import random

import cv2
import numpy as np


class noiseCanceller:
    def __init__(self, imgPath):
        self.path = imgPath

    def noiseDescriber(self):
        image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
        output = np.zeros(image.shape, np.uint8)
        prob = 0.2
        thres = 1 - prob
        # 遍历图像，获取叠加噪声后的图像
        for i in range(image.shape[0]):
            for j in range(image.shape[1]):
                rdn = random.random()
                if rdn < prob:
                    # 添加胡椒噪声
                    output[i][j] = 0
                elif rdn > thres:
                    # 添加食盐噪声
                    output[i][j] = 255
                else:
                    # 不添加噪声
                    output[i][j] = image[i][j]
        cv2.imwrite("saved Img/out.bmp", output)

    def meanFilter(self):
        image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
        output = np.zeros(image.shape, np.uint8)
        for i in range(image.shape[0]):
            for j in range(image.shape[1]):
                ij = 1
                for m in range(-1, 2):
                    if 0 <= j + m < image.shape[1]:
                        ij *= image[i][j + m]
                        output[i][j] = ij ** (1 / 3)
        cv2.imwrite("saved Img/mean filter.bmp", output)

    def sortFilter(self):
        def get_max(array):
            length = len(array)
            for i in range(length):
                for j in range(i + 1, length):
                    if array[j] > array[i]:
                        temp = array[j]
                        array[j] = array[i]
                        array[i] = temp
            return array[0]

        image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
        output = np.zeros(image.shape, np.uint8)
        array = []
        for i in range(image.shape[0]):
            for j in range(image.shape[1]):
                array.clear()
                for m in range(-1, 2):
                    for n in range(-1, 2):
                        if 0 <= i + m < image.shape[0] and 0 <= j + n < image.shape[1]:
                            array.append(image[i + m][j + n])

                output[i][j] = get_max(array)
        cv2.imwrite("saved Img/sort filter.bmp", output)

    def selectiveFilter(self):
        image = cv2.imread(self.path, cv2.IMREAD_GRAYSCALE)
        output = np.zeros(image.shape, np.uint8)
        minimum = 200
        for i in range(image.shape[0]):
            for j in range(image.shape[1]):
                if minimum < image[i][j]:
                    output[i][j] = image[i][j]
                else:
                    output[i][j] = 0
        cv2.imwrite("saved Img/selective filter.bmp", output)