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https://github.com/dnet/pySSTV

Python classes for generating Slow-scan Television transmissions
https://github.com/dnet/pySSTV

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Python classes for generating Slow-scan Television transmissions

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README 

        
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 {MartinM1,MartinM2,ScottieS1,ScottieS2,ScottieDX,Robot36,PasokonP3,PasokonP5,PasokonP7,PD90,PD120,PD160,PD180,PD240,PD290,WraaseSC2120,WraaseSC2180,Robot8BW,Robot24BW}]

                  [--rate RATE] [--bits BITS] [--vox] [--fskid FSKID]

                  [--chan CHAN] [--resize] [--keep-aspect-ratio]

                  [--keep-aspect] [--resample {nearest,bicubic,lanczos}]

                  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



    options:

      -h, --help            show this help message and exit

      --mode {MartinM1,MartinM2,ScottieS1,ScottieS2,ScottieDX,Robot36,PasokonP3,PasokonP5,PasokonP7,PD90,PD120,PD160,PD180,PD240,PD290,WraaseSC2120,WraaseSC2180,Robot8BW,Robot24BW}

                            image mode (default: Martin M1)

      --rate RATE           sampling rate (default: 48000)

      --bits BITS           bits per sample (default: 16)

      --vox                 add VOX tones at the beginning

      --fskid FSKID         add FSKID at the end

      --chan CHAN           number of channels (default: mono)

      --resize              resize the image to the correct size

      --keep-aspect-ratio   keep the original aspect ratio when resizing

                                (and cut off excess pixels)

      --keep-aspect         keep the original aspect ratio when resizing

                                (not cut off excess pixels)

      --resample {nearest,bicubic,lanczos}

                            which resampling filter to use for resizing

                                (see Pillow documentation)



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 calculations

 - `gen_freq_bits` generates tuples that describe a sine wave segment with

   frequency in Hz and duration in ms

 - `gen_values` generates samples between -1 and +1, performing sampling

   according to the samples per second value given during construction

 - `gen_samples` generates discrete samples, performing quantization

   according to the bits per sample value given during construction

 - `write_wav` writes 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/

 - robot 36 encoder/decoder in C: https://github.com/xdsopl/robot36/



Dependencies

------------



 - Python 3.5 or later

 - Python Imaging Library (Debian/Ubuntu package: `python3-pil`)