On Wed, May 21, 2008 at 2:03 AM, Vincent Schut <[EMAIL PROTECTED]> wrote:
> Robert Kern wrote:
>> On Wed, May 21, 2008 at 1:48 AM, Vincent Schut <[EMAIL PROTECTED]> wrote:
>>> Christopher Barker wrote:
>>
>>>> Also, if you image data is rgb, usually, that's a (width, height, 3)
>>>> array: rgbrgbrgbrgb... in memory. If you have a (3, width, height)
>>>> array, then that's rrrrrrr....gggggggg......bbbbbbbb. Some image libs
>>>> may give you that, I'm not sure.
>>> My data is. In fact, this is a simplification of my situation; I'm
>>> processing satellite data, which usually has more (and other) bands than
>>> just rgb. But the data is definitely in shape (bands, y, x).
>>
>> I don't think record arrays will help you much, then. Individual
>> records need to be contiguous (bar padding). You can't interleave
>> them.
>>
> Hmm, that was just what I was wondering about, when reading Stefan's
> reply. So in fact, recarrays aren't just another way to view some data,
> no matter in what shape it is.
>
> So his solution:
> x.T.reshape((-1,x.shape[0])).view(dt).reshape(x.shape[1:]).T won't work,
> than. Or, at least, won't give me a view on my original dat, but would
> give me a recarray with a copy of my data.
Right.
> I guess I was misled by this text on the recarray wiki page:
>
> "We would like to represent a small colour image. The image is two
> pixels high and two pixels wide. Each pixel has a red, green and blue
> colour component, which is represented by a 32-bit floating point number
> between 0 and 1.
>
> Intuitively, we could represent the image as a 3x2x2 array, where the
> first dimension represents the color, and the last two the pixel
> positions, i.e. "
>
> Note the "3x2x2", which suggested imho that this would work with an
> image with (bands,y,x) shape, not with (x,y,bands) shape.
Yes, the tutorial goes on to use record arrays as a view onto an
(x,y,bands) array and also make a (bands,x,y) view from that, too.
That is, in fact, quite a confusing presentation of the subject.
Now, there is a way to use record arrays here; it's a bit ugly but can
be quite useful when parsing data formats. Each item in the record can
also be an array. So let's pretend we have a (3,nx,ny) RGB array.
nbands, nx, ny = a.shape
dtype = numpy.dtype([
('r', a.dtype, [nx, ny]),
('g', a.dtype, [nx, ny]),
('b', a.dtype, [nx, ny]),
])
# The flatten() is necessary to pre-empt numpy from
# trying to do too much interpretation of a's shape.
rec = a.flatten().view(dtype)
print rec['r']
print rec['g']
print rec['b']
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
