Francois Vieille
2 years ago
7 changed files with 348 additions and 13 deletions
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import os |
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import base64 |
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import pandas as pd |
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import numpy as np |
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from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator |
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from iss.init_config import CONFIG |
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from iss.tools import Tools |
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SPRITE_NB_LIGNE = 145 |
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SPRITE_NB_COLONNE = 100 |
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TARGET_SIZE_WIDTH = 48*2 |
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TARGET_SIZE_HEIGHT = 27*2 |
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LIMIT = 14499 |
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def request_data(config, db_manager): |
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sql = """ |
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SELECT |
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v1.pictures_id, |
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v1.pictures_x as v1_x, |
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v1.pictures_y as v1_y, |
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CAST(v1.label AS CHAR) as v1_label, |
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v2.pictures_x as v2_x, |
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v2.pictures_y as v2_y, |
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CAST(v2.label AS CHAR) as v2_label, |
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v3.pictures_x as v3_x, |
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v3.pictures_y as v3_y, |
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CAST(v3.label AS CHAR) as v3_label, |
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loc.pictures_timestamp, |
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loc.pictures_location_text, |
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loc.pictures_latitude, |
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loc.pictures_longitude |
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FROM iss.pictures_embedding AS v1 |
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INNER JOIN iss.pictures_embedding v2 |
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ON v1.pictures_id = v2.pictures_id |
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AND v2.clustering_type = v1.clustering_type |
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AND v2.clustering_model_type = v1.clustering_model_type |
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AND v2.clustering_model_name = v2.clustering_model_name |
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AND v2.clustering_version = 2 |
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INNER JOIN iss.pictures_embedding v3 |
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ON v1.pictures_id = v3.pictures_id |
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AND v3.clustering_type = v1.clustering_type |
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AND v3.clustering_model_type = v1.clustering_model_type |
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AND v3.clustering_model_name = v1.clustering_model_name |
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AND v3.clustering_version = 3 |
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LEFT JOIN iss.pictures_location loc |
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ON loc.pictures_id = v1.pictures_id |
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WHERE v1.clustering_version = %s |
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ORDER BY pictures_id ASC LIMIT %s""" |
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db_manager.cursor.execute(sql, (1, LIMIT)) |
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results = db_manager.cursor.fetchall() |
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return pd.DataFrame(results, columns=db_manager.cursor.column_names) |
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def create_sprite(config, df): |
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images_array = [Tools.read_np_picture(os.path.join(config.get('directory')['collections'], "%s.jpg" % picture_id), target_size = (TARGET_SIZE_HEIGHT, TARGET_SIZE_WIDTH)) for picture_id in df['pictures_id']] |
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sprite = np.zeros((TARGET_SIZE_HEIGHT*SPRITE_NB_LIGNE, TARGET_SIZE_WIDTH*SPRITE_NB_COLONNE, 3)) |
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index = 0 |
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for i in range(SPRITE_NB_LIGNE): |
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for j in range(SPRITE_NB_COLONNE): |
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sprite[(i*TARGET_SIZE_HEIGHT):(i+1)*TARGET_SIZE_HEIGHT, (j*TARGET_SIZE_WIDTH):(j+1)*TARGET_SIZE_WIDTH, :] = images_array[index] |
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index += 1 |
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if index >= len(images_array): |
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break |
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if index >= len(images_array): |
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break |
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img = Tools.display_one_picture(sprite) |
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return img |
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def generate_facets(config, df): |
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proto = GenericFeatureStatisticsGenerator().ProtoFromDataFrames([{'name': 'facets-iss', 'table': df}]) |
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protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8") |
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HTML_TEMPLATE = """ |
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<script src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js"></script> |
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<link rel="import" href="https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html" > |
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<facets-overview id="elem"></facets-overview> |
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<script> |
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document.querySelector("#elem").protoInput = "{protostr}"; |
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</script>""" |
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html = HTML_TEMPLATE.format(protostr=protostr) |
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return html |
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def generate_facets_dive(config, df, relative_sprite_path): |
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jsonstr = df.to_json(orient = 'records') |
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HTML_TEMPLATE = """ |
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<script src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js"></script> |
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<link rel="import" href="https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html"> |
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<facets-dive id="elem" height="600" cross-origin="anonymous" sprite-image-width="{sprite_width}" sprite-image-height="{sprite_height}"> |
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</facets-dive> |
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<script> |
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var data = {jsonstr}; |
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var atlas_url = "{atlas_url}"; |
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document.querySelector("#elem").data = data; |
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document.querySelector("#elem").atlasUrl = atlas_url; |
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</script>""" |
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html = HTML_TEMPLATE.format(jsonstr=jsonstr, atlas_url = relative_sprite_path, sprite_width=TARGET_SIZE_WIDTH, sprite_height=TARGET_SIZE_HEIGHT) |
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return html |
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def main(): |
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## db manager |
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db_manager = Tools.create_db_manager(CONFIG) |
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## request data |
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df = request_data(CONFIG, db_manager) |
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## create sprite |
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sprite = create_sprite(CONFIG, df) |
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## save sprite |
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sprite.save(os.path.join(CONFIG.get('directory')['reports'], 'figures', 'sprite_altas.png'), "PNG") |
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## generate facets |
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html_facets = generate_facets(CONFIG, df) |
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with open(os.path.join(CONFIG.get('directory')['reports'], 'facets.html'),'w') as f: |
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f.write(html_facets) |
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## generate facets-dive |
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html_facets_dive = generate_facets_dive(CONFIG, df, './figures/sprite_altas.png') |
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with open(os.path.join(CONFIG.get('directory')['reports'], 'facets-dive.html'), 'w') as f: |
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f.write(html_facets_dive) |
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if __name__ == '__main__': |
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main() |
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