter():
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image = cv2.imread("image/test.png", cv2.IMREAD_GRAYSCALE)
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# 待输出的图片
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output = np.zeros(image.shape, np.uint8)
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for i in range(image.shape[0]):
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for j in range(image.shape[1]):
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# 最大值滤波器
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max=0
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for m in range(-1, 2):
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for n in range(-1, 2):
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# 防止越界
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if 0 <= i + m < image.shape[0] and 0 <= j + n < image.shape[1]:
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if max<image[i+m][j+n]:
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max=image[i+m][j+n]
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# 更新最大值
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output[i][j]=max
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cv2.imwrite("image/result.jpg",output)
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def opt_filter():
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image = cv2.imread("image/test.png", cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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max=200
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for i in range(image.shape[0]):
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for j in range(image.shape[1]):
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if image[i][j] <= max:
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output[i][j] = 0
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else:
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output[i][j] = image[i][j]
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cv2.imwrite("image/result.jpg",output)
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