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An intuitive Python tool for annotating images with bounding boxes. Easily assign custom classes to objects and save annotations. Includes AI model integration for automated annotation. Perfect for streamlining computer vision projects. classes to these objects, and save annotations.

License: MIT License

Jupyter Notebook 98.25% Python 1.75%
ai autodistill autodistill-grounding-dino deep-learning grounding-dino image-annotation-tool opencv tkinter-gui

ai-image-annotation's Introduction

AI Image Annotator ๐Ÿ–ผ๏ธ

The AI Image Annotator is a Python application built with tkinter for annotating images with bounding boxes representing objects detected in the images. It also provides functionality to annotate images using an AI model for object detection.

Features โœจ

  • Load and display images for annotation.
  • Draw bounding boxes around objects in images.
  • Annotate images with custom classes and colors.
  • Annotate images manually or with the assistance of an AI model.
  • Save annotations in YOLO Format.

Requirements ๐Ÿ› ๏ธ

  • Python 3.x
  • tkinter
  • OpenCV (cv2)
  • Pillow (PIL)
  • matplotlib
  • autodistill
  • autodistill_grounding_dino

Usage ๐Ÿš€

  1. Clone the repository:
git clone https://github.com/Marinto-Richee/Image-annotation.git
  1. Navigate to the project directory:
cd image-annotator
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Run the application:
python image_annotator.py
  1. Load images by clicking on the "Load Images" button.
  2. Draw bounding boxes around objects in the images by clicking and dragging the mouse.
  3. Choose classes from the "Annotation Classes" section to assign to the bounding boxes.
  4. Optionally, use the "AI Annotate" button to annotate images with the assistance of an AI model.
  5. Save annotations using the "Save Annotations" button.

Screenshots ๐Ÿ“ธ

image image

image image

License ๐Ÿ“

This project is licensed under the MIT License. See the LICENSE file for details.

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