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README.md 0 → 100644
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# SAMURAI
SAMURAI 基于SAM2的Zero-Shot视觉跟踪模型。
## 论文
`SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
`
- https://arxiv.org/abs/2411.11922
## 模型结构
<!-- 此处一句话简要介绍模型结构 -->
虽然 SAM2 模型在视频流上的目标分割效果非常出色，但是在精准的目标跟踪方面却略显粗糙，很难应对跟踪任务的复杂环境和各种遮挡、丢失等挑战。与此，SAMURAI 在 SAM2 模型基础上，通过结合时间运动线索和提出的运动感知记忆选择机制，有效地预测了对象运动并优化了掩码选择，在无需重新训练或微调的情况下实现了稳健且准确的跟踪。
<div align=center>
<img src="./doc/sam2.png"/>
<div >SAM2</div>
</div>
## 算法原理
SAMURAI 的算法原理如下图所示。主体结构设计遵循SAM2，并设计运动建模和记忆选择机制。\
运动建模（Motion Modeling）合并运动信息，有效地处理快速移动和遮挡的对象来增强 SAM2 的视觉跟踪准确性。\
运动感知的记忆选择机制（Memory Selection），该机制通过选择性存储相关框架来减少拥挤的场景中的错误，以运动和亲和力得分的混合来决定。
<div align=center>
<img src="./doc/overview.jpg"/>
<div >SAMURAI</div>
</div>
## 环境配置
```
mv samurai_pytorch samurai # 去框架名后缀
# docker的-v 路径、docker_name和imageID根据实际情况修改
# pip安装时如果出现下载慢可以尝试别的镜像源
```
### Docker（方法一）
<!-- 此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取docker镜像的地址与使用步骤 -->
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10 # 本镜像imageID为：0291c26699b0
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=16G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --network=host --name docker_name imageID bash
cd /your_code_path/samurai
cd sam2
pip install -e .
pip install -e ".[notebooks]"
cd ..
pip install -r requirements.txt
```
### Dockerfile（方法二）
<!-- 此处提供dockerfile的使用方法 -->
```
cd /your_code_path/samurai/docker
docker build --no-cache -t codestral:latest .
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=16G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --network=host --name docker_name imageID bash
cd /your_code_path/samurai
cd sam2
pip install -e .
pip install -e ".[notebooks]"
cd ..
pip install -r requirements.txt
```
### Anaconda（方法三）
<!-- 此处提供本地配置、编译的详细步骤，例如： -->
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```
DTK驱动: dtk24.04.3
python: python3.10
pytorch: 2.3.0
```
`Tips：以上DTK驱动、python、pytorch等DCU相关工具版本需要严格一一对应`
其它非深度学习库参照requirements.txt安装：
```
cd /your_code_path/samurai
cd sam2
pip install -e .
pip install -e ".[notebooks]"
cd ..
pip install -r requirements.txt
```
## 数据集
无
## 训练
无
## 推理
模型下载：
```
cd sam2/checkpoints
sh download_ckpts.sh
```
SAM2.1模型也可从 [SCNet](http://113.200.138.88:18080/aimodels/facebook) 上快速下载；
推理图片下载：
可以用 [GOT10k](http://113.200.138.88:18080/aidatasets/project-dependency/got10k) 的测试图片；\
也可以自行选择视频或者图片序列进行测试，只需要提供初始帧目标的坐标保存成txt即可，比如 data/GOT-10k_Test_000001，可供参考。
```
# 视频/图片序列
python scripts/demo.py --video_path <your_video.mp4 or your_frame_directory> --txt_path <path_to_first_frame_bbox.txt>
```
## result
<!-- 此处填算法效果测试图（包括输入、输出） -->
<div align=center>
<img src="./doc/GOT-10k_Test_000001.mp4" width=600/>
<div >推理结果</div>
</div>
### 精度
无。
<!-- | 加速卡 | lpips | clip sim |
| :-----| :----- | :---- |
| K100_AI | 0.115 | 0.977 | -->
<!-- | 单元格 | 单元格 | 单元格 | -->
## 应用场景
### 算法类别
<!-- 超出以上分类的类别命名也可参考此网址中的类别名：https://huggingface.co/ \ -->
`目标跟踪`
### 热点应用行业
<!-- 应用行业的填写需要做大量调研，从而为使用者提供专业、全面的推荐，除特殊算法，通常推荐数量>=3。 -->
`零售,制造,电商,医疗,教育`
## 源码仓库及问题反馈
<!-- - 此处填本项目gitlab地址 -->
- https://developer.sourcefind.cn/codes/modelzoo/samurai_pytorch
## 参考资料
- https://github.com/yangchris11/samurai
README_origin.md 0 → 100644
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<div align="center">
<img align="left" width="100" height="100" src="https://github.com/user-attachments/assets/1834fc25-42ef-4237-9feb-53a01c137e83" alt="">
# SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
[Cheng-Yen Yang](https://yangchris11.github.io), [Hsiang-Wei Huang](https://hsiangwei0903.github.io/), [Wenhao Chai](https://rese1f.github.io/), [Zhongyu Jiang](https://zhyjiang.github.io/#/), [Jenq-Neng Hwang](https://people.ece.uw.edu/hwang/)
[Information Processing Lab, University of Washington](https://ipl-uw.github.io/)
</div>
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/samurai-adapting-segment-anything-model-for-1/visual-object-tracking-on-lasot-ext)](https://paperswithcode.com/sota/visual-object-tracking-on-lasot-ext?p=samurai-adapting-segment-anything-model-for-1)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/samurai-adapting-segment-anything-model-for-1/visual-object-tracking-on-got-10k)](https://paperswithcode.com/sota/visual-object-tracking-on-got-10k?p=samurai-adapting-segment-anything-model-for-1)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/samurai-adapting-segment-anything-model-for-1/visual-object-tracking-on-needforspeed)](https://paperswithcode.com/sota/visual-object-tracking-on-needforspeed?p=samurai-adapting-segment-anything-model-for-1)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/samurai-adapting-segment-anything-model-for-1/visual-object-tracking-on-lasot)](https://paperswithcode.com/sota/visual-object-tracking-on-lasot?p=samurai-adapting-segment-anything-model-for-1)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/samurai-adapting-segment-anything-model-for-1/visual-object-tracking-on-otb-2015)](https://paperswithcode.com/sota/visual-object-tracking-on-otb-2015?p=samurai-adapting-segment-anything-model-for-1)
[[Arxiv]](https://arxiv.org/abs/2411.11922) [[Project Page]](https://yangchris11.github.io/samurai/) [[Raw Results]](https://drive.google.com/drive/folders/1ssiDmsC7mw5AiItYQG4poiR1JgRq305y?usp=sharing)
This repository is the official implementation of SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
https://github.com/user-attachments/assets/9d368ca7-2e9b-4fed-9da0-d2efbf620d88
All rights are reserved to the copyright owners (TM & © Universal (2019)). This clip is not intended for commercial use and is solely for academic demonstration in a research paper. Original source can be found [here](https://www.youtube.com/watch?v=cwUzUzpG8aM&t=4s).
## Getting Started
#### SAMURAI Installation
SAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://github.com/facebookresearch/sam2?tab=readme-ov-file) to install both PyTorch and TorchVision dependencies. You can install **the SAMURAI version** of SAM 2 on a GPU machine using:
```
cd sam2
pip install -e .
pip install -e ".[notebooks]"
```
Please see [INSTALL.md](https://github.com/facebookresearch/sam2/blob/main/INSTALL.md) from the original SAM 2 repository for FAQs on potential issues and solutions.
Install other requirements:
```
pip install matplotlib==3.7 tikzplotlib jpeg4py opencv-python lmdb pandas scipy loguru
```
#### SAM 2.1 Checkpoint Download
```
cd checkpoints && \
./download_ckpts.sh && \
cd ..
```
#### Data Preparation
Please prepare the data in the following format:
```
data/LaSOT
├── airplane/
│ ├── airplane-1/
│ │ ├── full_occlusion.txt
│ │ ├── groundtruth.txt
│ │ ├── img
│ │ ├── nlp.txt
│ │ └── out_of_view.txt
│ ├── airplane-2/
│ ├── airplane-3/
│ ├── ...
├── basketball
├── bear
├── bicycle
...
├── training_set.txt
└── testing_set.txt
```
#### Main Inference
```
python scripts/main_inference.py
```
## Demo on Custom Video
To run the demo with your custom video or frame directory, use the following examples:
**Note:** The `.txt` file contains a single line with the bounding box of the first frame in `x,y,w,h` format while the SAM 2 takes `x1,y1,x2,y2` format as bbox input.
### Input is Video File
```
python scripts/demo.py --video_path <your_video.mp4> --txt_path <path_to_first_frame_bbox.txt>
```
### Input is Frame Folder
```
# Only JPG images are supported
python scripts/demo.py --video_path <your_frame_directory> --txt_path <path_to_first_frame_bbox.txt>
```
## FAQs
**Question 1:** Does SAMURAI need training? [issue 34](https://github.com/yangchris11/samurai/issues/34)
**Answer 1:** Unlike real-life samurai, the proposed samurai do not require additional training. It is a zero-shot method, we directly use the weights from SAM 2.1 to conduct VOT experiments. The Kalman filter is used to estimate the current and future state (bounding box location and scale in our case) of a moving object based on measurements over time, it is a common approach that had been adopted in the field of tracking for a long time, which does not require any training. Please refer to code for more detail.
**Question 2:** Does SAMURAI support streaming input (e.g. webcam)?
**Answer 2:** Not yet. The existing code doesn't support live/streaming video as we inherit most of the codebase from the amazing SAM 2. Some discussion that you might be interested in: facebookresearch/sam2#90, facebookresearch/sam2#388 (comment).
**Question 3:** How to use SAMURAI in longer video?
**Answer 3:** See the discussion from sam2 https://github.com/facebookresearch/sam2/issues/264.
**Question 4:** How do you run the evaluation on the VOT benchmarks?
**Answer 4:** For LaSOT, LaSOT-ext, OTB, NFS please refer to the [issue 74](https://github.com/yangchris11/samurai/issues/74) for more details. For GOT-10k-test and TrackingNet, please refer to the official portal for submission.
## Acknowledgment
SAMURAI is built on top of [SAM 2](https://github.com/facebookresearch/sam2?tab=readme-ov-file) by Meta FAIR.
The VOT evaluation code is modifed from [VOT Toolkit](https://github.com/votchallenge/toolkit) by Luka Čehovin Zajc.
## Citation
Please consider citing our paper and the wonderful `SAM 2` if you found our work interesting and useful.
```
@article{ravi2024sam2,
title={SAM 2: Segment Anything in Images and Videos},
author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
journal={arXiv preprint arXiv:2408.00714},
url={https://arxiv.org/abs/2408.00714},
year={2024}
}
@misc{yang2024samurai,
title={SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory},
author={Cheng-Yen Yang and Hsiang-Wei Huang and Wenhao Chai and Zhongyu Jiang and Jenq-Neng Hwang},
year={2024},
eprint={2411.11922},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.11922},
}
```
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