A proof of concept (PoC) to demonstrate the integration of .NET Core and TensorFlow for real-time image recognition.
This project showcases the power of .NET Core combined with TensorFlow to perform real-time image recognition. Using TensorFlow's model inference capabilities, the PoC allows users to process images and identify objects in real-time.
The implementation is inspired by Paulo Torres' Medium article, with enhancements to demonstrate practical use cases.
graph TD
A[Input Device Camera/Local Images] -->|Image Data| B[.NET Core App]
B -->|Pre-Processing| C[TensorFlow Model]
C -->|Model Inference| D[Recognition Results]
D -->|Output| E[Display/Logging]
- Real-Time Image Processing: Capture images from a live camera feed or load pre-saved images.
- TensorFlow Integration: Leverage TensorFlow models for accurate object detection.
- Platform Independence: Built using .NET Core for cross-platform support.
- Extensible Design: Easy to integrate with different TensorFlow models and extend for additional functionality.
- Real-Time Image Recognition with .NET Core and TensorFlow (Medium Article)
- TensorFlow Official Documentation
- .NET Core Documentation
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Clone the repository:
git clone https://github.com/your-repository-url.git cd your-repository-folder -
Install prerequisites:
- Ensure you have .NET Core SDK installed (Download).
- Install TensorFlow runtime for your platform.
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Restore dependencies:
dotnet restore
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Build the project:
dotnet build
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Run the application:
dotnet run
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Usage:
- Provide an image input (via file or live camera feed).
- Observe real-time recognition results in the console or UI.
This project is licensed under the MIT License. See the LICENSE file for details.