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https://github.com/che220/easy-EAST
wrap EAST model with flask. Also replace lanms's C++ code with python implementation
https://github.com/che220/easy-EAST
Last synced: 9 days ago
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wrap EAST model with flask. Also replace lanms's C++ code with python implementation
- Host: GitHub
- URL: https://github.com/che220/easy-EAST
- Owner: che220
- Created: 2019-03-23T23:22:24.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-03-24T00:02:02.000Z (over 5 years ago)
- Last Synced: 2024-08-02T11:16:02.309Z (3 months ago)
- Language: C++
- Size: 1.81 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### Copied from: https://github.com/argman/EAST.git
Hui's comment: I chose to evaluate this because of its performance. For an 1500 x 1000 1099 form image,
it takes about 4 seconds to run on CPU. But it takes less than 200ms to run on GPU.
To run your own tests:
cd inference_local
python3 ./web_server.py (it will show you the URL. Copy that URL to your browser)
You will need the pretrained models mentioned below and put them at $HOME/data/cache/text_detection_east. If you
don't want to download from internet, please email me [email protected]
# EAST: An Efficient and Accurate Scene Text Detector
### Introduction
This is a tensorflow re-implementation of [EAST: An Efficient and Accurate Scene Text Detector](https://arxiv.org/abs/1704.03155v2).
The features are summarized blow:
+ Online demo
+ http://east.zxytim.com/
+ Result example: http://east.zxytim.com/?r=48e5020a-7b7f-11e7-b776-f23c91e0703e
+ CAVEAT: There's only one cpu core on the demo server. Simultaneous access will degrade response time.
+ Only **RBOX** part is implemented.
+ A fast Locality-Aware NMS in C++ provided by the paper's author.
+ The pre-trained model provided achieves **80.83** F1-score on ICDAR 2015
Incidental Scene Text Detection Challenge using only training images from ICDAR 2015 and 2013.
see [here](http://rrc.cvc.uab.es/?ch=4&com=evaluation&view=method_samples&task=1&m=29855>v=1) for the detailed results.
+ Differences from original paper
+ Use ResNet-50 rather than PVANET
+ Use dice loss (optimize IoU of segmentation) rather than balanced cross entropy
+ Use linear learning rate decay rather than staged learning rate decay
+ Speed on 720p (resolution of 1280x720) images:
+ Now
+ Graphic card: GTX 1080 Ti
+ Network fprop: **~50 ms**
+ NMS (C++): **~6ms**
+ Overall: **~16 fps**
+ Then
+ Graphic card: K40
+ Network fprop: ~150 ms
+ NMS (python): ~300ms
+ Overall: ~2 fps
Thanks for the author's ([@zxytim](https://github.com/zxytim)) help!
Please cite his [paper](https://arxiv.org/abs/1704.03155v2) if you find this useful.
### Contents
1. [Installation](#installation)
2. [Download](#download)
2. [Demo](#demo)
3. [Test](#train)
4. [Train](#test)
5. [Examples](#examples)
### Installation
1. Any version of tensorflow version > 1.0 should be ok.
### Download
1. Models trained on ICDAR 2013 (training set) + ICDAR 2015 (training set): [BaiduYun link](http://pan.baidu.com/s/1jHWDrYQ) [GoogleDrive](https://drive.google.com/open?id=0B3APw5BZJ67ETHNPaU9xUkVoV0U)
2. Resnet V1 50 provided by tensorflow slim: [slim resnet v1 50](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)
### Train
If you want to train the model, you should provide the dataset path, in the dataset path, a separate gt text file should be provided for each image
and run
```
python multigpu_train.py --gpu_list=0 --input_size=512 --batch_size_per_gpu=14 --checkpoint_path=/tmp/east_icdar2015_resnet_v1_50_rbox/ \
--text_scale=512 --training_data_path=/data/ocr/icdar2015/ --geometry=RBOX --learning_rate=0.0001 --num_readers=24 \
--pretrained_model_path=/tmp/resnet_v1_50.ckpt
```
If you have more than one gpu, you can pass gpu ids to gpu_list(like --gpu_list=0,1,2,3)
**Note: you should change the gt text file of icdar2015's filename to img_\*.txt instead of gt_img_\*.txt(or you can change the code in icdar.py), and some extra characters should be removed from the file.
See the examples in training_samples/**
### Demo
If you've downloaded the pre-trained model, you can setup a demo server by
```
python3 run_demo_server.py --checkpoint_path /tmp/east_icdar2015_resnet_v1_50_rbox/
```
Then open http://localhost:8769 for the web demo. Notice that the URL will change after you submitted an image.
Something like `?r=49647854-7ac2-11e7-8bb7-80000210fe80` appends and that makes the URL persistent.
As long as you are not deleting data in `static/results`, you can share your results to your friends using
the same URL.
URL for example below: http://east.zxytim.com/?r=48e5020a-7b7f-11e7-b776-f23c91e0703e
### Test
run
```
python eval.py --test_data_path=/tmp/images/ --gpu_list=0 --checkpoint_path=/tmp/east_icdar2015_resnet_v1_50_rbox/ \
--output_dir=/tmp/
```
a text file will be then written to the output path.
### Examples
Here are some test examples on icdar2015, enjoy the beautiful text boxes!
### Troubleshooting
+ How to compile lanms on Windows ?
+ See https://github.com/argman/EAST/issues/120
Please let me know if you encounter any issues(my email boostczc@gmail dot com).