Automated Image Augmentation for Object Detection Datasets

Developed a Python tool for augmenting images and annotations (YOLO) in object detection datasets using the ‘imgaug‘ library. Implemented various augmentation techniques including rotation, translation, flipping, cropping, and stretching to enhance dataset diversity.

– Loaded images and annotations from specified directories using ‘os‘ and ‘cv2‘.

– Applied random combinations of augmentations using ‘imgaug‘ to generate diverse dataset samples.

– Saved augmented images and updated annotations to facilitate training of computer vision models.