PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network
Introduction
This is a tensorflow re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network.
Thanks for the author's (@whai362) awesome work!
Installation
- Any version of tensorflow version > 1.0 should be ok.
- python 2 or 3 will be ok.
Download
trained on ICDAR 2015 (training set) + ICDAR2017 MLT (training set):
baiduyun extract code: pffd
This model is not as good as article's, it's just a reference. You can finetune on it or you can do a lot of optimization based on this code.
Database | Precision (%) | Recall (%) | F-measure (%) |
---|---|---|---|
ICDAR 2015(val) | 74.61 | 80.93 | 77.64 |
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 make sure that gt text and image file have the same names.
Then run train.py like:
python train.py --gpu_list=0 --input_size=512 --batch_size_per_gpu=8 --checkpoint_path=./resnet_v1_50/ \
--training_data_path=./data/ocr/icdar2015/
If you have more than one gpu, you can pass gpu ids to gpu_list(like --gpu_list=0,1,2,3)
Note:
- right now , only support icdar2017 data format input, like (116,1179,206,1179,206,1207,116,1207,"###"), but you can modify data_provider.py to support polygon format input
- Already support polygon shrink by using pyclipper module
- this re-implementation is just for fun, but I'll continue to improve this code.
- re-implementation pse algorithm by using c++ (if you use python2, just run it, if python3, please replace python-config with python3-config in makefile)
Test
run eval.py like:
python eval.py --test_data_path=./tmp/images/ --gpu_list=0 --checkpoint_path=./resnet_v1_50/ \
--output_dir=./tmp/
a text file and result image will be then written to the output path.
Test Execution on Windows Environment
Before executing above mentioned commands,
Open the x64 or x32 Visual Studio developer command prompt (or Native Tools Command Prompt) in Windows 10 and use the following command to generate the pse.pyd file: first cd into pse folder to execute below command in command prompt,
cl pse.cpp /I ./include /I "C:\Python36\include" /LD /Fe:pse.pyd /link/LIBPATH:"C:\Python36\libs"
This will generate required files for Windows execution/testing
Note please make sure you comment some code in init.py in pse folder comment line 8 and 9
if subprocess.call(['make', '-C', BASE_DIR]) != 0: # return value
raise RuntimeError('Cannot compile pse: {}'.format(BASE_DIR))
Examples
About issues
If you encounter any issue check issues first, or you can open a new issue.
Reference
- http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
- https://github.com/CharlesShang/FastMaskRCNN
- whai362/PSENet#15
- https://github.com/argman/EAST
Acknowledge
@rkshuai found a bug about concat features in model.py.
If this repository helps you,please star it. Thanks.