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recipe: promptable content moderation (#249)
* recipe: promptable content moderation * typo * readme * readme * readme * readme * remove unused imports * black format * updated readme * remove hf space logic * black format
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| # Python | ||
| __pycache__/ | ||
| *.py[cod] | ||
| *$py.class | ||
| *.so | ||
| .Python | ||
| build/ | ||
| develop-eggs/ | ||
| dist/ | ||
| downloads/ | ||
| eggs/ | ||
| .eggs/ | ||
| lib/ | ||
| lib64/ | ||
| parts/ | ||
| sdist/ | ||
| var/ | ||
| wheels/ | ||
| *.egg-info/ | ||
| .installed.cfg | ||
| *.egg | ||
| *.dll | ||
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| # Virtual Environment | ||
| venv/ | ||
| env/ | ||
| ENV/ | ||
| .venv/ | ||
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| # IDE | ||
| .idea/ | ||
| .vscode/ | ||
| *.swp | ||
| *.swo | ||
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| # Project specific | ||
| inputs/* | ||
| outputs/* | ||
| !inputs/.gitkeep | ||
| !outputs/.gitkeep | ||
| inputs/ | ||
| outputs/ | ||
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| # Model files | ||
| *.pth | ||
| *.onnx | ||
| *.pt | ||
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| # Logs | ||
| *.log | ||
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| certificate.pem |
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| # Promptable Content Moderation with Moondream | ||
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| Welcome to the future of content moderation with Moondream 2B, a powerful and lightweight vision-language model that enables detection and moderation of video content using natural language prompts. | ||
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| [Try it now.](https://huggingface.co/spaces/moondream/content-moderation) | ||
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| ## Features | ||
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| - Content moderation through natural language prompts | ||
| - Multiple visualization styles | ||
| - Intelligent scene detection and tracking: | ||
| - DeepSORT tracking with scene-aware reset | ||
| - Persistent moderation across frames | ||
| - Smart tracker reset at scene boundaries | ||
| - Optional grid-based detection for improved accuracy on complex scenes | ||
| - Frame-by-frame processing with IoU-based merging | ||
| - Web-compatible output format | ||
| - Test mode (process only first X seconds) | ||
| - Advanced moderation analysis with multiple visualization plots | ||
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| ## Examples | ||
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| | Prompt | Output | | ||
| |--------|-----------------| | ||
| | "white cigarette" |  | | ||
| | "gun" |  | | ||
| | "confederate flag" |  | | ||
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| ## Requirements | ||
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| ### Python Dependencies | ||
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| For Windows users, before installing other requirements, first install PyTorch with CUDA support: | ||
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| ```bash | ||
| pip install torch==2.5.1+cu121 torchvision==0.20.1+cu121 --index-url https://download.pytorch.org/whl/cu121 | ||
| ``` | ||
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| Then install the remaining dependencies: | ||
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| ```bash | ||
| pip install -r requirements.txt | ||
| ``` | ||
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| ### System Requirements | ||
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| - FFmpeg (required for video processing) | ||
| - libvips (required for image processing) | ||
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| Installation by platform: | ||
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| - Ubuntu/Debian: `sudo apt-get install ffmpeg libvips` | ||
| - macOS: `brew install ffmpeg libvips` | ||
| - Windows: | ||
| - Download FFmpeg from [ffmpeg.org](https://ffmpeg.org/download.html) | ||
| - Follow [libvips Windows installation guide](https://docs.moondream.ai/quick-start) | ||
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| ## Installation | ||
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| 1. Clone this repository and create a new virtual environment: | ||
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| ```bash | ||
| git clone https://github.com/vikhyat/moondream/blob/main/recipes/promptable-video-redaction | ||
| python -m venv .venv | ||
| source .venv/bin/activate # On Windows: .venv\Scripts\activate | ||
| ``` | ||
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| 2. Install Python dependencies: | ||
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| ```bash | ||
| pip install -r requirements.txt | ||
| ``` | ||
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| 3. Install ffmpeg and libvips: | ||
| - On Ubuntu/Debian: `sudo apt-get install ffmpeg libvips` | ||
| - On macOS: `brew install ffmpeg` | ||
| - On Windows: Download from [ffmpeg.org](https://ffmpeg.org/download.html) | ||
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| > Downloading libvips for Windows requires some additional steps, see [here](https://docs.moondream.ai/quick-start) | ||
| ## Usage | ||
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| The easiest way to use this tool is through its web interface, which provides a user-friendly experience for video content moderation. | ||
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| ### Web Interface | ||
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| 1. Start the web interface: | ||
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| ```bash | ||
| python app.py | ||
| ``` | ||
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| 2. Open the provided URL in your browser (typically <http://localhost:7860>) | ||
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| 3. Use the interface to: | ||
| - Upload your video file | ||
| - Specify content to moderate (e.g., "face", "cigarette", "gun") | ||
| - Choose redaction style (default: obfuscated-pixel) | ||
| - OPTIONAL: Configure advanced settings | ||
| - Processing speed/quality | ||
| - Grid size for detection | ||
| - Test mode for quick validation (default: on, 3 seconds) | ||
| - Process the video and download results | ||
| - Analyze detection patterns with visualization tools | ||
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| ## Output Files | ||
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| The tool generates two types of output files in the `outputs` directory: | ||
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| 1. Processed Videos: | ||
| - Format: `[style]_[content_type]_[original_filename].mp4` | ||
| - Example: `censor_inappropriate_video.mp4` | ||
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| 2. Detection Data: | ||
| - Format: `[style]_[content_type]_[original_filename]_detections.json` | ||
| - Contains frame-by-frame detection information | ||
| - Used for visualization and analysis | ||
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| ## Technical Details | ||
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| ### Scene Detection and Tracking | ||
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| The tool uses advanced scene detection and object tracking: | ||
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| 1. Scene Detection: | ||
| - Powered by PySceneDetect's ContentDetector | ||
| - Automatically identifies scene changes in videos | ||
| - Configurable detection threshold (default: 30.0) | ||
| - Helps maintain tracking accuracy across scene boundaries | ||
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| 2. Object Tracking: | ||
| - DeepSORT tracking for consistent object identification | ||
| - Automatic tracker reset at scene changes | ||
| - Maintains object identity within scenes | ||
| - Prevents tracking errors across scene boundaries | ||
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| 3. Integration Benefits: | ||
| - More accurate object tracking | ||
| - Better handling of scene transitions | ||
| - Reduced false positives in tracking | ||
| - Improved tracking consistency | ||
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| ## Best Practices | ||
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| - Use test mode for initial configuration | ||
| - Enable grid-based detection for complex scenes | ||
| - Choose appropriate redaction style based on content type: | ||
| - Censor: Complete content blocking | ||
| - Blur styles: Less intrusive moderation | ||
| - Bounding Box: Content review and analysis | ||
| - Monitor system resources during processing | ||
| - Use appropriate processing quality settings based on your needs | ||
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| ## Notes | ||
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| - Processing time depends on video length, resolution, GPU availability, and chosen settings | ||
| - GPU is strongly recommended for faster processing | ||
| - Grid-based detection increases accuracy but requires more processing time (each grid cell is processed independently) | ||
| - Test mode processes only first X seconds (default: 3 seconds) for quick validation |
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