This is a tensorflow re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.
This project has been modified from the forked source R2CNN_Faster-RCNN_Tensorflow to be consistant with the R2CNN paper.
For example, the bounding box coordinate is (x1, y1, x2, y2, h) instead of (x_c, y_c, w, h, theta).
1、tensorflow >= 1.2
2、cuda8.0
3、python2.7 (anaconda2 recommend)
4、opencv(cv2)
please download resnet50_v1、resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
├── VOCdevkit
│ ├── VOCdevkit_train
│ ├── Annotation
│ ├── JPEGImages
│ ├── VOCdevkit_test
│ ├── Annotation
│ ├── JPEGImages
cd $PATH_ROOT/libs/box_utils/
python setup.py build_ext --inplace
cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace
python eval.py --img_dir='/PATH/TO/IMAGES/'
--image_ext='.jpg'
--test_annotation_path='/PATH/TO/ANNOTATION/'
--gpu='0'
python inference.py --data_dir='/PATH/TO/IMAGES/'
--gpu='0'
1、If you want to train your own data, please note:
(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py
(3) Add data_name to line 75 of $PATH_ROOT/data/io/read_tfrecord.py
2、make tfrecord
cd $PATH_ROOT/data/io/
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/VOCdevkit/VOCdevkit_train/'
--xml_dir='Annotation'
--image_dir='JPEGImages'
--save_name='train'
--img_format='.png'
--dataset='DOTA'
3、train
cd $PATH_ROOT/tools
python train.py
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
Some relevant achievements based on this code.
@article{[yang2018position](https://ieeexplore.ieee.org/document/8464244),
title={Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network},
author={Yang, Xue and Sun, Hao and Sun, Xian and Yan, Menglong and Guo, Zhi and Fu, Kun},
journal={IEEE Access},
volume={6},
pages={50839-50849},
year={2018},
publisher={IEEE}
}
@article{[yang2018r-dfpn](http://www.mdpi.com/2072-4292/10/1/132),
title={Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks},
author={Yang, Xue and Sun, Hao and Fu, Kun and Yang, Jirui and Sun, Xian and Yan, Menglong and Guo, Zhi},
journal={Remote Sensing},
volume={10},
number={1},
pages={132},
year={2018},
publisher={Multidisciplinary Digital Publishing Institute}
}