MaddoScientisto
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Comprehensive XML documentation added to all public types, methods, and properties in AIFotoONLUS.Core. Project updated to generate and pack XML docs for NuGet consumers. README rewritten for clarity. Improves developer experience with rich IntelliSense and API docs.
65 lines
2.6 KiB
Markdown
65 lines
2.6 KiB
Markdown
# AIFotoONLUS Number Recognition Library
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This library provides a small, focused engine to detect and recognize numeric
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text (digits) in images using Darknet (YOLO) models via OpenCvSharp's DNN API.
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It is suitable for batch processing folders of images or individual files.
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Features
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- Detection network (Darknet/Yolo) to find candidate text regions.
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- Recognition network (Darknet/Yolo) to identify digits inside detected crops.
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- Single-file and directory-level processing APIs.
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- Parallel processing with per-thread network instances for throughput.
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- Diagnostic helpers to dump network output shapes and optionally save crop images.
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Basic usage
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1. Create a `ModelConfiguration` instance that points to your Darknet `.cfg`
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and `.weights` files for both detection and recognition networks, configure
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confidence and NMS thresholds and provide a list of number class labels.
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2. Create an instance of `NumberRecognitionEngine`:
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```csharp
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using var engine = new NumberRecognitionEngine(modelConfig, logger: null);
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```
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3. Process a single image:
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```csharp
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var result = engine.ProcessImage("/path/to/image.jpg");
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Console.WriteLine(result.Text);
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```
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4. Process a directory (parallelized):
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```csharp
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var results = await engine.ProcessDirectoryAsync("/path/to/images", recursive: false);
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foreach (var r in results) Console.WriteLine($"{r.FileName}: {r.Text}");
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```
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Configuration notes
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- `ModelConfiguration` controls model file paths, input sizes, thresholds and
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whether to save cropped images for diagnostics. Make sure the paths are
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accessible to the process and the model files match the expected network
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architectures.
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- The engine expects detection network outputs in the YOLO-style layout:
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`[cx, cy, w, h, objectness, class1, class2, ...]`.
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Threading & diagnostics
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- For directory/batch processing the engine creates per-thread Net instances
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so OpenCV forward calls can run concurrently. It also contains fallback
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logic that will perform processing with shared nets under a lock if needed.
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- When `EnableCropSaving` is enabled in configuration, each recognized crop is
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saved to `logs/crops` with a timestamp and optional context label to aid
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debugging false positives/negatives.
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Troubleshooting
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- If the engine returns no detections, verify the model files are correct and
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compatible with the expected output layout. Use
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`ProcessFileWithDiagnostics` to inspect output layer shapes.
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License & Notes
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This project is provided as-is. See repository for licensing information and
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for the model files distribution terms (models are usually not redistributed
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with code and must be obtained separately).
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