1.3.6

clustimage/clustimage.py

@@ -1901,7 +1901,7 @@ def store_to_disk(Xraw, dim, tempdir):
         pathname = os.path.join(tempdir, filename)
         # Write to disk
         img = imscale(Xraw[i,:].reshape(dim))
-        cv2.imwrite(pathname, Xraw[i,:].reshape(dim))
+        cv2.imwrite(pathname, img)
         filenames.append(filename)
         pathnames.append(pathname)
     return pathnames, filenames

clustimage/examples.py

@@ -5,43 +5,6 @@
 # 
 # Read image according the preprocessing steps
 
-# %%
-from clustimage import Clustimage
-
-# init with PCA method
-cl = Clustimage(method='pca',
-                embedding='tsne',
-                grayscale=False,
-                dim=(128,128),
-                params_pca={'n_components':0.95},
-                store_to_disk=False)
-
-# Load MNIST example data
-X = cl.import_example(data='mnist')
-
-# There are 1797 digits in total, each with 64 features which is the flattened 8x8 grayscale image.
-# print(X.shape)
-# (1797, 64)
-
-# print(X)
-# array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
-#        [ 0.,  0.,  0., ..., 10.,  0.,  0.],
-#        [ 0.,  0.,  0., ..., 16.,  9.,  0.],
-#        ...,
-#        [ 0.,  0.,  1., ...,  6.,  0.,  0.],
-#        [ 0.,  0.,  2., ..., 12.,  0.,  0.],
-#        [ 0.,  0., 10., ..., 12.,  1.,  0.]])
-
-# Preprocessing, feature extraction, embedding and cluster evaluation
-results = cl.fit_transform(X,
-                           cluster='agglomerative',
-                           evaluate='silhouette',
-                           metric='euclidean',
-                           linkage='ward',
-                           min_clust=3,
-                           max_clust=25,
-                           cluster_space='high')
-
 # %% HASHES
 import matplotlib.pyplot as plt
 from clustimage import Clustimage