The idea of the project is to create a model that can remove the background from an image and store it in Amazon S3 Bucket. The user can also invoke Lambda function to crop already stored images.
├── README.md <- The top-level README for developers using this project.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- Makes project pip installable (pip install -e .) so src can be imported
│
├── GETTING_STARTED.rst <- Informations about environment and AWS configuration
│
├── Project_diagram.jpg <- Diagram of the project structure
│
├── Streamlit_UI.png <- Screenshot from executed streamlit UI
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── saved_models <- Contains model weights
│ │ └── u2net <- u2net model weights dir
│ │ ├── __init__.py <- Makes src a Python module
│ │ └── u2net.pth <- u2net model weights
│ ├── features <- Scripts to handle AWS S3 buckets, Lambda functions and removing background
│ │ ├── __init__.py <- Makes features a Python module
│ │ ├── aws_client.py <- Contains class that handles all AWS tasks
│ │ └── background_removing_utils.py <- Functions to remove background from an image
│ │
│ ├── streamlit_ui <- Scripts to manage UI
│ │ │── __init__.py <- Makes front a Python module
│ │ ├── cropping_images_utils.py <- Function used to crop images, handles UI
│ │ ├── initialization.py <- Initialization of session states and paths
│ │ ├── reading_data_from_s3.py <- Functions used to read data from S3, handles UI
│ │ └── removing_background.py <- Removing background, handles UI
│ │
│ ├── model <- Contains model data
│ │ │── __init__.py <- Makes model a Python module
│ │ │── model.py <- Model loading
│ │ └── u2net.py <- u2net model
│ │
│ ├── main.py <- Main function
Project based on the cookiecutter data science project template. #cookiecutterdatascience