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 a 3-d array to hold this kind of data:
image_block = np.empty((100, 512, 512), dtype=??)
now you can load it up by using some lib (PIL, or ???) to load the tif
images, and then:
for i in images:
image_block[i,:,:] = i
note that I put dtype to ??? up there. What dtype you want is dependent
on what's in the tiff images -- tiff can hold just about anything. So if
they are say, 16 bit greyscale, you'd want:
dtype=np.uint16
if they are 24 bit rgb, you might want a custom dtype (I don't think
there is a 24 bit dtype built in):
RGB_type = np.dtype([('r',np.uint8),('g',np.uint8),('b',np.uint8)])
for 32 bit rgba, you can use the same approach, or just a 32 bit integer.
The cool thing is that you can make views of this array with different
dtypes, depending on what's easiest for the given use case. You can even
break out the rgb parts into different axis:
image_block = np.empty((100, 512, 512), dtype=RGB_type)
image_block_rgb=image_block.view(dtype=np.uint8).reshape((100,512,512,3))
The two arrays now share the same data block, but you can look at them
differently.
I think this a really cool feature of numpy.
> i then would like to use visvis for visualizing this in 3D.
you'll have to see what visvis is expecting in terms of data types, etc.
HTH,
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
chris.bar...@noaa.gov
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