如何使用 OpenCV 在 Python 中向图像添加噪声（高斯/盐和胡椒等）
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How to add noise (Gaussian/salt and pepper etc) to image in Python with OpenCV
提问by Sanchit
I am wondering if there exists some functions in Python with OpenCV or any other python image processing library that adds Gaussian or salt and pepper noise to an image? For example, in MATLAB there exists straight-forward functions that do the same job.
我想知道 Python 中是否存在一些带有 OpenCV 的函数或任何其他向图像添加高斯或椒盐噪声的 Python 图像处理库？例如，在 MATLAB 中存在执行相同工作的直接函数。
Or, how to add noise to an image using Python with OpenCV?
或者，如何使用 Python 和 OpenCV 向图像添加噪声？
采纳答案by Shubham Pachori
The Function adds gaussian , salt-pepper , poisson and speckle noise in an image
该函数在图像中添加高斯、椒盐、泊松和斑点噪声
Parameters
----------
image : ndarray
Input image data. Will be converted to float.
mode : str
One of the following strings, selecting the type of noise to add:
'gauss' Gaussian-distributed additive noise.
'poisson' Poisson-distributed noise generated from the data.
's&p' Replaces random pixels with 0 or 1.
'speckle' Multiplicative noise using out = image + n*image,where
n is uniform noise with specified mean & variance.
import numpy as np
import os
import cv2
def noisy(noise_typ,image):
if noise_typ == "gauss":
row,col,ch= image.shape
mean = 0
var = 0.1
sigma = var**0.5
gauss = np.random.normal(mean,sigma,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
noisy = image + gauss
return noisy
elif noise_typ == "s&p":
row,col,ch = image.shape
s_vs_p = 0.5
amount = 0.004
out = np.copy(image)
# Salt mode
num_salt = np.ceil(amount * image.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt))
for i in image.shape]
out[coords] = 1
# Pepper mode
num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
for i in image.shape]
out[coords] = 0
return out
elif noise_typ == "poisson":
vals = len(np.unique(image))
vals = 2 ** np.ceil(np.log2(vals))
noisy = np.random.poisson(image * vals) / float(vals)
return noisy
elif noise_typ =="speckle":
row,col,ch = image.shape
gauss = np.random.randn(row,col,ch)
gauss = gauss.reshape(row,col,ch)
noisy = image + image * gauss
return noisy
回答by ppk28
I don't know is there any method in Python API.But you can use this simple code to add Salt-and-Pepper noise to an image.
我不知道 Python API 中是否有任何方法。但是您可以使用这个简单的代码将椒盐噪声添加到图像中。
import numpy as np
import random
import cv2
def sp_noise(image,prob):
'''
Add salt and pepper noise to image
prob: Probability of the noise
'''
output = np.zeros(image.shape,np.uint8)
thres = 1 - prob
for i in range(image.shape[0]):
for j in range(image.shape[1]):
rdn = random.random()
if rdn < prob:
output[i][j] = 0
elif rdn > thres:
output[i][j] = 255
else:
output[i][j] = image[i][j]
return output
image = cv2.imread('image.jpg',0) # Only for grayscale image
noise_img = sp_noise(image,0.05)
cv2.imwrite('sp_noise.jpg', noise_img)
回答by berak
just look at cv2.randu()or cv.randn(), it's all pretty similar to matlab already, i guess.
看看cv2.randu()或 cv.randn()，我猜它已经和 matlab 非常相似了。
let's play a bit ;) :
让我们玩一下;) :
import cv2
import numpy as np
>>> im = np.empty((5,5), np.uint8) # needs preallocated input image
>>> im
array([[248, 168, 58, 2, 1], # uninitialized memory counts as random, too ? fun ;)
[ 0, 100, 2, 0, 101],
[ 0, 0, 106, 2, 0],
[131, 2, 0, 90, 3],
[ 0, 100, 1, 0, 83]], dtype=uint8)
>>> im = np.zeros((5,5), np.uint8) # seriously now.
>>> im
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
>>> cv2.randn(im,(0),(99)) # normal
array([[ 0, 76, 0, 129, 0],
[ 0, 0, 0, 188, 27],
[ 0, 152, 0, 0, 0],
[ 0, 0, 134, 79, 0],
[ 0, 181, 36, 128, 0]], dtype=uint8)
>>> cv2.randu(im,(0),(99)) # uniform
array([[19, 53, 2, 86, 82],
[86, 73, 40, 64, 78],
[34, 20, 62, 80, 7],
[24, 92, 37, 60, 72],
[40, 12, 27, 33, 18]], dtype=uint8)
to apply it to an existing image, just generate noise in the desired range, and add it:
要将其应用于现有图像，只需在所需范围内生成噪声，然后添加它：
img = ...
noise = ...
image = img + noise
