A set of special tools to assist the data generation process for the AI model. A number of operations performed during the preparation of the data were collected in a single application. These operations are:
- Resize
- Crop
- Increase brightness
- Rotate
- Label
- Split as train and test randomly
opencv-python version(4.6.0)
PyQt5 version(5.15.7)
pip install -r requeriments.txt
Mouse events of the Pyqt5 library are used
When the "save" button is clicked after the labeling process is completed,
It creates a label file in ".xml" format with the same file path as the images and the same name as the image.
To create a new AI project file on the desktop, write the project name and click the "create" button.
"images/train" and "images/train" paths are automatically created in the file.
All images in the dataset are resized according to the entered values
All images in the dataset are cropped according to the entered values
All images in the dataset are increased brightness according to the entered values
All images in the dataset are increased brightness according to the entered values
All data labeled with the entered rate value are randomly distributed in the train and test folders.
Thanks to randomly distributed data, your model training will be more efficient.