k3v007 / image-dataset-generator

Image augmentation using python library PIL (pillow). It generates images with great variations using just single line command.

Image-dataset Generator

It uses PIL, Python's imaging library to create a large dataset with great variations (contrast, brightnes, color balance, saturation etc) from few images.

Installation

• Create a virtual environment and install the requirements

    pip install -r requirements.txt

Usage

• Create a folder with different category folders inside it, and place the images in 'bright' / 'dim' / 'normal' folders depending upon the quality of image.

NOTE: If you are unsure about the quality, just place them in the same folder - normal. You can also skip some folders but at least one of the type-folders is must!

    Fruit_images
    ├───Apple
    │   ├───bright
    │   ├───dim
    │   └───normal
    ├───Banana
    │   ├───bright
    │   └───dim
    ├───Orange
    │   ├───bright
    │   └───normal
    └───Papaya
        └───normal

• Use the following commands to generate the images

    # python image_gen.py <src_folder> --ratio <validtion_data>
    # Run 'python image_gen.py -h' for more information
    
    # Fow windows
    python image_gen.py D:\myDocuments\Fruit_images --ratio 0.25
    
    # For linux
    python image_gen.py ~/home/Downloads/Fruit_images --ratio 0.25

• data folder is created, containing output images, inside the same source_folder with the following structure

    Fruit_images
    ├───Apple
    │   ├───bright
    │   ├───dim
    │   └───normal
    ├───Banana
    │   ├───bright
    │   └───dim
    ├───data
    │   ├───train
    │   │   ├───Apple
    │   │   ├───Banana
    │   │   ├───Orange
    │   │   └───Papaya
    │   └───validation
    │       ├───Apple
    │       ├───Banana
    │       ├───Orange
    │       └───Papaya
    ├───Orange
    │   ├───bright
    │   └───normal
    └───Papaya
        └───normal