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@@ -8,11 +8,12 @@ from ipfml.processing import reconstruction
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from ipfml.filters import convolution, kernels
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from ipfml import utils
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import cv2
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+from skimage.restoration import denoise_nl_means, estimate_sigma
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from PIL import Image
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# modules imports
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-from ..utils import data as functions
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+#from ..utils import data as functions
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# Transformation class to store transformation method of image and get usefull information
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@@ -85,6 +86,32 @@ class Transformation():
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sobel_reconstructed.thumbnail((h, w))
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data = np.array(sobel_reconstructed)
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+
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+ if self.transformation == 'nl_mean_noise_mask':
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+ patch_size, patch_distance = list(map(int, self.param.split(',')))
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+ h, w = list(map(int, self.size.split(',')))
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+
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+ img = np.array(img)
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+ sigma_est = np.mean(estimate_sigma(img, multichannel=True))
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+
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+ patch_kw = dict(patch_size=patch_size, # 5x5 patches
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+ patch_distance=patch_distance, # 13x13 search area
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+ multichannel=True)
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+
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+ # slow algorithm
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+ denoise = denoise_nl_means(img, h=0.8 * sigma_est, sigma=sigma_est,
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+ fast_mode=False,
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+ **patch_kw)
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+
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+ denoise = np.array(denoise, 'uint8')
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+ noise_mask = np.abs(denoise - img)
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+
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+ data_array = np.array(noise_mask, 'uint8')
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+
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+ img_array = Image.fromarray(data_array)
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+ img_array.thumbnail((h, w))
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+
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+ data = np.array(img_array)
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if self.transformation == 'static':
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# static content, we keep input as it is
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@@ -121,6 +148,11 @@ class Transformation():
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h, w = list(map(int, self.size.split(',')))
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path = os.path.join(path, 'K_' + str(k_size)) + '_L' + str(p_limit) + '_S_' + str(w) + '_' + str(h)
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+ if self.transformation == 'nl_mean_noise_mask':
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+ patch_size, patch_distance = list(map(int, self.param.split(',')))
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+ h, w = list(map(int, self.size.split(',')))
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+ path = os.path.join(path, 'S' + str(patch_size)) + '_D' + str(patch_distance) + '_S_' + str(w) + '_' + str(h)
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+
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if self.transformation == 'static':
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# param contains image name to find for each scene
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path = self.param
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