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TextBPN-Puls-Plus

This is a Pytorch implementation of TextBPN++: Arbitrary Shape Text Detection via Boundary Transformer; This project is based on TextBPN

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Prerequisites

python >= 3.7 ;
PyTorch >= 1.7.0;
Numpy >=1.2.0;
CUDA >=11.1;
GCC >=10.0;
opencv-python < 4.5.0
NVIDIA GPU(2080 or 3090);

NOTE: We tested the code in the environment of Arch Linux+Python3.9 and Ubuntu20.04+Python3.8. In different environments, the code needs to be adjusted slightly, and there is little difference in performance (not significant, so it can be ignored). Most of our experiments are mainly in Arch Linux + Python3.9.

Makefile

If DCN is used, some CUDA files need to be compiled

  # make sure that the PATH of CUDA in setup.py is set properly with the right environment
  # Set the GPU you need in setup.py, if If you have different GPUs in your machine.
  
  cd network/backbone/assets/dcn
  sh Makefile.sh
  
  # setup.py 
  import os
  PATH ="{}:{}".format(os.environ['PATH'], "/opt/cuda/bin")
  # os.environ['CUDA_VISIBLE_DEVICES'] = "1"
  os.environ['PATH'] = PATH
  from setuptools import setup
  from torch.utils.cpp_extension import BuildExtension, CUDAExtension

Dataset Links

  1. CTW1500 （new version dataset at https://github.com/Yuliang-Liu/Curve-Text-Detector）
  2. TD500
  3. Total-Text （new version dataset at: https://github.com/cs-chan/Total-Text-Dataset）

NOTE: The images of each dataset can be obtained from their official website.

Training

Prepar dataset

We provide a simple example for each dataset in data, such as Total-Text, CTW-1500, and ArT ...

Pre-training models

We provide some pre-tarining models on SynText Baidu Drive (download code: r1ff), Google Drive and MLT-2017 Baidu Drive (download code: srym), Google Drive

├── pretrain
│   ├──Syn
│       ├── TextBPN_resnet50_0.pth  #1s
│       ├── TextBPN_resnet18_0.pth  #4s
│       ├── TextBPN_deformable_resnet50_0.pth  #1s
│   ├── MLT
│       ├── TextBPN_resnet50_300.pth  #1s
│       ├── TextBPN_resnet18_300.pth  #4s
│       ├── TextBPN_deformable_resnet50_300.pth #1s

NOTE: we also provide the pre-tarining scripts for SynText.

Runing the training scripts

We provide training scripts for each dataset in scripts-train, such as Total-Text, CTW-1500, and ArT ...

# train_Totaltext_res50_1s.sh
#!/bin/bash
cd ../
CUDA_LAUNCH_BLOCKING=1 python3 train_textBPN.py --exp_name Totaltext --net resnet50 --scale 1 --max_epoch 660 --batch_size 12 --gpu 0 --input_size 640 --optim Adam --lr 0.001 --num_workers 30

# train_Totaltext_res50_1s_fine_mlt.sh
#!/bin/bash
cd ../
CUDA_LAUNCH_BLOCKING=1 python3 train_textBPN.py --exp_name Totaltext --net resnet50 --scale 1 --max_epoch 660 --batch_size 12 --gpu 0 --input_size 640 --optim Adam --lr 0.0001 --num_workers 30 --load_memory True --resume model/pretrain/MLT/TextBPN_resnet50_300.pth

Testing

Runing the Testing scripts

We provide testing scripts for each dataset in scripts-eval, such as Total-Text, CTW-1500, and ArT ...

# Eval_ArT.sh
#!/bin/bash
cd ../
##################### eval for ArT with ResNet50 1s ###################################
#CUDA_LAUNCH_BLOCKING=1 python3 eval_textBPN.py --net resnet50 --scale 1 --exp_name ArT --checkepoch 605 --test_size 960 2880 --dis_threshold 0.4 --cls_threshold 0.4 --gpu 0;


##################### eval for ArT with ResNet50-DCN 1s ###################################
CUDA_LAUNCH_BLOCKING=1 python3 eval_textBPN.py --net deformable_resnet50 --scale 1 --exp_name ArT --checkepoch 480 --test_size 960 2880 --dis_threshold 0.4 --cls_threshold 0.8 --gpu 0;


##################### batch eval for ArT ###################################
#for ((i=660; i>=300; i=i-5));
#do 
#CUDA_LAUNCH_BLOCKING=1 python3 eval_textBPN.py --exp_name ArT --net deformable_resnet50 --checkepoch $i --test_size 960 2880 --dis_threshold 0.45 --cls_threshold 0.8 --gpu 0;
#done

NOTE：If you want to save the visualization results, you need to open “--viz”. Here is an example:

CUDA_LAUNCH_BLOCKING=1 python3 eval_textBPN.py --net resnet18 --scale 4 --exp_name TD500 --checkepoch 1135 --test_size 640 960 --dis_threshold 0.35 --cls_threshold 0.9 --gpu 0 --viz;

Demo

You can also run prediction on your own dataset without annotations. Here is an example:

#demo.sh
#!/bin/bash
CUDA_LAUNCH_BLOCKING=1 python3 demo.py --net resnet18 --scale 4 --exp_name TD500 --checkepoch 1135 --test_size 640 960 --dis_threshold 0.35 --cls_threshold 0.9 --gpu 0 --viz --img_root /path/to/image

Evaluate the performance

Note that we provide some the protocols for benchmarks (Total-Text, CTW-1500, MSRA-TD500, ICDAR2015). The embedded evaluation protocol in the code are obtatined from the official protocols. You don't need to run these protocols alone, because our test code will automatically call these scripts, please refer to "util/eval.py"

Evaluate the speed

We refer to the speed testing scheme of DB. The speed is evaluated by performing a testing image for 50 times to exclude extra IO time.

CUDA_LAUNCH_BLOCKING=1 python3 eval_textBPN_speed.py --net resnet18 --scale 4 --exp_name TD500 --checkepoch 1135 --test_size 640 960 --dis_threshold 0.35 --cls_threshold 0.9 --gpu 1;

Note that the speed is related to both to the GPU and the CPU.

Model performance

Our replicated performance (without extra data for pre-training) by using the updated codes:

Datasetspre-trainingModelrecallprecisionF-measureFPS
Total-Text-Res50-1s84.2291.8887.8813
CTW-1500-Res50-1s82.6986.5384.5714

The results are reported in our paper as follows:

DatasetsModelrecallprecisionF-measureFPS
Total-TextRes18-4s81.9089.8885.7032.5
Total-TextRes50-1s85.3491.8188.4613.3
Total-TextRes50-1s+DCN87.9392.4490.1313.2
CTW-1500Res18-4s81.6287.5584.4835.3
CTW-1500Res50-1s83.7787.3085.5014.1
CTW-1500Res50-1s+DCN84.7188.3486.4916.5
MSRA-TD500Res18-4s87.4692.3889.8538.5
MSRA-TD500Res50-1s85.4089.2387.2715.2
MSRA-TD500Res50-1s+DCN86.7793.6990.1015.3
MLT-2017Res50-1s65.6780.4972.33-
MLT-2017Res50-1s+DCN72.1083.7477.48-
ICDAR-ArTRes50-1s71.0781.1475.77-
ICDAR-ArTRes50-1s+DCN77.0584.4880.59-

NOTE: The results on ICDAR-ArT and MLT-2017 are aslo can be found on the official competition website (ICDAR-ArT and MLT-2017). You can also download the models for each benchmarks from Baidu Drive (download code: wct1)

Visual comparison

Qualitative comparisons with TextRay, ABCNet, and FCENet on selected challenging samples from CTW-1500. The images (a)-(d) are borrowed from FCENet.

Reading Order Model

Using the Reading Order Model to predict four key corners on the boundary proposal in turn. The reading order module will be released at a certain time.

Citing the related works

Please cite the related works in your publications if it helps your research:

  @inproceedings{DBLP:conf/iccv/Zhang0YWY21,
  author    = {Shi{-}Xue Zhang and
               Xiaobin Zhu and
               Chun Yang and
               Hongfa Wang and
               Xu{-}Cheng Yin},
  title     = {Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection},
  booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV} 2021, Montreal, QC, Canada, October 10-17, 2021},
  pages     = {1285--1294},
  publisher = {{IEEE}},
  year      = {2021},
}

@article{zhang2023arbitrary,
  title={Arbitrary shape text detection via boundary transformer},
  author={Zhang, Shi-Xue and Yang, Chun and Zhu, Xiaobin and Yin, Xu-Cheng},
  journal={IEEE Transactions on Multimedia},
  year={2023},
  publisher={IEEE}
}

@article{DBLP:journals/pami/ZhangZCHY23,
  author       = {Shi{-}Xue Zhang and
                  Xiaobin Zhu and
                  Lei Chen and
                  Jie{-}Bo Hou and
                  Xu{-}Cheng Yin},
  title        = {Arbitrary Shape Text Detection via Segmentation With Probability Maps},
  journal      = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume       = {45},
  number       = {3},
  pages        = {2736--2750},
  year         = {2023},
  url          = {https://doi.org/10.1109/TPAMI.2022.3176122},
  doi          = {10.1109/TPAMI.2022.3176122},
}

@article{zhang2022kernel,
  title={Kernel proposal network for arbitrary shape text detection},
  author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Yang, Chun and Yin, Xu-Cheng},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022},
  publisher={IEEE}
}

@inproceedings{DBLP:conf/cvpr/ZhangZHLYWY20,
  author       = {Shi{-}Xue Zhang and
                  Xiaobin Zhu and
                  Jie{-}Bo Hou and
                  Chang Liu and
                  Chun Yang and
                  Hongfa Wang and
                  Xu{-}Cheng Yin},
  title        = {Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection},
  booktitle    = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
                  {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020},
  pages        = {9696--9705},
  publisher    = {Computer Vision Foundation / {IEEE}},
  year         = {2020},
  doi          = {10.1109/CVPR42600.2020.00972},
}

License

This project is licensed under the MIT License - see the LICENSE.md file for details

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