def plot_mosaics(config, clustering_type, clustering, output_image_width,
output_image_height, mosaic_nrow, mosaic_ncol_max):
"""
Mosaic of each cluster
"""
clusters_id = np.unique(clustering.final_labels)
clustering_res = clustering.get_results()
for cluster_id in clusters_id:
cluster_image_filenames = [
os.path.join(
config.get('directory')['collections'], "%s.jpg" % one_res[0])
for one_res in clustering_res if one_res[1] == cluster_id
]
images_array = [
Tools.read_np_picture(img_filename,
target_size=(output_image_height,
output_image_width))
for img_filename in cluster_image_filenames
]
img = Tools.display_mosaic(images_array,
nrow=mosaic_nrow,
ncol_max=mosaic_ncol_max)
img.save(
os.path.join(clustering.save_directory,
"cluster_%s.png" % str(cluster_id).zfill(2)), "PNG")
return clusters_id
res_tmp = select_cluster(clustering, cl)
print(len(res_tmp))
image_array = [Tools.read_np_picture(f, target_size = (54, 96)) for f in res_tmp[:100]]
# img = Tools.display_mosaic(image_array, nrow = 10)
# fig = plt.figure(1, figsize=(12, 7))
# plt.imshow(img, aspect = 'auto')
# plt.show()
#%% [markdown]
# ## Zoom sur le cluster 0
#%%
res_tmp = select_cluster(clustering, 1)
#%%
print(len(res_tmp))
image_array = [Tools.read_np_picture(f, target_size = (54, 96)) for f in res_tmp]
#%%
Tools.display_mosaic(image_array, nrow = 18)
#%%
col = [1 if l == 1 else 0 for l in clustering.kmeans_labels]
plt.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = col)
#%%
plt.scatter(clustering.pca_reduction[np.array(col) == 1, 0], clustering.pca_reduction[np.array(col) == 1, 1])
#%%[markdown]
# # Graphiques
#%%
def select_cluster(clustering, id_cluster):
return [os.path.join('data/processed/models/autoencoder/train/k/', res[0] + '.jpg') for res in clustering.get_zip_results() if res[2] == id_cluster]
#%%
for cl in np.unique(clustering.kmeans_labels):
print("Cluster %s" % (cl))
res_tmp = select_cluster(clustering, cl)
if len(res_tmp) >= 0:
print(len(res_tmp))
image_array = [Tools.read_np_picture(f, target_size = (54, 96)) for f in res_tmp[:100]]
img = Tools.display_mosaic(image_array, nrow = 10)
fig = plt.figure(1, figsize=(12, 7))
plt.imshow(img, aspect = 'auto')
plt.show()
#%% [markdown]
# ## faut essayer de faire des paquets
#%%
from sklearn.manifold import TSNE
output_tnse = TSNE(n_components=2).fit_transform(clustering.pca_reduction)
#%%
plt.scatter(