# EAST_ICPR: EAST for ICPR MTWI 2018 CHALLENGE
### Introduction
This is a repository forked from [argman/EAST](https://github.com/argman/EAST) for the [ICPR MTWI 2018 CHALLENGE](https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.144ed780W1xl9s&raceId=231651).
Origin Repository: [argman/EAST - A tensorflow implementation of EAST text detector](https://github.com/argman/EAST)
Origin Author: [argman](https://github.com/argman)
Author: [Haozheng Li](https://github.com/HaozhengLi)
Email: sai-2008@qq.com or akaHaozhengLi@gmail.com
### Contents
1. [Transform](#transform)
2. [Models](#models)
3. [Demo](#demo)
3. [Train](#train)
4. [Test](#test)
5. [Results](#results)
### Transform
Some data in the dataset is abnormal, just like [ICPR MTWI 2018](https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.4ec66a80qvIKLc&raceId=231651)[[BaiduYun](https://pan.baidu.com/s/1OBDwzGz0lJUjlooWZGyeHQ)]. Abnormal means that the ground true labels are anticlockwise, or the images are not in 3 channels. Then errors like ['poly in wrong direction'](https://github.com/argman/EAST/issues?utf8=%E2%9C%93&q=poly+in+wrong+direction) will occur while using [argman/EAST](https://github.com/argman/EAST).
So I wrote a matlab program to check and transform the dataset. The program named <[transform.m](https://github.com/HaozhengLi/EAST_ICPR/tree/master/data_transform/transform.m)> is in the folder 'data_transform/' and its parameters are descripted as bellow:
```
icpr_img_folder = 'image_9000\'; %origin images
icpr_txt_folder = 'txt_9000\'; %origin ground true labels
icdar_img_folder = 'ICPR2018\'; %transformed images
icdar_gt_folder = 'ICPR2018\'; %transformed ground true labels
icdar_img_abnormal_folder = 'ICPR2018_abnormal\'; %images not in 3 channels, which give errors in argman/EAST
icdar_gt_abnormal_folder = 'ICPR2018_abnormal\'; %transformed ground true labels
%images and ground true labels files must be renamed as , , ..., while using argman/EAST
first_index = 0; %first index of the dataset
transform_list_name = 'transform_list.txt'; %file name of the rename list
```
***Note: For abnormal images not in 3 channels, please transform them to normal ones through other tools like [Format Factory](http://www.pcfreetime.com/). Then add the right data to the and , so finally you get a whole normal dataset which has been checked and transformed.***
### Models
1. Resnet_V1_50 Models trained on [ICPR MTWI 2018 (train)](https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.4ec66a80qvIKLc&raceId=231651): [[100k steps](https://pan.baidu.com/s/1eBx4SYTZn4maxYVci7bSbQ)] [[500k steps](https://pan.baidu.com/s/17Qyu6U6or9Jbx7VAubCtgA)] [[1035k steps](https://pan.baidu.com/s/1jfjDxS4uknVavaOMO_wZQA)]
2. Resnet_V1_101 Models trained on [ICPR MTWI 2018 (train)](https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.4ec66a80qvIKLc&raceId=231651) + [ICDAR 2017 MLT (train + val)](http://rrc.cvc.uab.es/?ch=8&com=downloads) + [RCTW-17 (train)](http://www.icdar2017chinese.site:5080/dataset/): [[100k steps](https://pan.baidu.com/s/1EDt1eN99jGRkE641RrGgNQ)]
3. Resnet_V1_101 Models pre-trained on Models-2, then trained on just [ICPR MTWI 2018 (train)](https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.4ec66a80qvIKLc&raceId=231651): [[987k steps](https://pan.baidu.com/s/1ao77XZtOZQF8wu_ghOH8ZA)]
3. (In [argman/EAST](https://github.com/argman/EAST)) Resnet_V1_50 Models trained on ICDAR 2013 (train) + ICDAR 2015 (train): [[50k steps](https://pan.baidu.com/s/1ibyF0-fWE2AT3dEpwIltKw)]
4. (In [argman/EAST](https://github.com/argman/EAST)) Resnet_V1_50 Models provided by tensorflow slim: [[slim_resnet_v1_50](https://pan.baidu.com/s/1PFiJ7YKeoLKiF0dRPocvNg)]
### Demo
Download the pre-trained models and run:
```
python run_demo_server.py --checkpoint-path models/east_icpr2018_resnet_v1_50_rbox_1035k/
```
Then Open http://localhost:8769 for the web demo server, or get the results in 'static/results/'.
***Note: See [argman/EAST#demo](https://github.com/argman/EAST#demo) for more details.***
### Train
Prepare the training set and run:
```
python multigpu_train.py --gpu_list=0 --input_size=512 --batch_size_per_gpu=8 \
--checkpoint_path=models/east_icpr2018_resnet_v1_50_rbox/ \
--text_scale=512 --training_data_path=data/ICPR2018/ --geometry=RBOX \
--learning_rate=0.0001 --num_readers=18 --max_steps=50000
```
***Note 1: Images and ground true labels files must be renamed as , , ..., while using [argman/EAST](https://github.com/argman/EAST). Please see the examples in the folder 'training_samples/'.
Note 2: If ```--restore=True```, training will restore from checkpoint and ignore the ```--pretrained_model_path```. If ```--restore=False```, training will delete checkpoint and initialize with the ```--pretrained_model_path``` (if exists).
Note 3: See [argman/EAST#train](https://github.com/argman/EAST#train) for more details.***
### Test
Names of the images in [ICPR MTWI 2018](https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.4ec66a80qvIKLc&raceId=231651) are abnormal. Like but not . Then errors will occur while using [argman/EAST#test](https://github.com/argman/EAST#test).
So I wrote two matlab programs to rename and inversely rename the dataset. Before evaluating, run the program named <[rename.m](https://github.com/HaozhengLi/EAST_ICPR/blob/master/data_transform/rename.m)> to make names of the images normal. This program is in the folder 'data_transform/' and its parameters are descripted as bellow:
```
icpr_img_folder = 'image_10000\'; %origin images
icdar_img_folder = 'ICPR2018_test\'; %transformed images
icdar_img_abnormal_folder = 'ICPR2018_test_abnormal\'; %images not in 3 channels, which give errors in argman/EAST
icpr_count = 10000; %first index of the dataset
rename_list_name = 'rename_list.txt'; %file name of the rename list
```
***Note: Just like <[transform.m](https://github.com/HaozhengLi/EAST_ICPR/tree/master/data_transform/transform.m)>, please transform abnormal images through other tools like [Format Factory](http://www.pcfreetime.com/).***
After you have prepared the test set, run:
```
python eval.py --test_data_path=data/ICPR2018/ --gpu_list=0 \
--checkpoint_path=models/east_icpr2018_resnet_v1_50_rbox_1035k/ --output_dir=results/1035k/
```
Then get the results in 'results/'.
Finally inversely rename the result labels files from to according to the rename list generated by <[rename.m](https://github.com/HaozhengLi/EAST_ICPR/blob/master/data_transform/rename.m)>. Run the program named <[rename_inverse.m](https://github.com/HaozhengLi/EAST_ICPR/blob/master/data_transform/rename_inverse.m)> which is in the folder 'data_transform/' and its parameters are descripted as bellow:
```
rename_list_name = 'rename_list.txt'; %file name of the rename list
icpr_img_folder = 'image_10000\'; %origin images
icpr_txt_folder = 'results\'; %result labels files generated by argman/EAST
icdar_gt_folder = 'txt_10000\'; %inversely renamed result labels files
```
Then zip the results in 'txt_10000/' and submit it to the [ICPR MTWI 2018 CHALLENGE](https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.144ed780W1xl9s&raceId=231651).
### Results
Finally our model <[east_icpr2018_resnet_v1_50_rbox_1035k](https://pan.baidu.com/s/1jfjDxS4uknVavaOMO_wZQA)> rank 31 in the [ICPR MTWI 2018 CHALLENGE](https://tianchi.aliyun.com/competition/rankingList.htm?spm=5176.11165320.0.0.13eb6a9e8WAPZe&season=0&raceId=231651&pageIndex=2):
Here are some results on [ICPR MTWI 2018](https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.4ec66a80qvIKLc&raceId=231651):
# Have fun!! :)