This tool automatically collect images from Google or Bing and optionally resize them.

python download.py "funny cats" -limit=100 -dest=folder_name -resize=250x250

Then you can randomly generate new images with image augmentation from an existing folder. It will add noise, rotate, transform, flip, blur on random images.

python augmentation.py -folder=my_folder/funny_cats -limit=10000

TADA ! In few seconds you will get 10 000 different images of funny cats to train your favorite deep learning algorithm !

• Pre-requirements
• Installation
• Run unit tests
• Usage
  • Download images
  • Image augmentation
  • Create a custom image augmentation pipeline
• Common issues
• Acknowledgments

This project is tested with Python 3.5.4 and more.

Linux

• chromium-browser package (sudo apt-get install chromium-browser)

Windows

• Chrome should be installed
• Microsoft Visual C++ Build Tools (scikit image dependency, see for more info)

Git clone the project

Get the python dependencies

pip install -r requirements.txt

python -m unittest discover

python download.py "red car" -limit=150 -dest=folder_name -resize=250x250

After running this command, you will have 150 images of red cars (resized 250px by 250px) in the /folder_name/red_car folder.

You can find all possible parameters in the table below (also available with the --help parameter) :

python augmentation.py -folder=your_folder -limit=10000

10 000 augmented images will output by default to the "output" folder inside your image folder.

By default, this command will randomly apply these image transformations :

• Blur image (with a probability of 10%)

• Add Random noise (with a probability of 50%)

• Horizontal flip (with a probability of 30%)

• Left or Right rotation between 0 or 25 degree (with a probability of 50%)

• ... to be completed

You can customize these default values by editing the augmentation_config.py file or by making your own image augmentation pipeline

You can find all possible parameters in the table below (also available with the --help parameter) :

from augmentation.augmentation import DatasetGenerator

pipeline = DatasetGenerator(
    folder_path="images/red_car/",
    max_files=5000,
    save_to_disk=True,
    folder_destination="images/red_car/results"
)
pipeline.rotate(probability=0.5, max_left_degree=25, max_right_degree=25)
pipeline.random_noise(probability=0.5)
pipeline.blur(probability=0.5)
pipeline.vertical_flip(probability=0.1)
pipeline.horizontal_flip(probability=0.2)
pipeline.resize(probability=1, width=20, height=20)
pipeline.execute()

That's it !

WebDriverException: Message: unknown error: cannot find Chrome binary

Make sure chromedriver is well installed on your PATH (run the which chromedriver command on Linux and then echo $PATH). Also Chrome should be installed on your machine (or the chromium-package for Linux).

You can install the chromedriver with this command (more information here): pip install chromedriver_installer --install-option="--chromedriver-version=2.35"

error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools

As this repo use scikit-image for image processing, on Windows you need Microsoft Visual C++ Build Tools which is provided with Visual Studio (think to check the C++ options on installation). You can install it with the link below.

• This repo is largely inspired by the work of Marcus Bloice on his Augmentor project. Many thanks for the great work and the useful documentation.

• I also pick some ideas from this great series of articles for the automatic part to grab images.

The goal of this repo is mainly to provide the smaller python library as possible to generate an image dataset, without a big framework like Keras, Tflearn etc, which can be hard to configure and install for new people working on Data Science / AI.