Claude Vision Object Detection
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Claude Vision Object Detection
Overview :
Claude Vision Object Detection is a Python-based tool that utilizes the Claude 3.5 Sonnet Vision API to detect objects in images and visualize them. This tool automatically draws bounding boxes around detected objects, labels them, and displays confidence scores. It supports processing either single images or entire directories, providing high-precision confidence scores and using vibrant, distinct colors for each detected object. Additionally, it saves annotated images with the detection results.
Target Users :
This tool is aimed at developers and researchers who need to perform image object detection and visualization. With its high precision object detection capabilities and user-friendly interface, it is suitable for users who require quick and accurate extraction of information from images, applicable in areas like computer vision, security monitoring, and content moderation.
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Use Cases
Using this tool for real-time object detection on images captured by surveillance cameras
Automatically tagging and filtering inappropriate image content in content moderation
Tracking and analyzing specific objects in scientific research.
Features
Process single images or entire directories of images
Automatic object detection with bounding box drawing
High-precision confidence scores
Use vibrant and distinct colors for each detected object
Save annotated images with detection results
Support for JPEG, PNG, GIF, and WebP image formats
Comprehensive error handling for invalid image paths, unsupported file formats, API communication issues, and image processing errors.
How to Use
1. Clone the repository locally: git clone https://github.com/doriandarko/claude-vision-object-detection.git
2. Navigate to the project directory: cd claude-vision-detection
3. Install the required Python packages: pip install -r requirements.txt
4. Create a .env file in the project root and add your Anthropic API key: ANTHROPIC_API_KEY=your_api_key_here
5. Run the script: python main.py
6. Follow the prompts to input the path of a single image file or the directory containing multiple images.
7. The script will process each image, using the Claude Vision API to draw bounding boxes, add labels, and include confidence scores, saving annotated images to the output directory.
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