redpil 0.0.2

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

redpil

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

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 PIL backend. This is running on my SSD, a Samsung 960 Pro 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.