redpil 0.0.1

Join the wonderland of python, and decode all your images in a numpy compatible way

# redpil

[](https://pypi.python.org/pypi/redpil)
[](https://travis-ci.org/hmaarrfk/redpil)
[](https://redpil.readthedocs.io/en/latest/?badge=latest)


Join the wonderland of python, and decode all your images in a numpy compatible way.

Pillow's memory system isn't compatible with numpy. Meaning that everytime you
read or write images, they get copied to a Pillow array, then again to a numpy
array.

For large images, this is a serious bottleneck.

* Documentation: https://redpil.readthedocs.io.

## Supported file formats

* BMP: 8bit mono grayscale only. [Wikipedia](https://en.wikipedia.org/wiki/BMP_file_format)

## Future file formats

* BMP: more coverage
* JPEG, JPEG2000
* GIF
* PNG
* SVG
* TIFF

## Benchmarks

I don't have a fancy benchmarking service like scikit-image or dask has, but
here are the benchmarks results compared to a PILLOW backend. This is running
on my SSD, a Samsung Evo 960 which claims it can write at 1.8GB/s. This is
pretty close to what `redpil` achieves.


### 8 bit BMP grayscale images

Saving images:
```
================ ============ =============
-- mode
---------------- --------------------------
shape pillow redpil
================ ============ =============
(128, 128) 263±4μs 106±2μs
(1024, 1024) 1000±60μs 787±90μs
(2048, 4096) 5.15±0.2ms 4.62±0.07ms
(32768, 32768) 510±10ms 470±3ms
================ ============ =============
```

Reading image
```
================ =========== =============
-- mode
---------------- -------------------------
shape pillow redpil
================ =========== =============
(128, 128) 306±7μs 120±2μs
(1024, 1024) 990±300μs 187±7μs
(2048, 4096) 3.53±1ms 1.39±0.04ms
(32768, 32768) 242±7μs 369±4ms
================ =========== =============
```
Note, Pillow refuses to read the 1GB image because it thinks it is a fork bomb.


# History

## 0.0.1 (2018-09-22)

* First release on PyPI.