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Autoseg SAM2
Overview :
AutoSeg-SAM2 is an automatic full video segmentation tool based on Segment Anything 2 (SAM2) and Segment Anything 1 (SAM1). It enables tracking of each object in the video while detecting potential new objects. The tool's significance lies in providing static segmentation results and leveraging SAM2 to track these results, which is crucial for video content analysis, object detection, and video editing. This product was developed by zrporz, based on Facebook Research's SAM2 and zrporz's own SAM1. As an open-source project, it is available for free.
Target Users :
The target audience mainly includes video content analysis experts, video editors, computer vision researchers, and developers. This tool is well-suited for them as it offers an automated way to process and analyze video content, saving significant time on manual editing and analysis while enhancing accuracy and efficiency.
Use Cases
Video surveillance analysis: Use AutoSeg-SAM2 to automatically segment and track objects in surveillance videos to identify and analyze activities in specific areas.
Film post-production: In filmmaking, use this tool to automatically segment and track actors for easier effects addition and scene editing.
Scientific research: In animal behavior studies, use AutoSeg-SAM2 to track and analyze animals' behavior patterns in their natural environments.
Features
Automatic full video segmentation: Capable of automatically segmenting the entire video, identifying, and tracking every object within it.
Object tracking: Utilizes SAM2 technology to track objects in the video for behavioral analysis.
New object detection: Able to identify potentially new objects appearing in the video, enhancing the capacity for content analysis.
Static segmentation results: Provides static segmentation results via SAM1, serving as a foundation for video analysis.
Open-source project: Being an open-source project, users can freely access and modify the code to suit different needs.
Easy installation and use: Offers detailed environment setup and data preparation guides, enabling users to get started quickly.
How to Use
1. Clone the repository and its submodules via SSH or HTTPS.
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2. Ensure that your Python environment is version 3.10 or above and that you have installed the specified versions of torch and torchvision.
3. Install the SAM1 and SAM2 modules by using pip to install the corresponding modules from the submodule.
4. Download the checkpoints for SAM1 and SAM2 by executing the 'bash download.sh' command in the checkpoints directory.
5. Prepare the video data by organizing video frame images according to the specified file structure.
6. Use the provided scripts or write your own scripts to run video segmentation and object tracking.
7. Analyze the results and proceed with further video content analysis or editing based on the segmentation and tracking results.
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