- python 3.5+
- dlib
- Android Studio with Kotlin configured
Open the android directory in Android Studio.
Build and run.
Run:
python3.5 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py init_db --update_feats -o database/data/*_clean.json # This extracts features from scraped data and will take a while
python main.py server
├── algorithm # All things deep learning
│ ├── README.md
│ ├── __init__.py
│ ├── bbox # Extract upper-body bounding boxes
│ │ ├── __init__.py
│ │ ├── bbox_heuristic.py # Upper body bbox from face bbox
│ │ ├── crop.py # Crop images from bbox
│ │ ├── draw_bbox_prediction.py # Visualize bbox
│ ├── classification # Readying the data from DeepFashion for classification training
│ │ ├── config.py # Constants
│ │ ├── prepare_json_cats.py # Categorize Deepfashion into labeled categories
│ │ ├── prepare_json_meta_cats.py # Categorize Deepfashion into upper-body categories
│ │ ├── resnetimpl.py
│ │ ├── split_train_test.py
│ │ └── train_clf.py # Train classifier
│ ├── extract_color_data_runner.py # Manually extract color data from images
│ ├── feats # The code to use a trained model to exctract features
│ │ ├── FeatsExtractor.py # The interface to exctract feats, used by the database
│ │ ├── __init__.py
│ │ ├── closest_feat.py
│ │ ├── extract_feats.py
│ │ ├── plot_knn.py # [DEPRECATED]: generate html to plot results (see vgg_demo.jpg)
│ │ ├── resnetimpl.py
│ │ └── weights # Trained model weights go here
│ ├── nn_scorer.py # [DEPRECATED]: manually score results
│ ├── rgb_utils.py # Utils to append color data to the database
│ └── vgg_finetune # Re-train vgg with transfer-learning
│ ├── clf_feats_frozen.py
│ └── features_clf.py
├── android # The android App
├── database
│ ├── BaseDB.py # Interface for databases
│ ├── TinyDB_DB.py # Implementation of BaseDB using TinyDB
│ ├── data # Cleaned up scraped data
│ │ ├── castro_clean.json
│ │ └── hm_clean.json
│ ├── process_images.py # Pre-processing pipeline
├── main.py # Main commands to run the backend
├── requirements.txt # Pip package requirements
├── scraping # Data scraping from fashion outlets
│ ├── ScrapeProduct.py # Base interface for scrapers
│ ├── castro.py # Implementation of ScrapeProduct for castro
│ ├── clean_castro.py # Script to clean up scraped castro data for indexing
│ ├── clean_hm.py # Script to clean up scraped castro data for indexing
│ ├── hm.py # Implementation of ScrapeProduct for h&m
│ ├── phantomjs # The phantomjs browser driver
│ ├── utils.py # Html utilities
├── server # Backend server that communicates with the Frontend app
│ └── server_main.py # Implementation of the server using sockets
├── tests # Unit tests run with pytest
├── tiny_faces # The tiny-faces face recognition model
└── vgg_demo.jpg
- Itamar Shenhar github: itamar8910 - email: [email protected]
- Tomer Keren github: Tadaboody - email: [email protected]