np.random.normal(0, 1, size=2).clip(-1, 1)
时间: 2023-10-10 08:12:17 浏览: 39
This code generates two random numbers from a normal distribution with mean 0 and standard deviation 1, and then clips them to be within the range of -1 to 1.
The numpy.random.normal() function generates random numbers from a normal distribution with the specified mean and standard deviation. The size parameter specifies the number of random numbers to generate, in this case 2.
The clip() method is then used to limit the values to be within -1 and 1. Any values outside of this range will be set to the closest boundary value.
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def add_gaussian_noise(image, percent, mean=0, var=0.04): # 将图像转为浮点型 image = np.array(image / 255, dtype=float) # 计算噪声数量 num = int(percent * image.size) # 生成坐标和噪声 coords = np.random.randint(0, image.size, num) noise = np.random.normal(mean, var ** 0.5, num) # 添加噪声 image.flat[coords] += noise return image percent = 0.01 # 噪声比例 out = add_gaussian_noise(grayImage, percent) if out.min() < 0: low_clip = -1. else: low_clip = 0. out = np.clip ( out , low_clip ,1) gasuss_image = np.uint8(out *255)简化以上代码
以下是简化后的代码:
```
import numpy as np
def add_gaussian_noise(image, percent, mean=0, var=0.04):
image = image / 255.0
num = int(percent * image.size)
coords = np.random.randint(0, image.size, num)
noise = np.random.normal(mean, var ** 0.5, num)
image_flat = image.flatten()
image_flat[coords] += noise
image = np.clip(image_flat, 0, 1)
return np.uint8(image * 255)
percent = 0.01
out = add_gaussian_noise(grayImage, percent)
gasuss_image = out.astype(np.uint8)
```
这段代码的作用是给灰度图像添加高斯噪声。首先将图像转为浮点型,然后根据噪声比例计算需要添加的噪声数量。随机生成噪声坐标和噪声值,然后将噪声添加到图像中。最后将图像像素值限制在0到1之间,并将其转换为8位整数型。
def add_gaussian_noise(image, percent, mean=0, var=0.04): # 将图像转为浮点型 image = np.array(image / 255, dtype=float) # 计算噪声数量 num = int(percent * image.size) # 生成坐标和噪声 coords = np.random.randint(0, image.size, num) noise = np.random.normal(mean, var ** 0.5, num) # 添加噪声 image.flat[coords] += noise return image % = 0.01 # 噪声比例 out = add_gaussian_noise(grayImage, percent) if out.min() < 0: low_clip = -1.否则:low_clip = 0。out = np.clip ( out , low_clip ,1) gasuss_image = np.uint8(out *255)简化以上代码
以下是简化后的代码:
```
import numpy as np
def add_gaussian_noise(image, percent, mean=0, var=0.04):
# 将图像转为浮点型
image = image / 255.0
# 计算噪声数量
num = int(percent * image.size)
# 生成坐标和噪声
coords = np.random.randint(0, image.size, num)
noise = np.random.normal(mean, var ** 0.5, num)
# 添加噪声
image.flat[coords] += noise
image = np.clip(image, 0, 1)
# 噪声比例
out = image * percent
gasuss_image = np.uint8(out * 255)
return gasuss_image
```
这个函数的作用是给灰度图像添加高斯噪声。其中,`image` 是一个灰度图像,`percent` 是噪声比例,`mean` 和 `var` 分别是高斯分布的均值和方差。函数首先将图像转为浮点型,并计算噪声数量。然后,随机生成噪声的坐标和噪声值,并将噪声添加到图像中。最后,将图像像素值限制在 0 到 1 之间,并将噪声比例乘以图像像素值,得到最终的带有噪声的图像。