Analyze images and videos using Azure AI Vision, custom models, and Video Indexer.
Encompasses analyzing images including selecting visual features, detecting objects, generating tags, extracting and converting text using Azure Vision in Foundry Tools. Covers implementing custom vision models including image classification and object detection with proper labeling, training, evaluation, and deployment. Also includes analyzing videos using Azure AI Video Indexer and spatial analysis for detecting presence and movement.
5 minutes
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Implementing computer vision solutions in Azure involves leveraging Azure AI Vision services to analyze, process, and extract meaningful information from images and videos. As an Azure AI Engineer, you need to understand several key components and services.
**Azure AI Vision Service** is the primary platform for computer vision tasks. It provides pre-built models for image analysis, including object detection, image classification, facial recognition, and optical character recognition (OCR). You can access these capabilities through REST APIs or SDKs.
**Key Capabilities Include:**
1. **Image Analysis**: Extract visual features like tags, descriptions, categories, and dominant colors from images. The service can identify objects, landmarks, and celebrities.
2. **OCR and Read API**: Extract printed and handwritten text from images and documents. The Read API handles multi-page documents and supports multiple languages.
3. **Face API**: Detect human faces, analyze facial attributes like age and emotion, and perform face verification and identification.
4. **Custom Vision**: Train custom image classification and object detection models using your own labeled data when pre-built models do not meet specific requirements.
5. **Video Analysis**: Process video content for spatial analysis, people counting, and movement tracking using Video Indexer.
**Implementation Steps:**
- Create an Azure AI Vision resource in Azure Portal
- Obtain endpoint URL and subscription key
- Integrate using REST API calls or Azure SDKs (Python, C#, JavaScript)
- Process responses containing analyzed data in JSON format
**Best Practices:**
- Implement proper error handling and retry logic
- Consider rate limits and pricing tiers
- Use appropriate image formats and sizes for optimal results
- Secure API keys using Azure Key Vault
- Monitor usage through Azure Monitor
Understanding these components enables you to build intelligent applications that can interpret visual content, automate document processing, enhance accessibility features, and create innovative solutions across various industries including healthcare, retail, and manufacturing.Implementing computer vision solutions in Azure involves leveraging Azure AI Vision services to analyze, process, and extract meaningful information from images and videos. As an Azure AI Engineer, you need to understand several key components and services.
**Azure AI Vision Service** is the prima…