Add of svd_reconstruct metric

modules/utils/config.py

@@ -35,7 +35,7 @@ cycle_scenes_indices            = ['E', 'I']
 normalization_choices           = ['svd', 'svdn', 'svdne']
 zones_indices                   = np.arange(16)
 
-metric_choices_labels           = ['lab', 'mscn', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2', 'sub_blocks_stats', 'sub_blocks_area', 'sub_blocks_stats_reduced', 'sub_blocks_area_normed', 'mscn_var_4', 'mscn_var_16', 'mscn_var_64', 'mscn_var_16_max', 'mscn_var_64_max', 'ica_diff', 'svd_trunc_diff', 'ipca_diff']
+metric_choices_labels           = ['lab', 'mscn', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2', 'sub_blocks_stats', 'sub_blocks_area', 'sub_blocks_stats_reduced', 'sub_blocks_area_normed', 'mscn_var_4', 'mscn_var_16', 'mscn_var_64', 'mscn_var_16_max', 'mscn_var_64_max', 'ica_diff', 'svd_trunc_diff', 'ipca_diff', 'svd_reconstruct']
 
 keras_epochs                    = 500
 keras_batch                     = 32

modules/utils/data.py

@@ -6,6 +6,7 @@ from skimage import color
 from sklearn.decomposition import FastICA
 from sklearn.decomposition import IncrementalPCA
 from sklearn.decomposition import TruncatedSVD
+from numpy.linalg import svd as lin_svd
 
 import numpy as np
 
@@ -248,6 +249,24 @@ def get_svd_data(data_type, block):
         U, s, V = metrics.get_SVD(reduced_image)
         data = s
 
+    if data_type == 'svd_reconstruct':
+
+        reconstructed_interval = (90, 200)
+        begin, end = reconstructed_interval
+
+        lab_img = metrics.get_LAB_L(block)
+        lab_img = np.array(lab_img, 'uint8')
+
+        U, s, V = lin_svd(lab_img, full_matrices=True)
+
+        smat = np.zeros((end-begin, end-begin), dtype=complex)
+        smat[:, :] = np.diag(s[begin:end])
+        output_img = np.dot(U[:, begin:end],  np.dot(smat, V[begin:end, :]))
+
+        output_img = np.array(output_img, 'uint8')
+
+        data = metrics.get_SVD_s(output_img)
+
     return data
 
 def _get_mscn_variance(block, sub_block_size=(50, 50)):