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79 lines
2.7 KiB
79 lines
2.7 KiB
import random
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import cv2
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import numpy as np
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class noiseCanceller:
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def __init__(self, imgPath):
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self.path = imgPath
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def noiseDescriber(self):
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image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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prob = 0.2
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thres = 1 - prob
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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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rdn = random.random()
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if rdn < prob:
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# 添加胡椒噪声
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output[i][j] = 0
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elif rdn > thres:
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# 添加食盐噪声
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output[i][j] = 255
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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("saved Img/out.bmp", output)
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def meanFilter(self):
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image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
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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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ij = 1
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for m in range(-1, 2):
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if 0 <= j + m < image.shape[1]:
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ij *= image[i][j + m]
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output[i][j] = ij ** (1 / 3)
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cv2.imwrite("saved Img/mean filter.bmp", output)
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def sortFilter(self):
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def get_max(array):
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length = len(array)
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for i in range(length):
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for j in range(i + 1, length):
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if array[j] > array[i]:
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temp = array[j]
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array[j] = array[i]
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array[i] = temp
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return array[0]
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image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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array = []
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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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array.clear()
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for m in range(-1, 2):
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for n in range(-1, 2):
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if 0 <= i + m < image.shape[0] and 0 <= j + n < image.shape[1]:
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array.append(image[i + m][j + n])
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output[i][j] = get_max(array)
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cv2.imwrite("saved Img/sort filter.bmp", output)
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def selectiveFilter(self):
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image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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minimum = 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 minimum < image[i][j]:
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output[i][j] = image[i][j]
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else:
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output[i][j] = 0
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cv2.imwrite("saved Img/selective filter.bmp", output)
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