where, --datadir points to the renderer_data directory included in the
data torrent.
Specifying this datadir is optional, and if not specified, the script will
automatically download and extract the same renderer.tar.gz data file (~24 M).
This data file includes:
sample.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.
fonts: three sample fonts (add more fonts to this folder and then update fonts/fontlist.txt with their paths).
newsgroup: Text-source (from the News Group dataset). This can be subsituted with any text file. Look inside text_utils.py to see how the text inside this file is used by the renderer.
models/colors_new.cp: Color-model (foreground/background text color model), learnt from the IIIT-5K word dataset.
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).
This script will generate random scene-text image samples and store them in an h5 file in results/SynthText.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.h5 later, run:
python visualize_results.py
Pre-generated Dataset
A dataset with approximately 800000 synthetic scene-text images generated with this code can be found here.
Adding New Images
Segmentation and depth-maps are required to use new images as background. Sample scripts for obtaining these are available here.
predict_depth.m MATLAB script to regress a depth mask for a given RGB image; uses the network of Liu etal. However, more recent works (e.g., this) might give better results.
run_ucm.m and floodFill.py for getting segmentation masks using gPb-UCM.
For an explanation of the fields in sample.h5 (e.g.: seg,area,label), please check this comment.
Pre-processed Background Images
The 8,000 background images used in the paper, along with their
segmentation and depth masks, are included in the same
torrent
as the pre-generated dataset under the bg_data directory. The files are:
filenames
description
imnames.cp
names of images which do not contain background text
bg_img.tar.gz
images (filter these using imnames.cp)
depth.h5
depth maps
seg.h5
segmentation maps
Downloading without BitTorrent
Downloading with BitTorrent is strongly recommended. If that is not
possible, the files are also available to download over http from
https://thor.robots.ox.ac.uk/~vgg/data/scenetext/preproc/<filename>,
where, <filename> can be:
filenames
size
md5 hash
imnames.cp
180K
bg_img.tar.gz
8.9G
3eac26af5f731792c9d95838a23b5047
depth.h5
15G
af97f6e6c9651af4efb7b1ff12a5dc1b
seg.h5
6.9G
1605f6e629b2524a3902a5ea729e86b2
Note: due to large size, depth.h5 is also available for download as 3-part split-files of 5G each.
These part files are named: depth.h5-00, depth.h5-01, depth.h5-02. Download using the path above, and put them together using cat depth.h5-0* > depth.h5.
To download, use the something like the following:
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