2011/2/1 Asmi Shah <asmi.ca...@gmail.com>: > Thanks a lot Friedrich and Chris.. It came in handy to use PIL and numpy.. > :)
:-) > I have one more question: how to avoid the limitation of memoryerror in > numpy. as I have like 200 images to stack in the numpy array of say > 1024x1344 resolution.. have any idea apart from downsampling? In case you *have* to downsample: I also ran into this, with the example about my 5 images ... im.resize((newx newy), PIL.Image.ANTIALIAS) will be your friend. http://www.pythonware.com/library/pil/handbook/image.htm. Note, you might take advatage of floating-point images ('F' spec), I don't know what the trade-offs are here. 'F' most probably takes 4x(8bit), so ... The PIL handbook does not state what PIL.Image.ANTIALIAS actually does, we can only hope that it's real sinc interpolation or similar (if your images are frequency bounded this would be best to my knowledge). In this case you do not even lose information as long as the spacial resolution of the downsampled images is still sufficient to make the signal frequency bounded. You might do a FFT (spacial) to check if your images *are* actually bounded in frequency domain. I think it does not need to be perfect. I strongly believe sinc is in scipy, but I never looked for. Friedrich _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion