mirror of
https://github.com/prise6/smart-iss-posts
synced 2024-04-25 18:50:28 +02:00
221 lines
8.2 KiB
Python
221 lines
8.2 KiB
Python
import os
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import pandas as pd
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from bokeh.plotting import figure, output_file, show
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from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper
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from iss.init_config import CONFIG
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from iss.tools import Tools
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from iss.models import SimpleConvAutoEncoder, SimpleAutoEncoder
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from iss.clustering import ClassicalClustering, AdvancedClustering, N2DClustering, DBScanClustering
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_DEBUG = True
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def run_clustering(config, clustering_type, pictures_id, intermediate_output):
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"""
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Apply clustering on images
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"""
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if clustering_type == 'classical':
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if _DEBUG:
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print("Classical Clustering")
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clustering = ClassicalClustering(config.get('clustering')['classical'], pictures_id, intermediate_output)
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clustering.compute_pca()
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clustering.compute_kmeans()
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clustering.compute_kmeans_centers()
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clustering.compute_cah()
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clustering.compute_final_labels()
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clustering.compute_tsne()
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clustering.compute_colors()
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elif clustering_type == 'advanced':
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if _DEBUG:
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print("Advanced Clustering")
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clustering = AdvancedClustering(config.get('clustering')['classical'], pictures_id, intermediate_output)
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elif clustering_type == 'n2d':
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if _DEBUG:
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print("Not2Deep Clustering")
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clustering = N2DClustering(config.get('clustering')['n2d'], pictures_id, intermediate_output)
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clustering.compute_umap()
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clustering.compute_kmeans()
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clustering.compute_final_labels()
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clustering.compute_colors()
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elif clustering_type == 'dbscan':
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if _DEBUG:
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print("HDBSCAN Clustering")
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clustering = DBScanClustering(config.get('clustering')['dbscan'], pictures_id, intermediate_output)
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clustering.compute_umap()
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clustering.compute_dbscan()
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clustering.compute_final_labels()
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clustering.compute_colors()
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return clustering
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def run_plots(config, clustering_type, clustering):
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"""
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Plots specifics graphs
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"""
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if clustering_type in ['classical']:
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## Graphs of PCA and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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scatter = ax.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(clustering.save_directory, 'pca_clusters.png'))
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if clustering_type in ['classical']:
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## Graphs of TSNE and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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classes = clustering.final_labels
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scatter = ax.scatter(clustering.tsne_embedding[:, 0], clustering.tsne_embedding[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(clustering.save_directory, 'tsne_clusters.png'))
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if clustering_type in ['n2d', 'dbscan']:
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## Graphs of TSNE and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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classes = clustering.final_labels
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scatter = ax.scatter(clustering.umap_embedding[:, 0], clustering.umap_embedding[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(clustering.save_directory, 'umap_clusters.png'))
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if clustering_type in ['n2d', 'classical', 'dbscan']:
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filenames = [os.path.join(config.get('directory')['collections'], "%s.jpg" % one_res[0]) for one_res in clustering.get_results()]
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images_array = [Tools.read_np_picture(img_filename, target_size = (54, 96)) for img_filename in filenames]
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base64_images = [Tools.base64_image(img) for img in images_array]
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if clustering_type in ['n2d', 'dbscan']:
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x = clustering.umap_embedding[:, 0]
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y = clustering.umap_embedding[:, 1]
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html_file = 'umap_bokeh.html'
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title = 'UMAP projection of iss clusters'
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elif clustering_type == 'classical':
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x = clustering.tsne_embedding[:, 0]
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y = clustering.tsne_embedding[:, 1]
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html_file = 'tsne_bokeh.html'
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title = 't-SNE projection of iss clusters'
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df = pd.DataFrame({'x': x, 'y': y})
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df['image'] = base64_images
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df['label'] = clustering.final_labels.astype(str)
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df['color'] = df['label'].apply(Tools.get_color_from_label)
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datasource = ColumnDataSource(df)
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output_file(os.path.join(clustering.save_directory, html_file))
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plot_figure = figure(
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title=title,
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# plot_width=1200,
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# plot_height=1200,
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tools=('pan, wheel_zoom, reset')
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)
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plot_figure.add_tools(HoverTool(tooltips="""
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<div>
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<div>
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<img src='@image' style='float: left; margin: 5px 5px 5px 5px'/>
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</div>
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<div>
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<span style='font-size: 16px'>Cluster:</span>
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<span style='font-size: 18px'>@label</span>
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</div>
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</div>
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"""))
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plot_figure.circle(
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'x',
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'y',
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source=datasource,
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color=dict(field='color'),
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line_alpha=0.6,
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fill_alpha=0.6,
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size=4
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)
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show(plot_figure)
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if clustering_type in ['classical']:
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## Dendogram
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fig, ax = plt.subplots(figsize=(24, 14))
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plt.title('Hierarchical Clustering Dendrogram')
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Tools.plot_dendrogram(clustering.cah_fit, labels=clustering.cah_labels)
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plt.savefig(os.path.join(clustering.save_directory, 'dendograms.png'))
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return True
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def plot_silhouette(config, clustering_type, clustering):
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silhouettes = clustering.compute_silhouette_score()
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fig, ax = plt.subplots(figsize=(12, 7))
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ax.bar(silhouettes.keys(), silhouettes.values(), align='center')
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ax.set_xticks(list(silhouettes.keys()))
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ax.set_xticklabels(list(silhouettes.keys()))
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plt.savefig(os.path.join(clustering.save_directory, 'silhouettes_score.png'))
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return silhouettes
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def plot_mosaics(config, clustering_type, clustering, output_image_width, output_image_height, mosaic_nrow, mosaic_ncol_max):
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"""
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Mosaic of each cluster
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"""
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clusters_id = np.unique(clustering.final_labels)
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clustering_res = clustering.get_results()
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for cluster_id in clusters_id:
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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]
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images_array = [Tools.read_np_picture(img_filename, target_size = (output_image_height, output_image_width)) for img_filename in cluster_image_filenames]
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img = Tools.display_mosaic(images_array, nrow = mosaic_nrow, ncol_max = mosaic_ncol_max)
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img.save(os.path.join(clustering.save_directory, "cluster_%s.png" % str(cluster_id).zfill(2)), "PNG")
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return clusters_id
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def main():
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_CLUSTERING_TYPE = 'n2d'
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_BATCH_SIZE = 496
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_N_BATCH = 5
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_PLOTS = True
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_MOSAICS = True
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_SILHOUETTE = True
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_OUTPUT_IMAGE_WIDTH = 96
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_OUTPUT_IMAGE_HEIGHT = 54
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_MOSAIC_NROW = 10
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_MOSAIC_NCOL_MAX = 10
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model, model_config = Tools.load_model(CONFIG, CONFIG.get('clustering')[_CLUSTERING_TYPE]['model']['type'], CONFIG.get('clustering')[_CLUSTERING_TYPE]['model']['name'])
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filenames = Tools.list_directory_filenames(CONFIG.get('sampling')['autoencoder']['directory']['train'])
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pictures_id, intermediate_output = Tools.load_latent_representation(CONFIG, model, model_config, filenames, _BATCH_SIZE, _N_BATCH, False)
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clustering = run_clustering(CONFIG, _CLUSTERING_TYPE, pictures_id, intermediate_output)
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clustering.save()
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if _PLOTS:
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run_plots(CONFIG, _CLUSTERING_TYPE, clustering)
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if _SILHOUETTE:
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plot_silhouette(CONFIG, _CLUSTERING_TYPE, clustering)
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if _MOSAICS:
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plot_mosaics(CONFIG, _CLUSTERING_TYPE, clustering, _OUTPUT_IMAGE_WIDTH, _OUTPUT_IMAGE_HEIGHT, _MOSAIC_NROW, _MOSAIC_NCOL_MAX)
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if __name__ == '__main__':
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main()
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