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在matlab中,存在執(zhí)行直接得函數(shù)來(lái)添加高斯噪聲和椒鹽噪聲。Python-OpenCV中雖然不存在直接得函數(shù),但是很容易使用相關(guān)的函數(shù)來(lái)實(shí)現(xiàn)。
代碼:
import numpy as np import random import cv2 def sp_noise(image,prob): ''' 添加椒鹽噪聲 prob:噪聲比例 ''' 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 def gasuss_noise(image, mean=0, var=0.001): ''' 添加高斯噪聲 mean : 均值 var : 方差 ''' image = np.array(image/255, dtype=float) noise = np.random.normal(mean, var ** 0.5, image.shape) out = image + noise if out.min() < 0: low_clip = -1. else: low_clip = 0. out = np.clip(out, low_clip, 1.0) out = np.uint8(out*255) #cv.imshow("gasuss", out) return out
可見,只要我們得到滿足某個(gè)分布的多維數(shù)組,就能作為噪聲添加到圖片中。
例如:
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)
然后再:
img = ... noise = ... image = img + noise
參考鏈接:
1、https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv#
2、https://stackoverflow.com/questions/14435632/impulse-gaussian-and-salt-and-pepper-noise-with-opencv#
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