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@@ -1,5 +1,3 @@
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-
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-
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from numpy.linalg import svd
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from PIL import Image
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import matplotlib.pyplot as plt
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@@ -9,15 +7,11 @@ import time
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import numpy as np
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from sklearn import preprocessing
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+import ipfml as iml
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-import modules.model_helper.image_conversion as img_c
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-
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-'''
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-Method which extracts SVD features from image and returns 's' vector
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-@return 's' vector
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-'''
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def get_s_model_data(image):
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- U, s, V = svd(image, full_matrices=False)
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+
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+ s = iml.metrics.get_SVD_s(image)
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size = len(s)
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@@ -27,13 +21,13 @@ def get_s_model_data(image):
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return result
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-def get_s_model_data_img(image):
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+def get_s_model_data_img(image, ):
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fig_size = plt.rcParams["figure.figsize"]
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fig_size[0] = 1
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fig_size[1] = 1
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plt.rcParams["figure.figsize"] = fig_size
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- U, s, V = svd(image, full_matrices=False)
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+ s = iml.metrics.get_SVD_s(image)
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plt.figure()
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@@ -42,23 +36,8 @@ def get_s_model_data_img(image):
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plt.plot(output_normalized[70:100:, 1])
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plt.plot(output_normalized[70:100:, 2])
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- img = img_c.fig2img(plt.gcf())
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+ img = iml.image_processing.fig2img(plt.gcf())
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plt.close('all')
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- return img
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-
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-def get(image):
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- return svd(image, full_matrices=False)
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-
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-def get_s(image):
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- U, s, V = svd(image, full_matrices=False)
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- return s
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-
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-def get_U(image):
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- U, s, V = svd(image, full_matrices=False)
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- return U
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-
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-def get_V(image):
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- U, s, V = svd(image, full_matrices=False)
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- return V
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+ return img
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