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README.md
SSTV generator in pure Python
PySSTV generates SSTV modulated WAV files from any image that PIL can open (PNG, JPEG, GIF, and many others). These WAV files then can be played by any audio player connected to a shortwave radio for example.
My main motivation was to understand the internals of SSTV in practice, so performance is far from optimal. I tried keeping the code readable, and only performed such optimizations that wouldn't have complicated the codebase.
Command line usage
$ python -m pysstv -h
usage: __main__.py [-h]
[--mode {MartinM2,MartinM1,Robot24BW,ScottieS2,ScottieS1,Robot8BW}]
[--rate RATE] [--bits BITS]
image.png output.wav
Converts an image to an SSTV modulated WAV file.
positional arguments:
image.png input image file name
output.wav output WAV file name
optional arguments:
-h, --help show this help message and exit
--mode {MartinM2,MartinM1,Robot24BW,ScottieS2,ScottieS1,Robot8BW}
image mode (default: Martin M1)
--rate RATE sampling rate (default: 48000)
--bits BITS bits per sample (default: 16)
Python interface
The SSTV class in the sstv module implements basic SSTV-related
functionality, and the classes of other modules such as grayscale and
color extend this. Most instances implement the following methods:
__init__takes a PIL image, the samples per second, and the bits per sample as a parameter, but doesn't perform any hard calculationsgen_freq_bitsgenerates tuples that describe a sine wave segment with frequency in Hz and duration in msgen_valuesgenerates samples between -1 and +1, performing sampling according to the samples per second value given during constructiongen_samplesgenerates discrete samples, performing quantization according to the bits per sample value given during constructionwrite_wavwrites the whole image to a Microsoft WAV file
The above methods all build upon those above them, for example write_wav
calls gen_samples, while latter calls gen_values, so typically, only
the first and the last, maybe the last two should be called directly, the
others are just listed here for the sake of completeness and to make the
flow easier to understand.
License
The whole project is available under MIT license.
Useful links
- receive-only "counterpart": https://github.com/windytan/slowrx
- free SSTV handbook: http://www.sstv-handbook.com/
Dependencies
- Python 2.7 (tested on 2.7.5)
- Python Imaging Library (Debian/Ubuntu package:
python-imaging)