Jesper Larsen <jesper.webm...@gmail.com> writes: > Unfortunately the files are quite big (up to ~300 kb). I have however > tried using the Linux tool pngnq to reduce the file size with a factor > ~3-4 with almost no degradation of the result.
> Pixel depth (Pixel Depth): 32 > Colour Type (Photometric Interpretation): RGB with alpha channel > Pixel depth (Pixel Depth): 8 > Colour Type (Photometric Interpretation): PALETTED COLOUR (256 > colours, 0 transparent) This means pngnq has quantized the original RGBA image with 8 bits per channel to an image with a 256-color palette. I don't think Agg has any support for rendering directly to a paletted image, so to achieve similar results, you would have to do the quantization in a separate pass anyway. > I am not using transparency for anything. For a web application a > reduction from 300 kb to 90 kb is really important so I hope you have > some good ideas. A web application needs to be fast, right? According to its home page, pngnq "is limited mostly to off-line uses rather than real time image delivery". You could take a look at PIL to see if it has any fast quantization algorithms, and pass your result to it as in the to_numeric.py example (see also webapp_demo.py for how to avoid using the pylab machinery for figure management). If not, you could always implement some fast quantization algorithm in numpy: http://en.wikipedia.org/wiki/Color_quantization My guess is that if you always produce similar-looking images, you could fix the palette off-line using whatever fancy algorithm you like, and then the actual conversion could be done pretty fast, especially if you can forgo dithering - perhaps for many types of charts it is not necessary. -- Jouni K. Seppänen http://www.iki.fi/jks ------------------------------------------------------------------------------ Crystal Reports - New Free Runtime and 30 Day Trial Check out the new simplified licensing option that enables unlimited royalty-free distribution of the report engine for externally facing server and web deployment. http://p.sf.net/sfu/businessobjects _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
