dpservis/yolo-data-augmentation

Applied augmentation on yolov5 and yolov8 dataset.

Apply data augmentation on YOLOv5 or YOLOv8 dataset using Albumentations Library

Albumentations is a Python library for image augmentation that offers a simple and flexible way to perform a variety of image transformations.

Input

Output

Output Visualization

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.
• controller contain following scripts.
  • apply_album_aug.py contain the augmentated operations.
  • validate_results.py draw the augmented labels on augmented image to visualize the results.
  • album_to_yolo_bb.py is used to convert to labels in albumentation format to yolo format
  • get_album_bb.py is used to get labels in albumentation format from input yolo format.
  • workflow.py contain the pipeline to get the desired results.
  • save_augs.py to save the augmented results.
• 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.
  • 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 classes in CONSTANT.yaml in a sequence as use to assign class number in yolo dataset labelling.
    • For example, you provided class list is ['obj1', 'obj2', 'obj3'] class number used for obj1 in label file should be 0, similarly for 'obj2' class number should be 1 and so on.
  • run the pipeline using python3 run.py