# 子图分割

**Repository Path**: esheeper/separator

## Basic Information

- **Project Name**: 子图分割
- **Description**: 子图分割
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-17
- **Last Updated**: 2025-04-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Compound Figure Separator
- [Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks](http://vision.soic.indiana.edu/figure-separator/ )
- This repository contains an implementation of compound figure separator using a covolutional neral network (CNN). 

## Requirements
- tensorflow 1.0  https://www.tensorflow.org 
- opencv 3 http://opencv.org   
If you are new, I strongly recoomend [Anaconda](https://www.continuum.io/downloads) and then install tensorflow and opencv.
```
pip install tensorflow
#pip instal tensorflow-gpu #in case you want to use GPU
conda uninstall -c menpo opencv #in case you have opnecv2
conda install -c menpo opencv3
```

## Citation
If you find this tool useful, please consider to cite: 
```
@inproceedings{figure-separate,
  title={{A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks}},
  author={Satoshi Tsutsui, and David Crandall},
  booktitle={{The IAPR International Conference on Document Analysis and Recognition (ICDAR)}},
  year={2017}
}
```

## Another requirement: pretrained model
Let's donwload the pretrained model at the `./data` directory. I uploaded onto multiple places.  
- Hugging Face: [https://huggingface.co/apple2373/figure-sepration-model-submitted-544/blob/main/figure-sepration-model-submitted-544.pb](https://huggingface.co/apple2373/figure-sepration-model-submitted-544/blob/main/figure-sepration-model-submitted-544.pb)
- Dropbox: [https://www.dropbox.com/s/xug7uw1rrq7ljy0/figure-sepration-model-submitted-544.pb?dl=0](https://www.dropbox.com/s/xug7uw1rrq7ljy0/figure-sepration-model-submitted-544.pb?dl=0)


## I just want to separate compound figures. 
Sure, just use this command:
```
python main.py --images ./imgs --annotate 1
```
See the results directory. You have sub-figures at `./results` . That's it! 

Here is other options:
```
  --images IMAGES      the directory that has figures
  --model MODEL        model pb file. Default is ./data/figure-sepration-model-submitted-544.pb
  --thresh THRESH      sub-figuere detection threshold. Default is 0.5
  --output OUTPUT      output directory Default is ./results
  --annotate ANNOTATE  save annotation to the image or not. 1 is yes, 0 is no. Default is 0.
  ```

 Output json is somethine like:
```
[
 {
 "x": (x coordinate of left top point of the sub-figure),
 "y": (y coordinate of left top point of the sub-figure),
 "w": (width of the sub-figure),
 "h": (height of the sub-figure),
 "conf": (confidence value of the extaction),
 } ,....
] 
```

## I want to use inside my own code
Sure. You can use `FigureSeparator` class. See `simple_example.py` which is:
```
from code.FigureSeparator import FigureSeparator
fig_separator=FigureSeparator("./data/figure-sepration-model-submitted-544.pb")
sub_figures=fig_separator.extract("./imgs/PMC4076561-Figure5-1.png")
print(sub_figures)
```
Giving you the bounding box of sub-figures with confidence values. 

## Acknowledgements
The training is done by [darknet](https://github.com/pjreddie/darknet) and then ported to tensorflow model using [darkflow](https://github.com/thtrieu/darkflow). Part of the code is re-used from darkflow. Thank you very much for the authors of these two repositories.