Add VodKeeper UI application with comprehensive video processing workflow
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@@ -8,21 +8,62 @@ VodKeeper is designed to streamline video processing workflows by providing a si
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## Features
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- Unified UI for video processing workflows
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- Integration with existing mux workflows
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- Streamlined video management
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- Automated processing pipelines
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## Development
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This project is currently in initial development phase.
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- **Input Tab**: File selection, encoding settings (codec, preset, CQ), bitrate management, preview encoding
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- **Render Tab**: Progress monitoring, queue management, live FFmpeg output, pause/stop controls
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- **QC Tab**: Quality control checks, frame-by-frame comparison, visual verification
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## Setup
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1. Clone the repository
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2. Install dependencies (TBD)
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3. Configure video source paths
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4. Run the application
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1. **Install Python Dependencies**:
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```bash
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pip install -r requirements.txt
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```
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2. **Install FFmpeg** (if not already installed):
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- Windows: Download from https://ffmpeg.org/download.html
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- Or use: `winget install ffmpeg`
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3. **Configure Paths**:
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- Default input: `R:\mux\input`
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- Default output: `R:\mux\output`
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- Default logs: `R:\mux\logs`
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4. **Run the Application**:
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```bash
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python run.py
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# or
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python vodkeeper.py
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```
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## Usage
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### Input Tab
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- Browse and select input directory
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- Choose encoding settings (h265_nvenc, vp9, av1)
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- Set preset (p1-p7, default p7)
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- Configure CQ value (0-51, default 0)
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- Auto-detect or manually set bitrate cap
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- Preview encode (1 minute test)
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### Render Tab
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- Add files to render queue
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- Monitor progress with live FFmpeg output
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- Pause/stop rendering operations
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- View queue status and progress
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### QC Tab
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- Select source and output files for comparison
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- Run quality control checks
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- Compare frames side-by-side
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- Export QC reports
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## Development
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This project uses:
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- **Tkinter** for the UI
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- **ffmpeg-python** for video processing
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- **OpenCV** for frame analysis
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- **Pillow** for image handling
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## License
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