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import sys
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import random
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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def addNoisy(img_path, type, para1, para2):
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img = cv2.imread(img_path) # 读取图片
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out = np.zeros(img.shape, np.uint8)
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if type == 1:
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# 添加高斯噪声-------------------------------------------------------
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# 将图片的像素值归一化，存入矩阵中
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img = np.array(img / 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(para1, para2, img.shape) # 参数：正态分布的均值和方差，可自定义
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# 将噪声叠加到图片上
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out = img + 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('./output/Gaussian_noisy.jpg', out)
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elif type == 2:
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# 添加椒盐噪声--------------------------------------------------------
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# 遍历图像，获取叠加噪声后的图像
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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rdn = random.random()
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if rdn < para1:
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# 添加椒噪声
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out[i][j] = 0
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elif rdn > para2:
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# 添加盐噪声
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out[i][j] = 255
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else:
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# 不添加噪声
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out[i][j] = img[i][j]
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cv2.imwrite('./output/saltnoisy.jpg', out)
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'''
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输入：img_path:图片地址
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type：噪声类型(1为高斯噪声，2为椒盐噪声）
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para1,para2：如果添加的是高斯噪声，para1为正态分布均值，para2为正态分布方差
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如果添加的是椒盐噪声，para1为添加椒噪声阈值，para2为添加盐噪声阈值
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输出：添加了噪声的图片
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'''
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if __name__ == '__main__':
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addNoisy(sys.argv[1], eval(sys.argv[2]), eval(sys.argv[3]), eval(sys.argv[4]))
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