Eurus-Holmes / SynthText_CH

[SynthText Chinese] Improved code for generating synthetic text images as described in "Synthetic Data for Text Localisation in Natural Images", Ankush Gupta, Andrea Vedaldi, Andrew Zisserman, CVPR 2016.

SynthText_CH

Modify from SynthText_Chinese_py3 to generate chinese character.

Setup the env

python -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Usage

The data directory is just the same as here, including:

• dset.h5: This is a sample h5 file which contains a set of 5 images along with their depth and segmentation information. Note, this is just given as an example; you are encouraged to add more images (along with their depth and segmentation information) to this database for your own use.
• data/fonts: three sample fonts (add more fonts to this folder and then update fonts/fontlist.txt with their paths).
• data/newsgroup: Text-source (from the News Group dataset). This can be subsituted with any text file by newsgroup.py or your code. Look inside text_utils.py to see how the text inside this file is used by the renderer.
• data/models/colors_new.cp: Color-model (foreground/background text color model), learnt from the IIIT-5K word dataset.
• data/models: Other cPickle files (char_freq.cp: frequency of each character in the text dataset; font_px2pt.cp: conversion from pt to px for various fonts: If you add a new font, make sure that the corresponding model is present in this file, if not you can add it by adapting invert_font_size.py).

The dataset directory, you need to put these files into this folder.

• dset.h5: You need to genetate the "dset.h5" file by yourself. You must download these files: The 8,000 background images used in the paper, along with their segmentation and depth masks, have been uploaded here: http://www.robots.ox.ac.uk/~vgg/data/scenetext/preproc/ + filename, where, filename can be:

  • imnames.cp [180K]: names of filtered files, i.e., those files which do not contain text
  • bg_img.tar.gz [8.9G]: compressed image files (more than 8000, so only use the filtered ones in imnames.cp)
  • depth.h5 [15G]: depth maps
  • seg.h5 [6.9G]: segmentation maps

  After that, you also have to unzip the "bg_img.tar.gz" to this folder. You only run:

python gen_dset.py

The "gen_dset.py" file can generate 800k images infomation. If you want to generate more images infomation, You can modify the value of i or j. Then you just copy the generated file dset.h5 to the folder data.

At last, you only run:

python gen.py

If You want to visualize these synthtext images,you can run:

python gen.py --viz

This script will generate random scene-text image samples and store them in an h5 file in results/SynthText_800000.h5. If the --viz option is specified, the generated output will be visualized as the script is being run; omit the --viz option to turn-off the visualizations. If you want to visualize the results stored in results/SynthText_800000.h5 later, run:

python visualize_results.py

Note: I do not own the copyright to these images. More detail content,you can consult the https://github.com/ankush-me/SynthText.

Update

• To generate org_img, run draw_org_img.py
• To generate render_img & label_txt, run draw_wordBB.py
• To facilitate subsequent data preprocessing, run rename.py or rename2.py.