Annotate Images Like a Pro: Python Image Annotation Tool Walkthrough

Image Annotation Made Easy with DigitalSreeni's Python Tool In this video, I walk you through my Python-based image annotation application and its associated tools, providing a step-by-step demo to help you get started. Topics Covered: --Installation of the Python library for image annotation, along with setting up Anaconda and configuring your environment. - Creating new projects and adding 2D and multi-dimensional images (TIFF, CZI). - Manual annotation of 2D images and slices from multi-dimensional images using polygon and rectangle tools. - Semi-automatic annotations with the Segment Anything Model (SAM). - Renaming and assigning colors to classes for better organization. - Exporting annotations to various formats: COCO JSON, YOLO v8, labeled images, semantic images, Pascal VOC bounding boxes. - Verifying exported annotations by reloading them into the program. Additional Tools: - Annotation statistics - Combining JSON annotations - Data splitting - Patch extraction - Data augmentation of images and annotations Links: GitHub repository: https://github.com/bnsreenu/digitalsreeni-image-annotator PyPI for pip install info: https://pypi.org/project/digitalsreeni-image-annotator To Install: pip install digitalsreeni-image-annotator Once installed, simply type sreeni in your command prompt within the correct environment to launch the application. You can download SAM models from the following links. Please be cautious about the large model on systems with limited memory. https://github.com/ultralytics/assets/releases/download/v8.2.0/sam2_t.pt https://github.com/ultralytics/assets/releases/download/v8.2.0/sam2_s.pt https://github.com/ultralytics/assets/releases/download/v8.2.0/sam2_b.pt https://github.com/ultralytics/assets/releases/download/v8.2.0/sam2_l.pt It is recommended to place the SAM models in a directory from where you normally start the application to avoid multiple downloads of the same models from the Ultralytics server.