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https://github.com/shamspias/videolabelmagic
Automate video data creation. Extract frames, generate annotations, export in various formats (YOLOv8, YOLO1.1). Integrate with Roboflow, CVAT. Built with Python and Streamlit.
https://github.com/shamspias/videolabelmagic
annotation-tool artificial-intelligence auto-annotation auto-labeling auto-segmentation computer-vision computer-vision-annotation data-processing dataset-generation image-annotation-tool image-labeling-tool labeling-tool machine-learning object-detection python small-object-detection streamlit video-annotation yolo yolov11
Last synced: 14 days ago
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Automate video data creation. Extract frames, generate annotations, export in various formats (YOLOv8, YOLO1.1). Integrate with Roboflow, CVAT. Built with Python and Streamlit.
- Host: GitHub
- URL: https://github.com/shamspias/videolabelmagic
- Owner: shamspias
- License: mit
- Created: 2024-09-03T12:24:39.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-16T06:12:25.000Z (5 months ago)
- Last Synced: 2024-12-04T12:17:34.043Z (2 months ago)
- Topics: annotation-tool, artificial-intelligence, auto-annotation, auto-labeling, auto-segmentation, computer-vision, computer-vision-annotation, data-processing, dataset-generation, image-annotation-tool, image-labeling-tool, labeling-tool, machine-learning, object-detection, python, small-object-detection, streamlit, video-annotation, yolo, yolov11
- Language: Python
- Homepage:
- Size: 79.1 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# VideoLabelMagic
VideoLabelMagic is a Streamlit-based application tailored for researchers and developers in the computer vision field. It simplifies the process of extracting frames from videos, applying object detection using various models like YOLO, NAS, and RTDETR, and annotating these frames to generate training data.
## Features
- **Video Upload**: Upload video files via the web interface.
- **Model Selection**: Utilize pre-trained models for object detection, with support for multiple models such as YOLO, NAS, and RTDETR.
- **Multi-Model Operation**: Configure and run multiple detection models simultaneously to leverage their strengths in diverse scenarios.
- **Frame Rate Control**: Adjust the frame rate for extracting images from the video.
- **Dynamic Class Configuration**: Use YAML files to define and utilize different class configurations for object detection.
- **Output Customization**: Configure output directories for storing extracted frames and annotations.
- **Transformation Options**: Apply transformations such as resizing, converting to grayscale, or rotating frames.
- **Flexible Storage**: Choose between local file system or cloud-based object storage for input/output operations.
- **SAHI Integration**: Use SAHI for sliced predictions, allowing efficient handling of large or complex images.
## Usage
1. **Starting the Application**:
- Launch the application and access it via `http://localhost:8501` on your browser.
2. **Uploading and Configuring**:
- Upload a video file or select one from the configured object storage.
- Choose one or multiple detection models, class configuration, and specify the output directory and frame rate.
- Select desired transformations for the frames to be processed.
3. **Processing**:
- Click "Extract Frames" to start the frame extraction and annotation process.
- Once processing completes, the outputs can be found in the specified directory or uploaded to cloud storage.
4. **Viewing Results**:
- Access extracted images and annotations directly from the output directory or your cloud storage interface.
## Creating Class Configuration Files
To customize object detection classes, you need to create a YAML file specifying each class and its corresponding ID.
Here's how to set up your YAML file for dynamic class configuration:
1. **File Format**: Each class entry should contain an `id` and `name`. For example:
```yaml
classes:
- id: 0
name: person
- id: 1
name: car
- id: 2
name: truck
```
2. **Saving the File**: Save the file with a `.yaml` extension in the `object_class/` directory.
3. **Using in Application**: When running the application, select your new class configuration file from the dropdown menu.
### Prerequisites
- Python 3.11+
- Pipenv or virtualenv (recommended for package management)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/shamspias/VideoLabelMagic.git
cd VideoLabelMagic
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit application:
```bash
streamlit run app/main.py
```
## Contributing
Contributions to VideoLabelMagic are welcome! Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to make contributions.
## License
Distributed under the MIT License. See [LICENSE](LICENSE) for more information.
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