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https://github.com/mlapaglia/openalprwebhookprocessor

An OpenALPR Webhook Processor for IP Camera Overlays
https://github.com/mlapaglia/openalprwebhookprocessor

angular angular-material asp-net-core docker license-plate-recognition machine-learning

Last synced: 3 months ago
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An OpenALPR Webhook Processor for IP Camera Overlays

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README 

          
# OpenALPR Webhook Processor

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A comprehensive license plate recognition management system that processes webhooks from OpenALPR web servers, manages IP cameras, and provides intelligent alerting capabilities.

Untitled

## 🎯 Core Features



### License Plate Processing

- **Real-time Processing**: Receives and processes license plate webhooks from OpenALPR web servers

- **Data Storage**: Stores plate numbers, vehicle descriptions, timestamps, and confidence scores

- **Image Management**: Handles full vehicle images and cropped license plate images

- **Statistics**: Tracks plate frequency and visit patterns



### Camera Management

- **Multi-Camera Support**: Manage multiple IP cameras with individual configurations

- **Manufacturer Support**: Compatible with Hikvision and Dahua camera systems

- **Live Overlay Updates**: Display detected license plates directly on camera video feeds

- **Automatic Day/Night Mode**: Schedule sunrise/sunset camera mode switching

- **Focus Control**: Automatic zoom and focus adjustments based on time of day

- **Camera Masking**: Define detection areas to improve accuracy



### Intelligent Alerting

- **Custom Alert Rules**: Set up alerts for specific license plates or patterns  

- **Multiple Notification Channels**: Push notifications, Pushover integration, and webhook forwarding

- **Real-time Notifications**: Instant alerts when monitored plates are detected



### Machine Learning Predictions

- **Visit Prediction**: Predict when specific license plates will next appear

- **Pattern Recognition**: Analyze historical data to identify visiting patterns

- **Batch Processing**: Handle multiple prediction requests simultaneously

- **Model Training**: Automatic background model retraining with latest data



## 📊 Management Interface



### Dashboard & Monitoring

- **Live Feed**: Real-time license plate detection updates

- **Search & Filter**: Find specific plates by number, date, or camera

- **Statistics View**: Plate frequency analysis and visit patterns

- **System Logs**: Monitor application activity and troubleshoot issues



### Configuration Management

- **User Management**: Multi-user support

- **Camera Configuration**: Set up overlay text, day/night schedules, and focus settings

- **Alert Configuration**: Configure notification preferences and alert rules

- **Ignore Lists**: Define plates to exclude from processing or alerts

- **Webhook Forwarding**: Send processed data to external systems



### Advanced Features

- **Two-Factor Authentication**: Enhanced security for user accounts

- **Data Enrichment**: Enhance plate data with additional information sources

- **Real-time Updates**: Live system status and connection monitoring

- **Debug Tools**: System diagnostics and scheduled job monitoring



## 🚀 Quick Start



### Command Line

```bash

dotnet ./OpenAlprWebhookProcessor.dll

```



### Docker

```bash

docker run -d \

  --name=openalprwebhookprocessor \

  --net=bridge \

  -v /host/path/:/app/config/ \

  -p 3859:80 \

  mlapaglia/openalprwebhookprocessor

```



## 📸 Screenshots



### License Plates Dashboard

image



### Camera Configuration

image



### Agent Settings

image



## 🎥 Demo Video

[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/GqafBPlDC7Q/0.jpg)](https://www.youtube.com/watch?v=GqafBPlDC7Q)



## 📦 Docker Hub

Available at: [mlapaglia/openalprwebhookprocessor](https://hub.docker.com/repository/docker/mlapaglia/openalprwebhookprocessor)



## 💾 Data Storage

The application requires persistent storage for:

- License plate database

- User settings and configurations  

- Machine learning models

- Camera snapshots and images



Mount a volume to `/app/config/` to persist data between container restarts.



## 🛠️ Development



### Prerequisites

- [.NET 9 SDK](https://dotnet.microsoft.com/download/dotnet/9.0)

- [Node.js 24+](https://nodejs.org/) and npm

- [Angular CLI](https://angular.io/cli): `npm install -g @angular/cli`



### Getting Started



#### Backend (ASP.NET Core)

1. Navigate to the server project directory:

   ```cmd

   cd OpenAlprWebhookProcessor.Server

   ```



2. Restore dependencies:

   ```cmd

   dotnet restore

   ```



3. Start the backend server:

   ```cmd

   dotnet run

   ```

   

   The backend will start at `https://localhost:5001`



#### Frontend (Angular)

1. Navigate to the client project directory:

   ```cmd

   cd openalprwebhookprocessor.client

   ```



2. Install dependencies:

   ```cmd

   npm install

   ```



3. Start the development server:

   ```cmd

   npm start

   ```

   

   The frontend will start at `https://localhost:4200` and automatically proxy API requests to the backend.



### Running Both Together

For the best development experience, run both the backend and frontend simultaneously:



1. **Terminal 1** - Start the backend:

   ```cmd

   cd OpenAlprWebhookProcessor.Server

   dotnet run

   ```



2. **Terminal 2** - Start the frontend:

   ```cmd

   cd openalprwebhookprocessor.client

   npm start

   ```



3. Open your browser to `https://localhost:4200`



### Running Tests

- **Backend Tests**: `dotnet test` (from the solution root)

- **Frontend Tests**: `npm test` (from the client directory)