# pbaa : Prompt-Based Automatic Annotation
[](https://pypi.org/project/pbaa)
[](https://pypi.org/project/pbaa)
Easy inference implementation of [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) for
**Prompt-based automatic annotation**
-----
**Table of Contents**
- [Installation](#installation)
- [Usage](#usage)
- [Gradio](#gradio)
- [Demo](#demo)
- [License](#license)
- [Acknowledgements](#acknowledgements)
## Installation
### Docker (Recommend)
```console
git clone https://github.com/dh031200/pbaa.git
docker build docker -t pbaa:latest
docker run --gpus all -it --ipc=host -v `pwd`:/workspace -p 7860:7860 pbaa:latest
```
### Without docker
The code requires `python>=3.8`, `CUDA==11.7`.
```console
pip install pbaa
```
## Usage
### Options
```console
Usage: pbaa [OPTIONS]
Options:
--version Show the version and exit.
-s, --src TEXT Source image or directory path
-p, --prompt <TEXT TEXT>... Space-separated a pair of prompt and target
classe. (Multi)
-b, --box_threshold FLOAT Threshold for Object Detection (default: 0.25)
-n, --nms_threshold FLOAT Threshold for NMS (default: 0.8)
-o, --output_dir TEXT Path to result data (default: 'outputs')
-g, --gradio Launch gradio app
-h, --help Show this message and exit.
```
### CLI
```console
# pbaa -s <Source> -p <prompt> <class> -p <prompt> <class> ...
pbaa -s source_image.jpg -p "black dog" dog
pbaa -s source_image.jpg -p "black dog" dog -p "white cat" cat
```
### Python
```python
from pbaa import PBAA
annotator = PBAA()
# inference(<Source path>, <prompt:class dict>, box_threshold=0.25, nms_threshold=0.8, save=None, output_dir="outputs")
annotator("path/to/source_image.jpg", {"black dog": "dog", "white cat": "cat"})
```
## Gradio
Run the [gradio](https://github.com/gradio-app/gradio) demo with a simple command
```console
pbaa -g
```
Output
```
Launch gradio app
Running on local URL: http://0.0.0.0:7860
```
You can now access Gradio demos using your browser.
[localhost:7860](http://localhost:7860)
## Demo
```console
# Source : assets/demo9.jpg
# prompts : {"plant" : "plant", "picture" : "picture", "dog": "dog", "lamp" : "lamp", "carpet" : "carpet", "sofa" : "sofa"}
pbaa -s assets/demo9.jpg -p plant plant -p picture picture -p dog dog -p lamp lamp -p carpet carpet -p sofa sofa
```
| Origin | Detection | Segmentation |
|:-------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|
|  |  |  |
### Result data
[demo9.json](https://github.com/dh031200/pbaa/blob/main/assets/demo9.json)<br>
```console
json structure
filename
prompt
index
├ cls : class name
├ conf : confidence score
├ box : bounding box coordinates
└ poly : polygon coordinates
```
## License
`pbaa` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
## Acknowledgements
Grounded-Segment-Anything : [https://github.com/IDEA-Research/Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)<br>
Grounding DINO : [https://github.com/IDEA-Research/GroundingDINO](https://github.com/IDEA-Research/GroundingDINO)<br>
Segment-anything : [https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)<br>
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"description": "# pbaa : Prompt-Based Automatic Annotation\n\n[](https://pypi.org/project/pbaa)\n[](https://pypi.org/project/pbaa)\n\nEasy inference implementation of [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) for\n**Prompt-based automatic annotation**\n\n-----\n\n**Table of Contents**\n\n- [Installation](#installation)\n- [Usage](#usage)\n- [Gradio](#gradio)\n- [Demo](#demo)\n- [License](#license)\n- [Acknowledgements](#acknowledgements)\n\n## Installation\n\n### Docker (Recommend)\n\n```console\ngit clone https://github.com/dh031200/pbaa.git\ndocker build docker -t pbaa:latest\ndocker run --gpus all -it --ipc=host -v `pwd`:/workspace -p 7860:7860 pbaa:latest\n```\n\n### Without docker\n\nThe code requires `python>=3.8`, `CUDA==11.7`.\n\n```console\npip install pbaa\n```\n\n## Usage\n\n### Options\n\n```console\nUsage: pbaa [OPTIONS]\n\nOptions:\n --version Show the version and exit.\n -s, --src TEXT Source image or directory path\n -p, --prompt <TEXT TEXT>... Space-separated a pair of prompt and target\n classe. (Multi)\n -b, --box_threshold FLOAT Threshold for Object Detection (default: 0.25)\n -n, --nms_threshold FLOAT Threshold for NMS (default: 0.8)\n -o, --output_dir TEXT Path to result data (default: 'outputs')\n -g, --gradio Launch gradio app\n -h, --help Show this message and exit.\n```\n\n### CLI\n\n```console\n# pbaa -s <Source> -p <prompt> <class> -p <prompt> <class> ...\n\npbaa -s source_image.jpg -p \"black dog\" dog\npbaa -s source_image.jpg -p \"black dog\" dog -p \"white cat\" cat\n```\n\n### Python\n\n```python\nfrom pbaa import PBAA\n\nannotator = PBAA()\n# inference(<Source path>, <prompt:class dict>, box_threshold=0.25, nms_threshold=0.8, save=None, output_dir=\"outputs\")\nannotator(\"path/to/source_image.jpg\", {\"black dog\": \"dog\", \"white cat\": \"cat\"})\n```\n\n## Gradio\n\nRun the [gradio](https://github.com/gradio-app/gradio) demo with a simple command\n\n```console\npbaa -g\n```\n\nOutput\n\n```\nLaunch gradio app\nRunning on local URL: http://0.0.0.0:7860\n```\n\nYou can now access Gradio demos using your browser.\n[localhost:7860](http://localhost:7860)\n\n\n\n## Demo\n\n```console\n# Source : assets/demo9.jpg\n# prompts : {\"plant\" : \"plant\", \"picture\" : \"picture\", \"dog\": \"dog\", \"lamp\" : \"lamp\", \"carpet\" : \"carpet\", \"sofa\" : \"sofa\"}\n\npbaa -s assets/demo9.jpg -p plant plant -p picture picture -p dog dog -p lamp lamp -p carpet carpet -p sofa sofa\n```\n\n| Origin | Detection | Segmentation |\n|:-------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|\n|  |  |  |\n\n### Result data\n\n[demo9.json](https://github.com/dh031200/pbaa/blob/main/assets/demo9.json)<br>\n\n```console\njson structure\n\nfilename\nprompt\nindex\n \u251c cls : class name\n \u251c conf : confidence score\n \u251c box : bounding box coordinates\n \u2514 poly : polygon coordinates\n```\n\n## License\n\n`pbaa` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.\n\n## Acknowledgements\n\nGrounded-Segment-Anything : [https://github.com/IDEA-Research/Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)<br>\nGrounding DINO : [https://github.com/IDEA-Research/GroundingDINO](https://github.com/IDEA-Research/GroundingDINO)<br>\nSegment-anything : [https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)<br>\n",
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