Processing customized video data with SegAnyMo mainly consists of the following steps:
1. Data preparation:
- Organize video or image sequences in a standard directory structure
- If it is a video file, you need to use a tool (such as FFmpeg) to extract the frames to the images folder first.
- Ensure that the file name format meets the requirements (SAM2 requires plain numerically named .jpg files)
2. Operational pre-processing:
- Processing data using the run_inference.py script
- The -e parameter can be added to enable high-efficiency mode to reduce the processing load.
- Control the sampling rate with the -step parameter (the smaller the value, the higher the accuracy).
3. Trajectory forecasting:
- Download pre-trained model weights from official sources
- Modify the path parameter in the configuration file configs/example_train.yaml
- Run the motion trajectory prediction script
4. Generation of masks:
- Calling the SAM2 module for mask refinement
- Specify input path and output path parameters
- Check the generated segmentation results
The whole process may take a long time (the longer the video the longer the processing time) and it is recommended to execute it on a GPU device with sufficient performance. If formatting problems are encountered, they can be solved by modifying the source code or renaming the files. A detailed description of the command line parameters is provided in the project documentation.
This answer comes from the articleSegAnyMo: open source tool to automatically segment arbitrary moving objects from videoThe
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