Nataniel Ruiz Gutierrez
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import numpy as np
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import os
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import cv2
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from PIL import Image
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def PIL_to_cv2(image):
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image = np.array(image)
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image = image[:,:,::-1].copy()
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return image
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def cv2_to_PIL(image):
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image = image[:,:,::-1].copy().astype(np.uint8)
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image = Image.fromarray(image)
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return image
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def noisy(noise_typ, image):
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image = PIL_to_cv2(image)
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if noise_typ == "gauss":
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row,col,ch= image.shape
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mean = 0
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var = 0.2
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sigma = var**0.5
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gauss = np.random.normal(mean,sigma,(row,col,ch))
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gauss = gauss.reshape(row,col,ch)
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noisy = image + gauss
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return cv2_to_PIL(noisy)
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elif noise_typ == "s&p":
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row,col,ch = image.shape
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s_vs_p = 0.5
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amount = 0.004
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out = np.copy(image)
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# Salt mode
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num_salt = np.ceil(amount * image.size * s_vs_p)
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coords = [np.random.randint(0, i - 1, int(num_salt))
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for i in image.shape]
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out[coords] = 1
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# Pepper mode
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num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
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coords = [np.random.randint(0, i - 1, int(num_pepper))
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for i in image.shape]
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out[coords] = 0
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return cv2_to_PIL(out)
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elif noise_typ == "poisson":
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vals = len(np.unique(image))
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vals = 2 ** np.ceil(np.log2(vals))
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noisy = np.random.poisson(image * vals) / float(vals)
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return cv2_to_PIL(noisy)
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elif noise_typ =="speckle":
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row,col,ch = image.shape
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gauss = np.random.randn(row,col,ch)
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gauss = gauss.reshape(row,col,ch)
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noisy = image + image * gauss
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return cv2_to_PIL(noisy) |