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| # Getting Started with Object Intelligence Platform | |
| Welcome to the Open-World Object Intelligence Platform. This platform unifies three distinct computer vision paradigms into a single modular architecture: | |
| 1. **Known Object Detection (RT-DETR)**: Ultra-fast, predictable detection for standard trained classes (COCO). | |
| 2. **Open-Vocabulary Discovery (YOLO-World)**: Zero-shot visual concept detection driven by text prompts. | |
| 3. **Specific Object Recognition (Visual Embeddings)**: Teachable feature embeddings to recognize individual physical objects (e.g. *My Cup*). | |
| --- | |
| ## π Quick Setup | |
| ### 1. Installation | |
| Ensure Python 3.9+ is installed, then run: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 2. Launching the Gradio Web Application & API | |
| To start the Gradio interface locally or prepare for Hugging Face Spaces deployment: | |
| ```bash | |
| python app.py | |
| ``` | |
| Open your browser at `http://localhost:7860`. | |
| --- | |
| ## π¦ Python SDK Usage | |
| ```python | |
| from object_intelligence import ObjectDetector | |
| import cv2 | |
| # Initialize unified detector | |
| detector = ObjectDetector() | |
| # Select mode: 'combined', 'known', 'open_vocabulary', 'specific' | |
| detector.set_mode("combined") | |
| # Enable Lock Mode if desired | |
| detector.lock("My Cup") | |
| # Read frame and run detection | |
| frame = cv2.imread("test.jpg") | |
| annotated_frame, detections = detector.detect_and_draw(frame) | |
| # Save result | |
| cv2.imwrite("output.jpg", annotated_frame) | |
| ``` | |
| --- | |
| ## π Lock Mode | |
| Lock Mode filters all candidate detections across all pipelines, rendering **ONLY** bounding boxes that match your specified target string. All non-matching detections are suppressed before drawing. | |