/TextSnake.pytorch

PyTorch implement of ECCV2018 Paper: TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

Primary LanguagePythonMIT LicenseMIT

TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

A PyTorch implement of TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes (ECCV 2018) by Face++

Paper

Comparison of different representations for text instances. (a) Axis-aligned rectangle. (b) Rotated rectangle. (c) Quadrangle. (d) TextSnake. Obviously, the proposed TextSnake representation is able to effectively and precisely describe the geometric properties, such as location, scale, and bending of curved text with perspective distortion, while the other representations (axis-aligned rectangle, rotated rectangle or quadrangle) struggle with giving accurate predictions in such cases.

Text snake element:

  • center point
  • tangent line
  • text region

Description

Generally, this code has following features:

  1. include complete training and inference code
  2. pure python version without extra compiling
  3. compatible with laste PyTorch version (write with pytroch 0.4.0)
  4. support TotalText and SynthText dataset

Getting Started

This repo includes the training code and inference demo of TextSnake, training and infercence can be simplely run with a few code.

Prerequisites

To run this repo successfully, it is highly recommanded with:

  • Linux (Ubuntu 16.04)
  • Python3.6
  • Anaconda3
  • NVIDIA GPU(with 8G or larger GPU memory for training, 2G for inference)

(I haven't test it on other Python version.)

  1. clone this repository
git clone https://github.com/princewang1994/TextSnake.pytorch.git
  1. python package can be installed with pip
$ cd $TEXTSNAKE
$ pip install -r requirements.txt

Data preparation

Pretraining with SynthText

$ CUDA_VISIBLE_DEVICES=$GPUID python train.py synthtext_pretrain --dataset synth-text --viz --max_epoch 1 --batch_size 8

Training

Training model with given experiment name $EXPNAME

training from scratch:

$ EXPNAME=example
$ CUDA_VISIBLE_DEVICES=$GPUID python train.py $EXPNAME --viz

training with pretrained model(improved performance much)

$ EXPNAME=example
$ CUDA_VISIBLE_DEVICES=$GPUID python train.py example --viz --batch_size 8 --resume save/synthtext/textsnake_vgg_0.pth

options:

  • exp_name: experiment name, used to identify different training process
  • --viz: visualization toggle, output pictures are saved to './vis' by default

other options can be show by run python train.py -h

Running the tests

Runing following command can generate demo on TotalText dataset (300 pictures), the result are save to ./vis by default

$ EXPNAME=example
$ CUDA_VISIBLE_DEVICES=$GPUID python demo.py $EXPNAME --checkepoch 190

options:

  • exp_name: experiment name, used to identify different training process

other options can be show by run python train.py -h

Evaluation

Total-Text metric is included in dataset/total_text/Evaluation_Protocol/Python_scripts/Deteval.py, you should first modify the input_dir in Deteval.py and run following command for computing DetEval:

$ python dataset/total_text/Evaluation_Protocol/Python_scripts/Deteval.py

which will output

Performance

  • left: prediction/ground true
  • middle: text region(TR)
  • right: text center line(TCL)

What is comming

  • Pretraining with SynthText
  • Metric computing
  • Pretrained model upload (soon)
  • More dataset suport: [ICDAR15]

License

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

Acknowledgement

This project is writen by Prince Wang, part of codes refer to songdejia/EAST