In this repo, we expand Segment Anything Model (SAM) to support text prompt input. The text prompt could be object-level:full_moon: (eg, dog) and part-level:last_quarter_moon: (eg, dog head). Furthermore，we build a Visual ChatGPT-based dialogue system 🤖💬 that flexibly calls various segmentation models when receiving instructions in the form of natural language.
- 2023/04/14: Edit anything at more fine-grained part-level.
- 2023/04/11: Initial code release.
Part Prompt: "dog body"; Edit Prompt: "zebra"
Part Prompt: "cat head"; Edit Prompt: "tiger"
Part Prompt: "chair seat"; Edit Prompt: "cholocate"
Part Prompt: "person head"; Edit Prompt: "combover hairstyle"
Beyond class-agnostic mask segmentation, this repo contains:
- Grounded segment anything at both object level and part level.
- Interacting with models in the form of natural language.
These abilities come from a series of models, including:
| Model | Function |
|---|---|
| Segment Anything | Segment anything from prompt |
| GLIP | Grounded language-image pre-training |
| Visual ChatGPT | Connects ChatGPT and segmentation foundation models |
| ⭐VLPart⭐ | Going denser with open-vocabulary part segmentation |
Q: When will VLPart paper be released ?
A: VLPart paper has been released. 🚀🚀🚀
Q: What is the difference between Grounded SAM and this project ?
A: Grounded SAM is Grounded DINO + SAM, and this project is GLIP/VLPart + SAM. We believe any open-vocabulary (text prompt) object detection model can be used to combine with SAM.
See installation instructions.
python demo_part_edit.py# prepare your private OpenAI key (for Linux)
export OPENAI_API_KEY={Your_Private_Openai_Key}
python chatbot.py --load "ImageCaptioning_cuda:0, SegmentAnything_cuda:1, PartPromptSegmentAnything_cuda:1, ObjectPromptSegmentAnything_cuda:0"
wget https://github.com/Cheems-Seminar/grounded-segment-any-parts/releases/download/v1.0/swinbase_part_0a0000.pth
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
python demo_vlpart_sam.py --input_image assets/twodogs.jpeg --output_dir outputs_demo --text_prompt "dog head"Result:
wget https://github.com/Cheems-Seminar/grounded-segment-any-parts/releases/download/v1.0/glip_large.pth
python demo_glip_sam.py --input_image assets/demo2.jpeg --output_dir outputs_demo --text_prompt "frog"
Result:
For multiple prompts, seperate each prompt with ., for example, --text_prompt "dog head. dog nose"
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.
A large part of the code is borrowed from segment-anything, EditAnything, CLIP, GLIP, Grounded-Segment-Anything, Visual ChatGPT. Many thanks for their wonderful works.
If you find this project helpful for your research, please consider citing the following BibTeX entry.
@misc{segrec2023,
title = {Grounded Segment Anything: From Objects to Parts},
author = {Sun, Peize and Chen, Shoufa and Luo, Ping},
howpublished = {\url{https://github.com/Cheems-Seminar/grounded-segment-any-parts}},
year = {2023}
}
@article{vlpart2023,
title = {Going Denser with Open-Vocabulary Part Segmentation},
author = {Sun, Peize and Chen, Shoufa and Zhu, Chenchen and Xiao, Fanyi and Luo, Ping and Xie, Saining and Yan, Zhicheng},
journal = {arXiv preprint arXiv:2305.11173},
year = {2023}
}