Picman

Picman is a Python-based tool for rapidly building training and test sets with images taken from your camera device.

You have only to capture the images you want by pressing SPACE, and ESC when done.

Behind the scene, Picman creates two folders under the same root, one for the training set, and the other for the test set, and populates them autonomously when you capture the images.

Installing

These instructions will get you a copy of this project up and running on your machine.
If any problem occurs, please, open an issue or email me.

Development and test platform

• PyCharm 2021
• macOS 11.4 (Big Sur) on MacBookPro with Intel core
• Python 3.9

Installation steps

1. Clone or download this project.
2. Create and activate a new venv (strongly recommended).
3. Install the project's requirements in the venv.

That's all!

Run Picman

You can run Picman from PyCharm, or via Terminal. The script path is ../picman/src/picman.py.
Note that Picman will create the dataset folder in your current working directory.

Run via PyCharm

You have to create a run configuration before running, as shown here. In detail, you have to set your working directory, for example abs_path_to/picman/, and the script path abs_path_to/picman/src/picman.py.
Then run.

Run via Terminal (the hard way)

You have to export the Picman absolute path abs_path_to/picman/src/ in PYTHONPATH.
Then run.

(venv)$ export PYTHONPATH=$PYTHONPATH:my_path_to_picman
(venv)$ cd picman
(venv)/picman$ python src/picman.py -r 0.8

In the examples above, the parameter -r 0.8 sets a ratio of the training set over the whole dataset of about 80%.
See below for the detailed list of customization parameters.

Custom parameters

You can specify several parameters to customize Picman, as described in the following.

Parameter	Default	Description
-d	dataset-current_time	The name of the base folder, then the current date-time will be appended
-n	img	Base name for captured images, then an incremental counter will be appended
-r	0.9	The size of the training set over the entire dataset, in percentage
-t	train	The name of the folder storing the training set
-v	test	The name of the folder storing the validation/test set
-i	0	Video device index (0 is the webcam)

Contributing

Please read our contributing instructions and our code of conduct, for details on the process of submitting requests to us.

Versioning

We use SemVer for versioning. For the versions available, see the releases on this repository.

License

This project is licensed under the MIT License - see the LICENSE for details.

Author

Francesco Racciatti