From e3faf10dcbc1f10d8d7abfb19178dfc88ce53f8b Mon Sep 17 00:00:00 2001 From: Alessio Savi Date: Sun, 19 May 2019 22:25:48 +0200 Subject: [PATCH] Neural network improved [v0.1.0] (#1) * Artifact - Best parameter for train neural network (.json) - Face rappresentation 'pickled' for avoid face recognition process (.dat) - Neural network model tuned * v0.1.0 BugFix - predict_image: Ensure that classifier is loaded before apply face recognition - load_classifier_from_file: Avoid to raise exception in case of no valid classifier written in configuration. Instead handle only the training/tuning process - init_dataset: Ensure that image is processable Enhancements - dump_model: Handle more use case - Set model path as default if not provided - Set 'model' as file name if not provided - Dump model parameter + model name - init_peoples_list: Initialization of new people is now parallelized - init_peoples_list_core is delegated to execute the core work - init_dataset: Recognition and parsing of new face is now parallelized - init_dataset_core is delegated to execute the core work New Features - unzip_data: Create method for unzip archive in oreder to semplify upload/storage phase - remove_dir: Create method for remove the data after be processed - dump_dataset: Create method for 'picklelize' the image processed and recognized - verify_extension: Create a basecode for verify the type of the file uploaded - tune_network: Create method for tune the hyperparameter of the nerual network - Handle zip file (apply face recognition process - Handle pre-processed data (picklelized dataset) - tuning: Search among every possibile hyperparameter combination and train the neural network - verify_performance: Print lot of usefull information about the trained model --- api/Api.py | 68 ++++++++- conf/test.json | 2 +- dataset/model/README.md | 15 ++ .../model/image_dataset-20190519_210331.dat | Bin 0 -> 8721460 bytes dataset/model/model-20190519_210950.clf | Bin 0 -> 287 bytes dataset/model/model-20190519_210950.json | 10 ++ datastructure/Classifier.py | 138 +++++++++++++++--- datastructure/Person.py | 55 ++++--- main.py | 26 +++- utils/util.py | 52 ++++++- 10 files changed, 316 insertions(+), 50 deletions(-) create mode 100644 dataset/model/README.md create mode 100644 dataset/model/image_dataset-20190519_210331.dat create mode 100644 dataset/model/model-20190519_210950.clf create mode 100644 dataset/model/model-20190519_210950.json diff --git a/api/Api.py b/api/Api.py index 2d84d6b..ad782af 100644 --- a/api/Api.py +++ b/api/Api.py @@ -2,13 +2,15 @@ """ Custom function that will be wrapped for be HTTP compliant """ - +import os +import pickle +import time import zipfile from logging import getLogger from os.path import join as path_join from datastructure.Response import Response -from utils.util import print_prediction_on_image, random_string, remove_dir +from utils.util import print_prediction_on_image, random_string, remove_dir, unzip_data log = getLogger() @@ -23,7 +25,10 @@ def predict_image(img_path, clf, PREDICTION_PATH): """ response = Response() log.debug("predict_image | Predicting {}".format(img_path)) - prediction = clf.predict(img_path) + if clf is None: + prediction = None + else: + prediction = clf.predict(img_path) log.debug("predict_image | Image analyzed!") # Manage success if prediction is not None and isinstance(prediction, list) and len(prediction) == 1: @@ -91,3 +96,60 @@ def train_network(folder_uncompress, zip_file, clf): response.description = "Model succesfully trained!" return response.__dict__ + + +def tune_network(folder_uncompress, zip_file, clf): + """ + Train a new neural model with the zip file provided + :param folder_uncompress: + :param zip_file: + :param clf: + :return: + """ + log.debug("tune_network | uncompressing zip file ...") + check = verify_extension(zip_file.filename) + if check == "zip": # Image provided + folder_name = unzip_data(folder_uncompress, zip_file) + log.debug("tune_network | zip file uncompressed!") + clf.init_peoples_list(peoples_path=folder_name) + dataset = clf.init_dataset() + elif check == "dat": + dataset = pickle.load(zip_file) + else: + dataset = None + + if dataset is not None: + start_time = time.time() + neural_model_file = clf.tuning(dataset["X"], dataset["Y"]) + elapsed_time = time.time() - start_time + + log.debug("tune_network | Removing unzipped files") + if check == "zip": + # TODO: Refactor this method :/ + remove_dir(folder_name) + response = Response() + response.status = "OK" + response.data = neural_model_file + response.description = "Model succesfully trained! | {}".format( + time.strftime("%H:%M:%S.%f", time.gmtime(elapsed_time))) + else: + response = Response() + response.error = "ERROR DURING LOADING DAT" + return response.__dict__ + + +def verify_extension(file): + """ + Wrapper for validate file + :param file: + :return: + """ + extension = os.path.splitext(file)[1] + log.debug("verify_extension | File: {} | Ext: {}".format(file, extension)) + if extension == ".zip": + # In this case we have to analyze the photos + return "zip" + elif extension == ".dat": + # Photos have been alredy analyzed, dataset is ready! + return "dat" + return None diff --git a/conf/test.json b/conf/test.json index bd92348..5fcf24f 100644 --- a/conf/test.json +++ b/conf/test.json @@ -23,7 +23,7 @@ "classifier": { "trainin_dir": "dataset/images/", "model_path": "dataset/model/", - "model": "model-20190519_125204.clf", + "model": "model-20190518_191827.clf", "n_neighbors": "", "knn_algo": "" }, 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