mirror of
https://github.com/prise6/smart-iss-posts
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predict picture embedding and save it
This commit is contained in:
Francois Vieille
parent
78ebb24f25
commit
ac1f75d28e
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@ -29,8 +29,11 @@ class AbstractClustering:
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self.colors = [Tools.get_color_from_label(label, n_classes) for label in self.final_labels]
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return self
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def predict_embedding(self):
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raise NotImplementedError
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def save(self):
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raise NotImplementedError
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def load(self):
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raise NotImplementedError
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raise NotImplementedError
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@ -51,6 +51,9 @@ class N2DClustering(AbstractClustering):
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cluster in np.unique(self.final_labels)}
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return self.silhouette_score_labels
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def predict_embedding(self, pictures_np):
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return self.umap_fit.transform(pictures_np)
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def save(self):
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Tools.create_dir_if_not_exists(self.save_directory)
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@ -58,5 +61,5 @@ class N2DClustering(AbstractClustering):
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joblib.dump(self.kmeans_fit, os.path.join(self.save_directory, self.kmeans_save_name))
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def load(self):
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self.umap_fit = joblib.load(os.path.join(self.save_directory, self.pca_save_name))
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self.umap_fit = joblib.load(os.path.join(self.save_directory, self.umap_save_name))
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self.kmeans_fit = joblib.load(os.path.join(self.save_directory, self.kmeans_save_name))
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@ -1,7 +1,7 @@
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# -*- coding: utf-8 -*-
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class DataBaseManager:
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class MysqlDataBaseManager:
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def __init__(self, connexion, config):
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self.conn = connexion
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@ -9,25 +9,66 @@ class DataBaseManager:
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self.cursor = self.conn.cursor()
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def createPicturesTable(self, force = False):
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def create_pictures_location_table(self, force = False):
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if force:
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self.cursor.execute("DROP TABLE IF EXISTS `iss`.`pictures`;")
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self.cursor.execute("DROP TABLE IF EXISTS `iss`.`pictures_location`;")
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self.cursor.execute("""
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CREATE TABLE `iss`.`pictures` (
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CREATE TABLE IF NOT EXISTS `iss`.`pictures_location` (
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`pictures_latitude` FLOAT(10, 6) NULL,
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`pictures_longitude` FLOAT(10, 6 ) NULL ,
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`pictures_id` VARCHAR( 15 ) PRIMARY KEY ,
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`pictures_timestamp` TIMESTAMP NULL ,
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`pictures_location` TEXT NULL
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`pictures_location_text` TEXT NULL
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) ENGINE = MYISAM ;
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""")
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def insertRowPictures(self, array):
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def insert_row_pictures_location(self, array):
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sql_insert_template = "INSERT INTO `iss`.`pictures` (pictures_latitude, pictures_longitude, pictures_id, pictures_timestamp, pictures_location) VALUES (%s, %s, %s, %s, %s);"
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sql_insert_template = "INSERT INTO `iss`.`pictures_location` (pictures_latitude, pictures_longitude, pictures_id, pictures_timestamp, pictures_location_text) VALUES (%s, %s, %s, %s, %s);"
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self.cursor.executemany(sql_insert_template, array)
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self.conn.commit()
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return self.cursor.rowcount
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def create_pictures_embedding_table(self, force = False):
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if force:
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self.cursor.execute("DROP TABLE IF EXISTS `iss`.`pictures_embedding`;")
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self.cursor.execute("""
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CREATE TABLE IF NOT EXISTS `iss`.`pictures_embedding` (
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`pictures_id` VARCHAR( 15 ) ,
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`pictures_x` FLOAT(8, 4),
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`pictures_y` FLOAT(8, 4),
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`clustering_type` VARCHAR(15),
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`clustering_version` VARCHAR(5),
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`clustering_model_type` VARCHAR(15),
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`clustering_model_name` VARCHAR(15),
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UNIQUE KEY `unique_key` (`pictures_id`,`clustering_type`, `clustering_version`, `clustering_model_type`,`clustering_model_name`),
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KEY `index_key_1` (`pictures_id`)
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) ENGINE = MYISAM ;
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""")
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def drop_embedding_partition(self, clustering_type, clustering_version, clustering_model_type, clustering_model_name):
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req = "DELETE FROM `iss`.`pictures_embedding` WHERE clustering_type = %s AND clustering_version = %s AND clustering_model_type = %s AND clustering_model_name = %s"
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self.cursor.execute(req, (clustering_type, clustering_version, clustering_model_type, clustering_model_name))
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self.conn.commit()
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return self.cursor.rowcount
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def insert_row_pictures_embedding(self, array):
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sql_insert_template = "INSERT INTO `iss`.`pictures_embedding` (pictures_id, pictures_x, pictures_y, clustering_type, clustering_version, clustering_model_type, clustering_model_name) VALUES (%s, %s, %s, %s, %s, %s, %s);"
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self.cursor.executemany(sql_insert_template, array)
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self.conn.commit()
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106
iss/exec/bdd.py
106
iss/exec/bdd.py
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@ -3,28 +3,96 @@ import time
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import mysql.connector
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import pandas as pd
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import datetime as dt
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import numpy as np
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from iss.init_config import CONFIG
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from iss.data.DataBaseManager import DataBaseManager
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CON_MYSQL = mysql.connector.connect(
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host = CONFIG.get('mysql')['database']['server'],
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user = CONFIG.get('mysql')['database']['user'],
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passwd = CONFIG.get('mysql')['database']['password'],
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database = CONFIG.get('mysql')['database']['name'],
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port = CONFIG.get('mysql')['database']['port']
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)
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dbm = DataBaseManager(CON_MYSQL, CONFIG)
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from iss.data.DataBaseManager import MysqlDataBaseManager
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from iss.clustering import ClassicalClustering, AdvancedClustering, N2DClustering
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from iss.tools import Tools
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history = pd.read_csv(os.path.join(CONFIG.get("directory")['data_dir'], "raw", "history", "history.txt"), sep=";", names=['latitude', 'longitude', 'id', 'location'])
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history['timestamp'] = pd.to_datetime(history.id, format="%Y%m%d-%H%M%S").dt.strftime("%Y-%m-%d %H:%M:%S")
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history.fillna('NULL', inplace=True)
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history = history[['latitude', 'longitude', 'id', 'timestamp', 'location']]
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history_tuple = [tuple(x) for x in history.values]
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def create_db_manager(config):
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dbm.createPicturesTable(force=True)
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count = dbm.insertRowPictures(history_tuple)
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CON_MYSQL = mysql.connector.connect(
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host = config.get('mysql')['database']['server'],
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user = config.get('mysql')['database']['user'],
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passwd = config.get('mysql')['database']['password'],
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database = config.get('mysql')['database']['name'],
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port = config.get('mysql')['database']['port']
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)
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print(count)
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return MysqlDataBaseManager(CON_MYSQL, config)
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def populate_locations(config, db_manager):
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history = pd.read_csv(os.path.join(CONFIG.get("directory")['data_dir'], "raw", "history", "history.txt"), sep=";", names=['latitude', 'longitude', 'id', 'location'])
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history['timestamp'] = pd.to_datetime(history.id, format="%Y%m%d-%H%M%S").dt.strftime("%Y-%m-%d %H:%M:%S")
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history.fillna('NULL', inplace=True)
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history = history[['latitude', 'longitude', 'id', 'timestamp', 'location']]
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history_tuple = [tuple(x) for x in history.values]
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db_manager.create_pictures_location_table(force=True)
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count = db_manager.insert_row_pictures_location(history_tuple)
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print("Nombre d'insertion: %s" % count)
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def populate_embedding(config, db_manager, clustering_type, clustering_version, clustering_model_type, clustering_model_name, drop=False):
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db_manager.create_pictures_embedding_table()
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clustering_config = config.get('clustering')[clustering_type]
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clustering_config['version'] = clustering_version
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clustering_config['model']['type'] = clustering_model_type
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clustering_config['model']['name'] = clustering_model_name
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if drop:
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db_manager.drop_embedding_partition(clustering_type, clustering_version, clustering_model_type, clustering_model_name)
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if clustering_type == 'n2d':
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clustering = N2DClustering(clustering_config)
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elif clustering_type == 'classical':
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clustering = ClassicalClustering(clustering_config)
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else:
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raise Exception
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clustering.load()
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model, model_config = Tools.load_model(CONFIG, clustering_model_type, clustering_model_name)
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filenames = Tools.list_directory_filenames(CONFIG.get('directory')['collections'])
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generator = Tools.load_latent_representation(CONFIG, model, model_config, filenames, 496, None, True)
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count = 0
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for ids, latents in generator:
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pictures_embedding = clustering.predict_embedding(latents)
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rows = []
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for i, id in enumerate(ids):
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rows.append((
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id,
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float(np.round(pictures_embedding[i][0], 4)),
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float(np.round(pictures_embedding[i][1], 4)),
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clustering_type,
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clustering_version,
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clustering_model_type,
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clustering_model_name
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))
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count += db_manager.insert_row_pictures_embedding(rows)
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print("Nombre d'insertion: %s / %s" % (count, len(filenames)))
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return
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def main(action = 'populate_embedding'):
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db_manager = create_db_manager(CONFIG)
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if action == 'population_locations':
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populate_locations(CONFIG, db_manager)
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elif action == 'populate_embedding':
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populate_embedding(CONFIG, db_manager, 'n2d', 1, 'simple_conv', 'model_colab')
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else:
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pass
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if __name__ == '__main__':
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main()
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@ -17,44 +17,6 @@ from iss.clustering import ClassicalClustering, AdvancedClustering, N2DClusterin
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_DEBUG = True
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def load_model(config, clustering_type):
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"""
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Load model according to config
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"""
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model_type = config.get('clustering')[clustering_type]['model']['type']
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model_name = config.get('clustering')[clustering_type]['model']['name']
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config.get('models')[model_type]['model_name'] = model_name
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if model_type == 'simple_conv':
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model = SimpleConvAutoEncoder(config.get('models')[model_type])
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elif model_type == 'simple':
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model = SimpleAutoEncoder(config.get('models')[model_type])
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else:
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raise Exception
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model_config = config.get('models')[model_type]
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return model, model_config
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def load_images(config, clustering_type, model, model_config, batch_size, n_batch):
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"""
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load images and predictions
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"""
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model_type = config.get('clustering')[clustering_type]['model']['type']
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filenames = Tools.list_directory_filenames(os.path.join(config.get('sampling')['autoencoder']['directory']['train']))
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (model_config['input_height'], model_config['input_width']), batch = batch_size, nb_batch = n_batch)
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pictures_id, pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model)
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if model_type in ['simple_conv']:
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intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], model_config['latent_width']*model_config['latent_height']*model_config['latent_channel']))
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else:
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intermediate_output = pictures_preds
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return pictures_id, intermediate_output
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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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@ -217,9 +179,9 @@ def plot_mosaics(config, clustering_type, clustering, output_image_width, output
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def main():
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_CLUSTERING_TYPE = 'classical'
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_CLUSTERING_TYPE = 'n2d'
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_BATCH_SIZE = 496
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_N_BATCH = 1
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_N_BATCH = 10
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_PLOTS = True
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_MOSAICS = True
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_SILHOUETTE = True
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@ -228,8 +190,9 @@ def main():
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_MOSAIC_NROW = 10
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_MOSAIC_NCOL_MAX = 10
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model, model_config = load_model(CONFIG, _CLUSTERING_TYPE)
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pictures_id, intermediate_output = load_images(CONFIG, _CLUSTERING_TYPE, model, model_config, _BATCH_SIZE, _N_BATCH)
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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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@ -2,6 +2,7 @@
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import PIL
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import os
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import re
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import numpy as np
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from io import BytesIO
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import base64
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@ -57,19 +58,30 @@ class Tools:
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return path
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@staticmethod
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def encoded_pictures_from_generator(generator, model):
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def encoded_pictures_from_generator(generator, model, by_step=False):
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if by_step:
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return Tools.encoded_pictures_from_generator_by_step(generator, model)
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predictions_list = []
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predictions_id = []
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for imgs in generator:
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predictions_id.append(imgs[0])
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predictions_list.append(model.get_encoded_prediction(imgs[1]))
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tmp_id = [os.path.splitext(os.path.basename(id))[0] for id in imgs[0]]
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tmp_pred = model.get_encoded_prediction(imgs[1])
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predictions_id += tmp_id
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predictions_list.append(tmp_pred)
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predictions = np.concatenate(tuple(predictions_list), axis = 0)
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predictions_id = [os.path.splitext(os.path.basename(id))[0] for sub_id in predictions_id for id in sub_id]
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return predictions_id, predictions
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@staticmethod
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def encoded_pictures_from_generator_by_step(generator, model):
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for imgs in generator:
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# tmp_id = [os.path.splitext(os.path.basename(id))[0] for sub_id in imgs[0] for id in sub_id]
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tmp_id = [os.path.splitext(os.path.basename(id))[0] for id in imgs[0]]
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tmp_pred = model.get_encoded_prediction(imgs[1])
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yield (tmp_id, tmp_pred)
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@staticmethod
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def read_np_picture(path, target_size = None, scale = 1):
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# img = PIL.Image.open(filename)
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@ -79,11 +91,12 @@ class Tools:
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return img_np
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@staticmethod
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def list_directory_filenames(path):
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def list_directory_filenames(path, pattern = ".*jpg$"):
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filenames = os.listdir(path)
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np.random.seed(33213)
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np.random.shuffle(filenames)
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filenames = [os.path.join(path,f) for f in filenames]
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pattern_regex = re.compile(pattern)
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filenames = [os.path.join(path,f) for f in filenames if pattern_regex.match(f)]
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return filenames
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@ -138,4 +151,47 @@ class Tools:
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linkage_matrix = np.column_stack([children, distance, no_of_observations]).astype(float)
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# Plot the corresponding dendrogram
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dendrogram(linkage_matrix, **kwargs)
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dendrogram(linkage_matrix, **kwargs)
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@staticmethod
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def load_model(config, model_type, model_name):
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"""
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Load model according to config
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"""
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from iss.models import SimpleConvAutoEncoder, SimpleAutoEncoder
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config.get('models')[model_type]['model_name'] = model_name
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if model_type == 'simple_conv':
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model = SimpleConvAutoEncoder(config.get('models')[model_type])
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elif model_type == 'simple':
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model = SimpleAutoEncoder(config.get('models')[model_type])
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else:
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raise Exception
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model_config = config.get('models')[model_type]
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return model, model_config
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@staticmethod
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def load_latent_representation(config, model, model_config, filenames, batch_size, n_batch, by_step):
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"""
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load images and predictions
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"""
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if by_step:
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return Tools.load_latent_representation_by_step(config, model, model_config, filenames, batch_size, n_batch)
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (model_config['input_height'], model_config['input_width']), batch = batch_size, nb_batch = n_batch)
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pictures_id, pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model, by_step)
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intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], -1))
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return pictures_id, intermediate_output
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@staticmethod
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def load_latent_representation_by_step(config, model, model_config, filenames, batch_size, n_batch):
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (model_config['input_height'], model_config['input_width']), batch = batch_size, nb_batch = n_batch)
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for pictures_id, pictures_preds in Tools.encoded_pictures_from_generator(generator_imgs, model, True):
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intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], -1))
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yield pictures_id, intermediate_output
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