A Wednesday 02 February 2011 18:12:47 Christopher Barker escrigué:
One other option, that I've never tried, is carray, which is an array
compressed in memory. Depending on your images, perhaps they would
compress a lot (or not ):
https://github.com/FrancescAlted/carray
Hi all,
It seems that using 64 bit python is the solution. But the thing is i would
compile my code and wanna distribute it to the clients.. and that is the
only reason why i want to work on 32 bit system. Sturla, how I can make it
sure that some part of the data is kept on the disk and only the
On Wed, Feb 2, 2011 at 8:22 AM, Asmi Shah asmi.ca...@gmail.com wrote:
Hi all,
It seems that using 64 bit python is the solution. But the thing is i would
compile my code and wanna distribute it to the clients.. and that is the
only reason why i want to work on 32 bit system. Sturla, how I can
It seems that using 64 bit python is the solution.
It's certainly the easy way to access a lot of memory -- and memory is
cheap these days.
But the thing is i would
compile my code and wanna distribute it to the clients..
I don't think 64 bit gets in the way of that -- except that it will
Thanks a lot Friedrich and Chris.. It came in handy to use PIL and numpy..
:)
@Zach, m aware of the poor handling of 16bit images in PIL, for that I am
using imagemagick to convert it into 8 bit first and then PIL for rest of
the processing..
I have one more question: how to avoid the limitation
Hi,
On Tue, Feb 1, 2011 at 6:39 AM, Asmi Shah asmi.ca...@gmail.com wrote:
Thanks a lot Friedrich and Chris.. It came in handy to use PIL and numpy..
:)
@Zach, m aware of the poor handling of 16bit images in PIL, for that I am
using imagemagick to convert it into 8 bit first and then PIL for
Den 1. feb. 2011 kl. 11.20 skrev totonixs...@gmail.com totonixs...@gmail.com
:
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?
Take
of Numerical Python numpy-discussion@scipy.org
Date: Tue, 1 Feb 2011 14:49:39 +0100
Subject: Re: [Numpy-discussion] create a numpy array of images
Den 1. feb. 2011 kl. 11.20 skrev totonixs...@gmail.com
totonixs...@gmail.com:
I have one more question: how to avoid the limitation of memoryerror in
numpy
Den 01.02.2011 15:07, skrev Asmi Shah:
Hi Zach and Sturla,
Well I am a she :))
I apologize, I did not deduce correct gender from your name :)
Thanks for your inputs.. I am using 32 bit python as have so many
libraries integrated with it.. and moreover, i plan to put this volume
rendered
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
On 2/1/11 12:39 AM, Asmi Shah wrote:
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?
If I'm doing my math right, that's 262 MB, shouldn't be
On 2/1/11 8:31 AM, Friedrich Romstedt wrote:
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.
If you want to downsample by
A Tuesday 01 February 2011 19:58:16 Sturla Molden escrigué:
Den 01.02.2011 18:58, skrev Christopher Barker:
But if you really have big collections of images, you might try
memory mapped arrays -- as Sturla pointed out they wont' let you
create monster arrays on a 32 bit python,
But they
I've been done that but with CT and MRI dicom files, and the cool
thing is that with numpy I can do something like this:
# getting axial slice
axial = slices[n,:,:]
# getting coronal slice
coronal = slices[:, n, :]
# getting sagital slice
sagital = slices[:,:, n]
On Sun, Jan 30, 2011 at 5:29
I am using python for a while now and I have a requirement of
creating a
numpy array of microscopic tiff images ( this data is 3d, meaning
there are
100 z slices of 512 X 512 pixels.) How can I create an array of
images?
It's quite straightforward to create a 3-d array to hold this
2011/1/28 Christopher Barker chris.bar...@noaa.gov:
On 1/28/11 7:01 AM, Asmi Shah wrote:
I am using python for a while now and I have a requirement of creating a
numpy array of microscopic tiff images ( this data is 3d, meaning there are
100 z slices of 512 X 512 pixels.) How can I create an
Hi guys,
I am using python for a while now and I have a requirement of creating a
numpy array of microscopic tiff images ( this data is 3d, meaning there are
100 z slices of 512 X 512 pixels.) How can I create an array of images? i
then would like to use visvis for visualizing this in 3D.
any
On 1/28/11 7:01 AM, Asmi Shah wrote:
I am using python for a while now and I have a requirement of creating a
numpy array of microscopic tiff images ( this data is 3d, meaning there are
100 z slices of 512 X 512 pixels.) How can I create an array of images?
It's quite straightforward to create
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