Developing a Windows Application with
Azure Vision Models
In one of my recent projects, I developed a Windows application for real-time video streaming and object detection called PortVideoStreamer. This project aimed to process video feeds from multiple cameras, capture images at regular intervals, and send them to an Azure Vision Model for object detection. The goal was to automate defect identification and improve quality control in a manufacturing setup.
The application required real-time processing of images from different cameras while keeping the user interface responsive. Running deep learning models locally was not an option due to hardware constraints, so we needed a cloud-based AI approach that could deliver high accuracy with minimal delay.
Approach and Implementation
1. Video Streaming & Image Capture
The application supports USB cameras and simulator mode (pre-recorded videos). I used AForge.NET for handling live camera feeds and Windows Media Player for simulator mode.
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USB Mode: Detects connected cameras and streams live video.
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Simulator Mode: Allows users to load video files and play/pause them.
To ensure efficient image capture, I implemented a timer-based mechanism that captures a frame from each video feed every second. These images are then passed to the detection model.
2. Sending Images to Azure Vision Model
Captured images are sent to an Azure-hosted detection model using an HTTP POST request. The API is expected to return:
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If no objects are detected: {"message": "No objects detected."}
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If objects are detected: The image itself, with metadata headers
i) X-Detection-Classes: List of detected objects
ii) X-Detection-Count: Number of detected objects
3. Displaying Detection Results in the UI
Each camera has a corresponding Textbox in the UI to display detected objects. When an image is processed, the detection results are extracted from the API response and displayed in the respective Textbox.
4. Storing and Organizing Captured Images
Captured images are stored in separate folders based on the camera source. Each image is saved with a timestamp-based filename, ensuring proper organization and easy retrieval for future analysis.
Challenges Faced in AI Integration
When building PortVideoStreamer, I encountered several key challenges:
1. Real-Time Processing of Video Streams
Processing multiple video feeds at once while keeping the application responsive was a major challenge.
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Solution: I used AForge.NET for handling live camera feeds and Windows Media Player for playing videos in simulator mode. The application also includes an efficient timer-based image capture mechanism.
2. Handling Detection API Responses
Azure’s detection API returns results in different formats depending on whether objects are detected.
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Solution: The application processes JSON responses dynamically and updates the UI with detection results.
3. UI Responsiveness & Multi-Threading
Processing multiple video streams and sending images to an external API can freeze the UI.
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Solution: Used asynchronous programming (async/await in C#) to prevent UI lag.
Results and Impact
By integrating Azure Vision Models, PortVideoStreamer achieved:
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Real-Time Object Detection – Images captured every second are processed instantly.
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Seamless API Communication – Optimized HTTP requests ensure low-latency detection.
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Scalable Video Handling – Supports both USB and simulator video modes.
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Enhanced UI Responsiveness – Asynchronous API calls prevent UI freezing.
Final Thoughts
Developing PortVideoStreamer reinforced the importance of efficient AI integration in Windows applications. Leveraging Azure Vision Models allowed for real-time quality inspection, improving detection accuracy while maintaining system performance.
As AI-powered vision systems become more widespread, seamless integration with local applications will be crucial for automated defect detection, surveillance, and industrial automation.
Author: Bhavya Pothala with AI assistance