shawbc / yolo-data-augmentation

Applied augmentation on yolov5 and yolov8 dataset.

YOLOv5/YOLOv8 Data Augmentation with Albumentations

This GitHub repository offers a solution for augmenting datasets for YOLOv8 and YOLOv5 using the Albumentations library. Albumentations is a Python package designed for image augmentation, providing a simple and flexible approach to perform various image transformations. For more detail you can refer my medium article.

Input

Output

Directories description

• input-ds contain the input of YOLOv8 and YOLOv5 which are following directories.
  • Images directory contains the images
  • labels directory contains the .txt files. Each .txt file contains the normalized bounding boxes in a following format.
• out-aug-ds contain the augmented output contains following directories.
  • Images directory contains the augmented images.
  • labels directory contains the augmented labels.
• bb_image This directory contains images with bounding boxes for visualizing the results of augmented data. This is for validation; bounding boxes should be correctly drawn on the objects of interest.
• utils.py: Contains all user-defined helper function.
• main.py: Contains Yolo dataset augmentor pipeline
• CONSTANT.yaml contain following contants need to update on according to your case.
  • inp_img_pth for input images path
  • inp_lab_pth for input labels path
  • out_img_pth for output image path
  • out_lab_pth for output labels path
  • transformed_file_name: use to name augmented output to differentiate from other input dataset.
  • CLASSES: list of input class name according to class number.

Usage

• step to apply augmentation on your own dataset.
  • Create Virtual Environment.
  • Install requirements using

    pip install -r requirements.txt
    

  • Provide the input and output path in CONSTANT.yaml file.
  • Update the name of transformed_file_name in CONSTANT.yaml otherwise code will overwrite last augmentations.
  • Provide the list of name of classes in CONSTANT.yaml in a same sequence as used to assign class numbers in the YOLO dataset labeling.
    • For example, if you provided a class list as ['obj1', 'obj2', 'obj3'], the class number used for 'obj1' in the label file should be 0, similarly for 'obj2', the class number should be 1, and so on.
  • Run the pipeline using

    python3 run.py