> This example assumes that facearray is an ndarray.(like you described
> in original post ;-) ) It looks like you are using a matrix.
hi Arnar
thanks ..
a few doubts however
1.when i use say 10 images of 4X3 each
u, s, vt = linalg.svd(facearray, 0)
i will get vt of shape (10,12)
can't i take th
I think that is correct...
Here is what the final result should look like:
http://www.datawrangling.com/media/images/first_16.png
If the dimensions for the sample faces don't work out to ( 361 x 361 ) in
the end, then you are likely to be missing a transpose somewhere. Also, be
aware that the s
On 01/03/2008, Charles R Harris <[EMAIL PROTECTED]> wrote:
> > On Fri, Feb 29, 2008 at 10:53 AM, John Hunter <[EMAIL PROTECTED]> wrote:
> > > I have a boolean array and would like to find the lowest index "ind"
> > > where N contiguous elements are all True. Eg, if x is
[...]
> Oops, ind = aran
On Fri, Feb 29, 2008 at 11:12 PM, Charles R Harris <
[EMAIL PROTECTED]> wrote:
>
>
> On Fri, Feb 29, 2008 at 10:53 AM, John Hunter <[EMAIL PROTECTED]> wrote:
>
> > [apologies if this is a resend, my mail just flaked out]
> >
> > I have a boolean array and would like to find the lowest index "ind"
On Fri, Feb 29, 2008 at 10:53 AM, John Hunter <[EMAIL PROTECTED]> wrote:
> [apologies if this is a resend, my mail just flaked out]
>
> I have a boolean array and would like to find the lowest index "ind"
> where N contiguous elements are all True. Eg, if x is
>
> In [101]: x = np.random.rand(20)
On Mar 1, 12:57 am, "Peter Skomoroch" wrote:
I think
> > matlab example should be easy to translate to scipy/matplotlib using the
> > montage function:
>
> > load faces.mat
> > %Form covariance matrix
> > C=cov(faces');
> > %build eigenvectors and eigenvalues
> > [E,D] = eig(C);
hi Peter,
nice
At 10:00 AM 2/29/2008, you wrote:
> Robin wrote
> I'm not sure why they would be doing this - to me it looks they might
> be using Image as a convenient way to store some other kind of data...
thanks Robin,
I am wondering if there is a more straightforward way to do these..
especially the vector
Forgot the url:
http://www.cis.hut.fi/Opinnot/T-61.2010/harjoitustyo_en07.shtml
On Fri, Feb 29, 2008 at 2:56 PM, Peter Skomoroch <[EMAIL PROTECTED]>
wrote:
> Here is the page I referenced for the octave version ... it includes
> examples very similar to what you want. I will be posting a very s
Here is the page I referenced for the octave version ... it includes
examples very similar to what you want. I will be posting a very similar
example in Python later this month.
I don't have any Python code on hand for the Petland paper, but I think
matlab example should be easy to translate to s
RoyG,
The timing of your question couldn't be better, I just did an blog post on
this (I also plugged scipy and the EPD):
http://www.datawrangling.com/python-montage-code-for-displaying-arrays.html
The code basically replicates the matlab montage() function and approach to
handling grayscale ima
hi guys
I have a set of face images with which i want to do face recognition
using Petland's PCA method.I gathered these steps from their docs
1.represent matrix of face images data
2.find the adjusted matrix by substracting the mean face
3.calculate covariance matrix (cov=A* A_transpose) where
[EMAIL PROTECTED] wrote:
>> Robin wrote
>> I'm not sure why they would be doing this - to me it looks they might
>> be using Image as a convenient way to store some other kind of data...
>
> thanks Robin,
> I am wondering if there is a more straightforward way to do these..
> especially the vector
On Fri, Feb 29, 2008 at 11:53 AM, John Hunter <[EMAIL PROTECTED]> wrote:
> [apologies if this is a resend, my mail just flaked out]
>
> I have a boolean array and would like to find the lowest index "ind"
> where N contiguous elements are all True. Eg, if x is
>
> In [101]: x = np.random.rand(2
> Robin wrote
> I'm not sure why they would be doing this - to me it looks they might
> be using Image as a convenient way to store some other kind of data...
thanks Robin,
I am wondering if there is a more straightforward way to do these..
especially the vector to image function
D
__
[apologies if this is a resend, my mail just flaked out]
I have a boolean array and would like to find the lowest index "ind"
where N contiguous elements are all True. Eg, if x is
In [101]: x = np.random.rand(20)>.4
In [102]: x
Out[102]:
array([False, True, True, False, False, True, True, F
On Fri, Feb 29, 2008 at 2:54 PM, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote:
> hi
> i came across a codebase by rice univ people..in that there are some
> functions for conversion btw image and vectors
I'm not an expert by any means but I thought I'd try and help...
> 1.
> def image_to_vecto
hi
i came across a codebase by rice univ people..in that there are some
functions for conversion btw image and vectors
1.
def image_to_vector(self, filename):
try:
im = Image.open(filename)
except IOError:
print 'couldn\'t load ' + filename
sys.e
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Neal Becker wrote:
> Sounds like this needs a bit of re-thinking.
>
> Given a set of function signatures:
> F(a,b,c)
> F(d,e,f)
> ...
>
> The user calls:
> F(A,B,C) (no relation between a,A ,etc)
>
> How do we find the 'best' match?
>
> I think we can start with:
> Rules:
> 1) Only allowed (at
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