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
> http://mail.
> 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
On Wed, Feb 2, 2011 at 8:22 AM, Asmi Shah 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 make it
> sure th
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 n
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,
>
> B
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 will on 64 bit Python :D We can just memory map a temporary
fil
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
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
2011/2/1 Asmi Shah :
> 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 f
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
al Python
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
>
>>
Den 1. feb. 2011 kl. 11.20 skrev "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 a look at nu
Hi,
On Tue, Feb 1, 2011 at 6:39 AM, Asmi Shah 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 rest of
> the proc
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
>>> 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
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 PM
2011/1/28 Christopher Barker :
> 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?
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 crea
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 hel
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