cp wrote:
>>> arr=asarray(img)
>>> arr.shape
>>> (1600,1900,3)
>
>> No, it means that you have 1600 rows, 1900 columns and 3 colour channels.
>
> According to scipy documentation at
> http://pages.physics.cornell.edu/~myers/teaching/ComputationalMethods/python/arrays.html
> you are right.
>
> In
> > arr=asarray(img)
> > arr.shape
> > (1600,1900,3)
> No, it means that you have 1600 rows, 1900 columns and 3 colour channels.
According to scipy documentation at
http://pages.physics.cornell.edu/~myers/teaching/ComputationalMethods/python/arrays.html
you are right.
In this case I import numpy
On Wed, May 27, 2009 at 5:12 PM, cp wrote:
> Hi,
> I'm using PIL for image processing, but lately I also try numpy for the
> flexibility and superior speed it offers. The first thing I noticed is that
> for
> an RGB image with height=1600 and width=1900 while
>
> img=Image.open('something.tif')
>
2009/5/27 cp :
> img=Image.open('something.tif')
> img.size
> (1900,1600)
>
> then
>
> arr=asarray(img)
> arr.shape
> (1600,1900,3)
>
> This means that the array-image has 1600 color channels, 1900 image pixel rows
> and 3 image pixel columns. Why is that?
No, it means that you have 1600 rows, 190
Hi,
I'm using PIL for image processing, but lately I also try numpy for the
flexibility and superior speed it offers. The first thing I noticed is that for
an RGB image with height=1600 and width=1900 while
img=Image.open('something.tif')
img.size
(1900,1600)
then
arr=asarray(img)
arr.shape
(160
