ObjectVision Documentation
Comprehensive documentation platform for an advanced object detection and analysis system. Built with modern technologies and best practices for software documentation.
Next Js
The React Framework for the Web
Fast API
Python framework for building APIs
Documentation Framework
ObjectVision features comprehensive documentation across both frontend and backend systems, with interactive examples and thorough API references.
Advanced search capabilities across all documentation with real-time results and syntax highlighting.
Step-by-step tutorials with interactive code examples that can be run directly in the browser.
Comprehensive API documentation with request/response examples and schema validation.
Documentation versioned in sync with software releases to ensure historical accuracy.
Secure authentication flows documented with security best practices and implementation guides.
Detailed performance documentation with benchmarks and optimization recommendations.
System Architecture
Explore the comprehensive architecture documentation for ObjectVision's frontend and backend systems.
Our frontend documentation covers all aspects of the ObjectVision UI, including:
- Component API: Detailed props, methods, and event documentation for all reusable components
- State Management: Implementation details for global and local state using React Context and hooks
- TypeScript Interfaces: Complete type definitions for all data structures and API responses
- Interactive Examples: Live code sandboxes for testing component behavior
TypeDoc Generated App References
Automatically generated interface documentation using TypeDoc, ensuring always up-to-date App references.
Performance Optimization Guide
Detailed documentation on component memoization, code splitting, and lazy loading strategies implemented in the app.
Accessibility Compliance
Comprehensive guidelines for ARIA attributes, keyboard navigation, and screen reader testing results for all components.
Object Vision API Reference
Interactive documentation for our computer vision endpoints with real-time testing capabilities.
Request Body
{
file: (binary),
client_id: "your-client-id",
parameters: {
"model_size": "OV-model-M",
"confidence_threshold": 0.25,
"service_requests": ["detection", "segmentation"]
},
metadata: {
"mime-type": "image/png",
"size": 1458396,
"width: 1024,
"height": 720
}
}Parameters
- model_sizePre-trained model to use (i.e. OV-model-M, OV-model-L etc)
- confidence_thresholdMinimum confidence score (0.0-1.0) for detections
- service_requestsList of requested services (i.e. detection, pose etc)
This is just demo data, for actual requests and responses refer to the documents.
Response
{
file_name: "pexels-jopwell-2422290.webp"
file_path: "uploads/images/pexels-jopwell-2422290.webp"
timestamp: "2025-03-15T14:32:18Z",
detections: [
{
"class_id": 0,
"class_name": "person",
"confidence": 0.9632,
"bbox": [0.345, 0.211, 0.512, 0.876],
"bbox_pixels": [124, 76, 184, 315],
"attributes": {
"pose": "standing",
"facing": "front"
}
},
{
"class_id": 2,
"class_name": "car",
"confidence": 0.8814,
"bbox": [0.125, 0.723, 0.342, 0.892],
"bbox_pixels": [45, 260, 123, 321]
}
]
}