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import math
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import cv2
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import numpy as np
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import struct
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import matplotlib.pyplot as plt
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import random
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class FilterMenu:
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def __init__(self, pic_path):
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self.pic_path = pic_path
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self.pic = cv2.imread(self.pic_path, 1).astype(np.uint8)
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self.greypic = cv2.imread(self.pic_path, cv2.IMREAD_GRAYSCALE).astype(np.uint8)
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def ShowPic(self):
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cv2.imshow('当前图片', self.pic)
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cv2.waitKey(0) # 0一直显示，直到有键盘输入。也可以是其他数字.
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def GaussNoise(self, average, variance):
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# 将图片的像素值归一化，存入矩阵中
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image = np.array(self.pic / 255, dtype=float)
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# 生成正态分布的噪声，其中0表示均值，0.1表示方差
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noise = np.random.normal(average, variance, image.shape)
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# 将噪声叠加到图片上
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out = image + noise
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# 将图像的归一化像素值控制在0和1之间，防止噪声越界
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out = np.clip(out, 0.0, 1.0)
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# 将图像的像素值恢复到0到255之间
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out = np.uint8(out * 255)
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cv2.imwrite('img/filterImgs/FilterPic.jpg', out)
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cv2.waitKey(0)
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def PepperSaltyNoise(self, threshold):
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# 待输出的图片
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output = np.zeros(self.pic.shape, np.uint8)
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# 椒盐噪声的阈值
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prob = threshold
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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(self.pic.shape[0]):
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for j in range(self.pic.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] = self.pic[i][j]
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0)
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def gaussFilter(self, core):
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num = core
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grayImage = cv2.GaussianBlur(self.pic, (num, num), 0)
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cv2.imwrite('img/filterImgs/FilterPic.jpg', grayImage)
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cv2.waitKey(0)
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def AverageFilter(self):
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output = np.zeros(self.greypic.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(self.greypic.shape[0]):
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for j in range(self.greypic.shape[1]):
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# 滤波器内像素值的和
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sum = 0
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# 遍历滤波器内的像素值
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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 < self.greypic.shape[0] and 0 <= j + n < self.greypic.shape[1]:
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# 像素值求和
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sum += self.greypic[i + m][j + n]
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# 求均值，作为最终的像素值
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output[i][j] = int(sum / 9)
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0)
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# 获取列表的中间值的函数
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def get_middle(self, array):
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# 列表的长度
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length = len(array)
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# 对列表进行选择排序，获得有序的列表
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for i in range(length):
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for j in range(i + 1, length):
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# 选择最大的值
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if array[j] > array[i]:
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# 交换位置
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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[int(length / 2)]
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def middleFilter(self):
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output = np.zeros(self.greypic.shape, np.uint8)
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# 存储滤波器范围内的像素值
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self.array = []
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# 遍历图像，进行中值滤波
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for i in range(self.greypic.shape[0]):
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for j in range(self.greypic.shape[1]):
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# 清空滤波器内的像素值
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self.array.clear()
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# 遍历滤波器内的像素
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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 < self.greypic.shape[0] and 0 <= j + n < self.greypic.shape[1]:
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# 像素值加到列表中
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self.array.append(self.greypic[i + m][j + n])
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# 求中值，作为最终的像素值
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output[i][j] = self.get_middle(self.array)
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0)
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def LeastFilter(self):
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output = np.zeros(self.greypic.shape, np.uint8)
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######### Begin #########
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for i in range(self.greypic.shape[0]):
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for j in range(self.greypic.shape[1]):
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# 最小值滤波器
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minone = 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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if 0 <= i + m < self.greypic.shape[0] and 0 <= j + n < self.greypic.shape[1]:
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# 通过比较判断是否需要更新最小值
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if self.greypic[i + m][j + n] < minone:
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minone = self.greypic[i + m][j + n]
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# 更新最小值
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output[i][j] = minone
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0)
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def LargestFilter(self):
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output = np.zeros(self.greypic.shape, np.uint8)
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######### Begin #########
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for i in range(self.greypic.shape[0]):
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for j in range(self.greypic.shape[1]):
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# 最大值滤波器
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maxone = 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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if 0 <= i + m < self.greypic.shape[0] and 0 <= j + n < self.greypic.shape[1]:
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# 通过比较判断是否需要更新最大值
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if self.greypic[i + m][j + n] > maxone:
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maxone = self.greypic[i + m][j + n]
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# 更新最小值
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output[i][j] = maxone
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0)
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def PassFilter(self, rangemin, rangemax, filt_num):
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# 待输出的图片
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output = np.zeros(self.greypic.shape, np.uint8)
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# 遍历图像，进行均值滤波
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array = []
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# 带通的范围
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min = rangemin
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max = rangemax
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for i in range(self.greypic.shape[0]):
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for j in range(self.greypic.shape[1]):
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# 滤波器内像素值的和
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array.clear()
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if min < self.greypic[i][j] < max:
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output[i][j] = self.greypic[i][j]
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else:
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if filt_num == '0':
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output[i][j] = 0
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elif filt_num == '255':
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output[i][j] = 255
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0)
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def BlockFilter(self, rangemin, rangemax, filt_num):
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# 待输出的图片
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output = np.zeros(self.greypic.shape, np.uint8)
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# 遍历图像，进行均值滤波
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array = []
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# 带阻的范围
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min = rangemin
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max = rangemax
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for i in range(self.greypic.shape[0]):
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for j in range(self.greypic.shape[1]):
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# 滤波器内像素值的和
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array.clear()
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if min < self.greypic[i][j] < max:
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if filt_num == '0':
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output[i][j] = 0
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elif filt_num == '255':
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output[i][j] = 255
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else:
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output[i][j] = self.greypic[i][j]
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cv2.imwrite('img/filterImgs/FilterPic.jpg', output)
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cv2.waitKey(0) |
