autocropper-supervision

This utility uses the YoloV8 model for detecting people, and the YoloV8-face model for detecting faces.

It processes a directory of images, detects the people with faces, auto-crops the images (as a copy) and saves them to a specified directory.

Processing results for 25 images from Unsplash

Dataset

Command

python "main.py" --images_directory "unsplash-images" --results_folder "unsplash-images-results" --crop_ratio "(3,4)" --person_percent_detection_cutoff 0.075 --person_padding_percent 0.02

Pre-Requisites

1. Git
2. Python 3.10
3. Open cmd
4. Clone the repository
   1. git clone https://github.com/djbielejeski/autocropper-supervision
5. Navigate into the repository
   1. cd autocropper-supervision

Activate Environment and Install Dependencies

python -m venv autocropper-supervision-venv
autocropper-supervision-venv\Scripts\activate.bat
pip install -r requirements.txt

Run

python "main.py" --images_directory "C:/Images"

Cleanup

cmd> deactivate 

All Arguments

python "main.py" --images_directory "C:\\images" --results_folder "C:\\results" --crop_ratio "(3,4)" --person_percent_detection_cutoff 0.075 --person_padding_percent 0.02

Command Line Parameters

Command	Type	Example	Description
--images_directory	string	"C:\\images"	The path the images folder to process
--results_folder	string	"C:\\results"	Optional Defaults to \results
--crop_ratio	tuple	(3,4)	Optional Defaults to (3,4). Available Options: (1,1), (2,3), or (3,4)
--person_percent_detection_cutoff	float	0.075	Optional Defaults to 0.075. Percentage of the overall image that must contain a person to be detected. Useful for removing background people from the results
--person_padding_percent	float	0.02	Optional Defaults to 0.02. Percent of the detected person to add padding for. Example: Detected person is 512x512px, will add 512px * 0.02 => 10px of padding.