9 examples of 'add gaussian noise to image python' in Python

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176def __call__(self, image):
177 if isinstance(self.sigma, collections.Sequence):
178 sigma = random_num_generator(
179 self.sigma, random_state=self.random_state)
180 else:
181 sigma = self.sigma
182 if isinstance(self.mean, collections.Sequence, random_state=self.random_state):
183 mean = random_num_generator(self.mean)
184 else:
185 mean = self.mean
186 row, col, ch = image.shape
187 gauss = self.random_state.normal(mean, sigma, (row, col, ch))
188 gauss = gauss.reshape(row, col, ch)
189 image += gauss
190 return image
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26def addGaussianNoise(src):
27 row,col,ch= src.shape
28 mean = 0
29 var = 0.1
30 sigma = 15
31 gauss = np.random.normal(mean,sigma,(row,col,ch))
32 gauss = gauss.reshape(row,col,ch)
33 noisy = src + gauss
34
35 return noisy
305def noiseImage(n1,n2):
306 r = Random(3)
307 x = sub(randfloat(r,n1,n2),0.5)
308 rgf = RecursiveGaussianFilter(2.0)
309 for x2 in x:
310 rgf.apply1(x2,x2)
311 return x
176def AddGauss(img, level):
177 return cv2.blur(img, (level * 2 + 1, level * 2 + 1));
29def compute_scaled_noise(self, noise, background_noise):
30 """Compute a scaled noise map from the baseline noise map. This scales each galaxy component individually \
31 using their galaxy contribution map and sums their scaled noise maps with the baseline and background noise maps.
32
33 Parameters
34 -----------
35 noise : ndarray
36 The noise before scaling (electrons per second)..
37 background_noise : ndarray
38 The background noise values (electrons per second)..
39 """
40 return noise + (self.background_noise_scale * background_noise)
138def _add_noise(self, img):
139 """Adds Gaussian or Poisson noise to image."""
140
141 w, h = img.size
142 c = len(img.getbands())
143
144 # Poisson distribution
145 if self.noise_type == 'poisson':
146 noise = np.random.poisson(self.noise_param, (h, w, c))
147
148 # Normal distribution (default)
149 else:
150 std = np.random.uniform(0, self.noise_param)
151 noise = np.random.normal(0, std, (h, w, c))
152
153 # Add noise and clip
154 noise_img = np.array(img) + noise
155 noise_img = np.clip(noise_img, 0, 255).astype(np.uint8)
156
157 return Image.fromarray(noise_img)
87def rand_noise(img):
88 img_noisy = img + np.random.normal(scale=noise, size=img.shape)
89 return img_noisy
85def remove_background_gauss(img, min_sigma=3, max_sigma=30, threshold=1):
86 """Remove background from an image using a difference of gaussian approach
87
88 img: ndarray
89 Image array
90 min_sigma: float, optional
91 The minimum standard deviation for the gaussian filter
92 max_sigma: float, optional
93 The maximum standard deviation for the gaussian filter
94 threshold: float, optional
95 Remove any remaining features below this threshold
96
97 Returns img: ndarray
98 Image array with background removed
99 """
100 img_float = img.astype(float)
101 img_corr = np.maximum(ndimage.gaussian_filter(img_float, min_sigma) - ndimage.gaussian_filter(img_float, max_sigma) - threshold, 0)
102 return img_corr.astype(int)
86def filter(image):
87 image_ = clean_image.convert_to_greyscale(image)
88 image_ = clean_image.clean_noise(image_, 3)
89 return image_

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