# R2CNN_Faster-RCNN_Tensorflow

**Repository Path**: likuan08/R2CNN_Faster-RCNN_Tensorflow

## Basic Information

- **Project Name**: R2CNN_Faster-RCNN_Tensorflow
- **Description**: Rotational region detection based on Faster-RCNN.
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2018-08-20
- **Last Updated**: 2023-03-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# R2CNN_Faster-RCNN_Tensorflow

## Abstract
This is a tensorflow re-implementation of [R<sup>2</sup>CNN: Rotational Region CNN for Orientation Robust Scene Text Detection](https://arxiv.org/abs/1706.09579).      
It should be noted that we did not re-implementate exactly as the paper and just adopted its idea.     

This project is based on [Faster-RCNN](https://github.com/DetectionTeamUCAS/Faster-RCNN_Tensorflow), and completed by [YangXue](https://github.com/yangxue0827) and [YangJirui](https://github.com/yangJirui).

## Status
**Demo available** !

## Citation
Some relevant achievements based on this code.     

    @article{https://arxiv.org/abs/1806.04828
        Author = {Xue Yang, Hao Sun, Xian Sun, Menglong Yan, Zhi Guo, Kun Fu},
        Title = {Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multiscale Rotation Region Convolutional Neural Network},
        Year = {2018}
    } 
    
    @article{yangxue_r-dfpn:http://www.mdpi.com/2072-4292/10/1/132 or https://arxiv.org/abs/1806.04331
        Author = {Xue Yang, Hao Sun, Kun Fu, Jirui Yang, Xian Sun, Menglong Yan and Zhi Guo},
        Title = {{R-DFPN}: Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks},
        Journal = {Published in remote sensing},
        Year = {2018}
    }

## [DOTA](https://captain-whu.github.io/DOTA/index.html) test results      
![1](DOTA.png)

## Comparison
| Approaches | mAP | PL | BD | BR | GTF | SV | LV | SH | TC | BC | ST | SBF | RA | HA | SP | HC |
|------------|:---:|:--:|:--:|:--:|:---:|:--:|:--:|:--:|:--:|:--:|:--:|:---:|:--:|:--:|:--:|:--:|
|[SSD inception-v2](https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_2)|17.84|41.06|24.31|4.55|17.1|15.93|7.72|13.21|39.96|12.05|46.88|9.09|30.82|1.36|3.5|0.0|
|[YOLOv2](https://arxiv.org/abs/1612.08242)|25.492|52.75|24.24|10.6|35.5|14.36|2.41|7.37|51.79|43.98|31.35|22.3|36.68|14.61|22.55|11.89| 
|[R-FCN](http://papers.nips.cc/paper/6465-r-fcn-object-detection-via-region-based-fully-convolutional-networks)|30.84|39.57|46.13|3.03|38.46|9.1|3.66|7.45|41.97|50.43|66.98|40.34|51.28|11.14|35.59|17.45|
|[FR-H](https://ieeexplore.ieee.org/abstract/document/7485869/)|39.95|49.74|64.22|9.38|56.66|19.18|14.17|9.51|61.61|65.47|57.52|51.36|49.41|20.8|45.84|24.38|
|[FR-O](https://arxiv.org/abs/1711.10398)|54.13|79.42|**77.13**|17.7|64.05|35.3|38.02|37.16|89.41|69.64|59.28|50.3|52.91|47.89|47.4|46.3|
|This repo|60.67|80.94|65.75|35.34|67.44|59.92|50.91|55.81|**90.67**|66.92|72.39|55.06|52.23|55.14|53.35|48.22|
|Current improvement|**65.33**|**84.89**|77.07|**38.55**|**67.88**|**61.78**|**51.86**|**56.22**|89.82|**75.77**|**76.30**|**53.68**|**63.25**|**63.85**|**65.05**|**53.99**|

## Face Detection
Environment: NVIDIA GeForce GTX 1060
![2](face.gif)

## Requirements
1、tensorflow >= 1.2     
2、cuda8.0     
3、python2.7 (anaconda2 recommend)    
4、[opencv(cv2)](https://pypi.org/project/opencv-python/)    

## Download Model
1、please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)、[resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights.     
2、please download [mobilenet_v2](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) pre-trained model on Imagenet, put it to data/pretrained_weights/mobilenet.     
3、please download [trained model](https://github.com/DetectionTeamUCAS/Models/tree/master/R2CNN_Faster-RCNN_Tensorflow) by this project, put it to output/trained_weights.   

## Compile
```  
cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace
```

## Demo(available)

**Select a configuration file in the folder (libs/configs/) and copy its contents into cfgs.py, then download the corresponding [weights](https://github.com/DetectionTeamUCAS/Models/tree/master/R2CNN_Faster-RCNN_Tensorflow).**      

### DOTA:  
```   
python demo.py --src_folder='/PATH/TO/DOTA/IMAGES_ORIGINAL/' 
               --image_ext='.png' 
               --des_folder='/PATH/TO/SAVE/RESULTS/' 
               --save_res=True
               --gpu='0'
```

### Face:
```   
python camera_demo.py --gpu='0'     
```

## Eval(available)
```  
python eval.py --img_dir='/PATH/TO/DOTA/IMAGES/' 
               --image_ext='.png' 
               --test_annotation_path='/PATH/TO/TEST/ANNOTATION/'
```

## Inference(available)
```  
python inference.py --data_dir='/PATH/TO/DOTA/IMAGES_CROP/'
```

## Train
```  
python train.py
```