ObjectRemove¶

class agentlego.tools.ObjectRemove(sam_model='sam_vit_h_4b8939.pth', grounding_model='glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365', device='cuda', toolmeta=None)[source]

A tool to remove the certain objects in the image.

Parameters:

sam_model (str) – The model name used to inference. Which can be found in the segment_anything repository. Defaults to sam_vit_h_4b8939.pth.
grounding_model (str) – The model name used to inference. Which can be found in the MMdetection repository. Defaults to glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.
device (str) – The device to load the model. Defaults to ‘cuda’.
toolmeta (None | dict | ToolMeta) – The additional info of the tool. Defaults to None.

Default Tool Meta¶

name: ObjectRemove
description: This tool can remove the specified object in the image.
inputs:
- image (ImageIO)
- text (str): The object to remove.
outputs:
- ImageIO

Example¶

Use the tool directly (without agent)

from agentlego.apis import load_tool

# load tool
tool = load_tool('ObjectRemove', device='cuda')

# apply tool
image = tool('examples/demo.png','dog')
print(image)

With Lagent

from lagent import ReAct, GPTAPI, ActionExecutor
from agentlego.apis import load_tool

# load tools and build agent
# please set `OPENAI_API_KEY` in your environment variable.
tool = load_tool('ObjectRemove', device='cuda').to_lagent()
agent = ReAct(GPTAPI(temperature=0.), action_executor=ActionExecutor([tool]))

# agent running with the tool.
img_path = 'examples/demo.png'
ret = agent.chat(f'According to the image `{img_path}`, remove the dog in the image.')
for step in ret.inner_steps[1:]:
    print('------')
    print(step['content'])

Set up¶

Before using this tool, please confirm you have installed the related dependencies by the below commands.

pip install -U diffusers
pip install -U segment_anything
pip install -U openmim
mim install -U mmdet

Reference¶

This tool uses SAM, Stable Diffusion and GLIP in default settings. See the following papers for details.

@article{kirillov2023segany,
  title={Segment Anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

@InProceedings{Rombach_2022_CVPR,
    author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
    title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {10684-10695}
}

@article{zhang2022glipv2,
  title={GLIPv2: Unifying Localization and Vision-Language Understanding},
  author={Zhang, Haotian* and Zhang, Pengchuan* and Hu, Xiaowei and Chen, Yen-Chun and Li, Liunian Harold and Dai, Xiyang and Wang, Lijuan and Yuan, Lu and Hwang, Jenq-Neng and Gao, Jianfeng},
  journal={arXiv preprint arXiv:2206.05836},
  year={2022}
}