Arbitrary-Oriented Scene Text Detection via Rotation Proposals(RRPN)

---

A Tensorflow implementation of RRPN based on FPN. You can refer to the papers RRPN paper here, and FPN paper here.

We(me and yang Xue) also implement the $R^2 CNN$ (link)based on FPN. And you can find the paper here. And we also proposed R-DFPN, but papers is under review so the complete code and instructions are will uploaded later. If useful to you, please star to support my work. Thanks.

---

##Configuration Environment: ubuntu+ python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080 If you want to use cpu, you need to modify the parameters of NMS and IOU functions use_gpu = False in cfgs.py You can also use docker environment.

docker push yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0

---

##Installation:

Clone the repository

---

##Make tfrecords: The data is VOC format, reference here. You can make tfrecords as following:

cd $RRPN_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship'

If the format of your data is not VOC, you can follow my advices below and generate tfrecords by yourself.

• Features in TFrecords are as follow:

feature = tf.train.Features(feature={
            'img_name': _bytes_feature(img_name),
            'img_height': _int64_feature(img_height),
            'img_width': _int64_feature(img_width),
            'img': _bytes_feature(img.tostring()),
            'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()),
            'num_objects': _int64_feature(gtbox_label.shape[0])
        })
# the format of gtboxes of label:
'''
gtboxes_and_label are gtboxes and labels in a img. It's a Matrix.
Shape:(num_objects, 9)
Contents:(x0, y0, x1, y1, x2, y2, x3, y3, category)

(x0, y0, x1, y1, x2, y2, x3, y3) are 4 vertices of inclined rectangle.
Note: they can be unorderd

(x0, y0)
+----------+(x1, y1)
|          |
+----------+(x2, y2)
(x3, y3)
'''

• You can refer data/io/convert_data_to_tfrecord.py and data/io/read_tfrecord.pyto to make yourself code.

---

##Demo This is a demo about detecting arbitrary-oriented buildings.(our dataset from SpaceNet and some modifications have been done)

1. Download Trained Weights: you can download trained weights here(the link will attach soon)
2. unzip them on ***/FPN_with_RRPN/output/trained_weights/FPN_RRPN_v1

cd tools/
python inference.py

4. Put your test imgs on FPN_with_RRPN/tools/inference_image. I have uploaded some imgs for demo test.
5. Detection results will show on folder: /FPN_with_RRPN/tools/inference_results

---

##Train

1. download pretrain weight(resnet_v1_101_2016_08_28.tar.gz or resnet_v1_50_2016_08_28.tar.gz) from here, then extract to folder data/pretrained_weights.
2. Change the name of pretrained weights to 'resnet_v1_101.ckpt'(for resnet101), or 'resnet_v1_50'(for resnet 50).(you can also use softlink)

cd tools/
python train.py

---

##Some Test Results