https://github.com/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.
https://github.com/eurus-holmes/synthtext_ch

data-augmentation synthtext

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[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.

• Host: GitHub
• URL: https://github.com/eurus-holmes/synthtext_ch
• Owner: Eurus-Holmes
• Created: 2020-06-24T03:44:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T12:04:32.000Z (almost 2 years ago)
• Last Synced: 2023-03-01T22:32:15.398Z (over 1 year ago)
• Topics: data-augmentation, synthtext
• Language: Python
• Homepage:
• Size: 11.9 MB
• Stars: 7
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# SynthText_CH

> *Modify from [SynthText_Chinese_py3](https://github.com/yanhaiming56/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](https://github.com/yanhaiming56/SynthText_Chinese_py3/tree/master/data), 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`.