[SciPy-user] saving raw image

Vincent Schut schut@sarvision...
Thu Oct 16 03:39:26 CDT 2008


Numpy int's are int32 by default, so in your '.astype(int)' you're 
casting to int32. If you want a different type, you need to specify the 
number of bits. As a side note, you're reading as *unsigned* integer 
(uint16) and then converting to *signed* int32. Try "Raw = 
fromstring(f0, uint16).astype(uint16)" to keep the original (unsigned 
int16) datatype.

Vincent.

Sahar Vilan wrote:
> Hi Zach,
> Thanks for your help.
> 
> I used your advices and got an image I can view. However, its size doubles
> and I have to open it as 32 bit while the original looks fine as 16 bit.
> Here is the code I used:
> # ************************************************
> from scipy import *
> 
> # read image
> f0 = file('Image0.raw', 'rb').read()
> Raw = fromstring(f0, uint16).astype(int)
> Im = Raw.reshape([1024, 1024], order='F')
> 
> # save image
> Im_str = Im.tostring(order='F')
> f1 = file( 'Image1.raw', 'wb').write(Im_str)
> 
> # ************************************************
> Thanks again,
> Sahar
> 
> -----Original Message-----
> From: scipy-user-bounces@scipy.org [mailto:scipy-user-bounces@scipy.org]On
> Behalf Of Zachary Pincus
> Sent: Wed, October 15, 2008 4:21 PM
> To: SciPy Users List
> Subject: Re: [SciPy-user] saving raw image
> 
> 
> Hi Sahar,
> 
> Please send a minimal example of loading and saving an array that
> produces "garbage" output, if you could... (If you could send a small
> input file that would be helpful too.)
> 
> Also, does the garbage you see in ImageJ have some structure -- does
> it look streaky, like you can see rows of pixels that should be
> together, but the rows don't line up right?
> 
> One possible problem is that the array you are saving has been
> promoted to a different dtype (e.g. by participating in signed/float
> arithmetic), so the pixels you save are no longer uint16s. Unless you
> have an 'astype(uint16)' in your save code, this could be the issue,
> since the loading code you showed does immediately convert the array
> from uint16s. (This is why sending a complete example of the failure
> is useful...)
> 
> Another possible problem has to do with the order the pixels are read/
> written out. Typically, images on disk are stored as rows of pixels
> next to one another -- this is "column major" or "fortran" order
> (going from one memory location to the next typically increments the x-
> value, except at row boundaries where the y-value is incremented, so
> it is said that the x-value "varies the fastest"). Typically, numpy
> arrays are created in row major or "C" order, where the y-value varies
> the fastest. When loading and manipulating images, you need either to
> make sure that the images are loaded in fortran-order, or reverse the
> x/y coordinates and shape.
> 
> So, e.g. when loading a 200x300 image:
> 
> 
> s = file(path, 'rb').read()
> raw = fromstring(s, uint16).astype(int)
> image = raw.reshape((200,300), order='F')
> 
> now, image[30,40] gives the same pixel as coordinate (30,40) in ImageJ.
> 
> If you did:
> image = raw.reshape((300,200), order='C')
> then image[30,40] would give the same pixel as coordinate (40,30) in
> ImageJ.
> 
> Note that the 'C' order is default. So:
> image = raw.reshape((200,300))
> will give garbage, with the rows of pixels broken up along the wrong
> boundaries, giving rise to the "streaky" images I mentioned.
> 
> Finally, the tostring() method also takes an order option, so for
> saving images, you need:
> raw = image.tostring(order='F')
> (assuming that you reshaped the image as order 'F')
> 
> Zach Pincus
> 
> 
> 
> On Oct 15, 2008, at 4:49 AM, Sahar Vilan wrote:
> 
>> I used scipy for basic image processing of raw images, and I can't
>> save
>> these images in the same format:
>> To open image I use:
>> 	s = file(path, 'rb').read()
>>    	raw = fromstring(s, uint16).astype(int)
>>
>> I try to save this matrix as raw image again but I get some garbage
>> when I
>> open it in some viewer (Image-J, for instance).
>> Can anyone help me with this?
>>
>> Thanks,
>> Sahar
> 
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