Perfect for profile picture processing for your website or batch work for ID cards, autocrop will output images centered around the biggest face detected.

From the command line:

usage: [-h] [-o OUTPUT] [-i INPUT] [-w WIDTH] [-H HEIGHT] [-v]

Automatically crops faces from batches of pictures

optional arguments:
  -h, --help            Show this help message and exit
  -o, --output, -p, --path
			Folder where cropped images will be placed.
			Default: current working directory
  -i, --input
			Folder where images to crop are located.
			Default: current working directory
  -w, --width
			Width of cropped files in px. Default=500
  -H, --height
			Height of cropped files in px. Default=500
  -v, --version         Show program's version number and exit

• Example: autocrop -i pics -o crop -w 400 -H 400.

The previous command will:

1. Copy all images found in the top level of pics to crop,
2. Crop around the face and resize to 400x400 pixels all images in crop.

Images where a face can't be detected will be left in crop. If no output folder is added, asks for confirmation and destructively crops images in-place.

Simple! In your command line, type:

pip install autocrop

Autocrop uses OpenCV to perform face detection, which is installed through binary wheels. If you already have OpenCV 3+ installed, you may wish to uninstall the additional OpenCV installation: pip uninstall opencv-python.

In some cases, you may wish the package directly, instead of through PyPI:

cd ~
git clone https://github.com/leblancfg/autocrop
cd autocrop
pip install .

Development of a conda-forge package for the Anaconda Python distribution is also currently slated for development. Please leave feedback on issue #7 if you are insterested in helping out.

Best practice for your projects is of course to use virtual environments. At the very least, you will need to have pip installed.

Autocrop is currently being tested on:

• Python:
  • 2.7
  • 3.4
  • 3.5
  • 3.6
• OS:
  • Linux
  • macOS
  • Windows

Check out:

• http:https://docs.opencv.org/master/d7/d8b/tutorial_py_face_detection.html#gsc.tab=0
• http:https://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html#gsc.tab=0

Adapted from:

• http:https://photo.stackexchange.com/questions/60411/how-can-i-batch-crop-based-on-face-location

Although autocrop is essentially a CLI wrapper around a single OpenCV function, it is actively developed. It has active users throughout the world.

We have all the love in the world for extra contributors if you'd like to contribute to the codebase. Please follow these steps:

• Fork the repository on GitHub.
• Install the extra dev packages with pip install -r requirements-test.txt
• Make a branch off of master, commit and test your changes to it.

Pull requests are tested on continuous integration (CI) servers before they are green-lit to merge with the master branch.

• Run the tests with pytest.
• Always run flake8 . before submitting to check your coding style, as your CI will fail otherwise.
• Submit a Pull Request to the master branch on GitHub.

If you have any questions regarding this, please reach me at [email protected]. We'll make sure we get through the steps correctly.

If you'd like to have a development environment for autocrop, you should create a virtualenv and then do pip install -e . from within the directory.