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 !
This project is tested with Python 3.6.4 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) :
| Parameters | Description |
|---|---|
| Keyword (required) | The first parameter should be a keyword describing the images to search for. python download.py "red car" |
| Destination folder -dest or -d |
Specify the destination folder to save files (default: images/) python download.py "red car" -dest=your_folder |
| Limit number -limit or -l |
Specify the number of files to download (default: 50). See the note below for the maximum limit. python download.py "red car" -limit=200 |
| Thumbnail only -thumbnail or -thumb |
Download the thumbnail instead of the full original image python download.py "red car" -thumbnail |
| Resize image -resize |
Resize downloaded images on the fly, to get a dataset formatted with the same size (default: no resizing). The parameter should be a couple of number representing the width and height (32x32 will ouput 32px x 32px image files) python download.py "red car" -resize=32x32" |
| Grab source -source, -src or -allsources |
Choose the website to grab images : Google and/or Bing (default: Google). -allsources parameter can be use to. It will equally mix image files from all available sources python download.py "red car" -source=Google (single source) python download.py "red car" -source=Google -source=Bing (multi source)python download.py "red car" -allsources (all sources) |
Note : There are known limitations for the total number of images you can download in one use of the download.py script. Bing and Google won't let you download more than 800 images each, so the maximum for one download is around 1600 images if you use the -allsources 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%)
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Horizontal flip (with a probability of 30%)
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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) :
| Parameters | Description |
|---|---|
| Keyword (required) | Folder input path containing images that will be augmented.` |
| Destination folder -dest or -d |
Specify the destination folder to save augmented files (default: /your_folder/output) python augmentation.py -folder=your_folder -limit=50 -dest=other_folder |
| Limit number -limit or -l |
Number of image to generate by augmentation (default: 50) |
from augmentation.augmentation import DatasetGenerator
pipeline = DatasetGenerator(
folder_path="images/red_car/",
num_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.
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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.
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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.