Re: using PIL for PCA analysis
if i want to do an array of PIL image data i can use img=Image.open(myimg.jpg) .convert(L) pixelarray=img.getdata() convert(L) is a good way to make images grayscale. An option to using getdata() is to try numpy's array: pixelarray = numpy.array(img) this gives lots of possibilities for working with the images numerically, like for PCA. (see example code in the link below) thus i guess i can build a matrix of a set of images is there something wrong in the way i do this above?may be i can use that to find covariance matrix for the set of images? I wrote a short script for doing PCA on images using python, with some explanations and example code http://jesolem.blogspot.com/2009/01/pca-for-images-using-python.html here . Could be of help to you guys. -- View this message in context: http://www.nabble.com/using-PIL-for-PCA-analysis-tp15606311p21432675.html Sent from the Python - python-list mailing list archive at Nabble.com. -- http://mail.python.org/mailman/listinfo/python-list
Re: using PIL for PCA analysis
Paul McGuire wrote # following approx fromhttp://www.dfanning.com/ip_tips/color2gray.html grayscale = lambda (R,G,B) : int(0.3*R + 0.59*G + 0.11*B) print [ [ grayscale(rgb) for rgb in row ] for row in sampledata ] Paul in PIL handbook ,they mention a Luma transform on page15, under the im.convert() section.. L = R * 299/1000 + G * 587/1000 + B * 114/1000 is that not similar to what you mentioned?(I am newbie in this area..) if i want to do an array of PIL image data i can use img=Image.open(myimg.jpg) .convert(L) pixelarray=img.getdata() thus i guess i can build a matrix of a set of images is there something wrong in the way i do this above?may be i can use that to find covariance matrix for the set of images? H -- http://mail.python.org/mailman/listinfo/python-list
Re: using PIL for PCA analysis
Hi,
You should convert your data to numpy and make it 1D (for the moment, it is
3D) by calling the ravel() method. Then you can create your covariance
matrix ;)
Matthieu
2008/2/21, [EMAIL PROTECTED] [EMAIL PROTECTED]:
hi guys
i am trying out PCA analysis using python.I have a set of
jpeg(rgbcolor) images whose pixel data i need to extract and make a
matrix .( rows =num of images and cols=num of pixels)
For this i need to represent an image as an array.
i was able to do this using java's BufferedImage as below
javacode
int[] rgbdata = new int[width * height];
image.getRGB(0,0,width,height,rgbdata,0,width);
doubles = new double[rgbdata.length];
int i;
for ( i = 0; i bytes.length; i++) {
doubles[i] = (double)(rgbdata[i]);
}
/javacode
this doubles[] now represent a single image's pixels
then i can get a matrix of say 4 images ..(each of 4X3 size)
sampledata
images[][] rows=4,cols=12
[
[-4413029.0, -1.0463919E7,... -5201255.0]
[-5399916.0, -9411231.0, ... -6583163.0]
[-3886937.0, -1.0202292E7,... -6648444.0]
[-5597295.0, -7901339.0,... -5989995.0]
]
/sampledata
i can normalise the above matrix to zeromean and then find covariance
matrix by
images * transpose(images)
my problem is how i can use PIL to do the same thing..if i extract
imagedata using im.getdata()
i will get
sampledata
[
[(188, 169, 155), (96, 85, 81),.. (176, 162, 153)]
[(173, 154, 148), (112, 101, 97),.. (155, 140, 133)]
[(196, 176, 167), (100, 83, 76), ... (154, 141, 132)]
[(170, 151, 145), (135, 111, 101), ... (164, 153, 149)]
]
/sampledata
i donot know how to find covariance matrix from such a matrix..it
would'v been ideal if they were single values instead of tuples..i
can't use greyscale images since the unput images are all rgb jpeg
can someone suggest a solution?
thanks
dn
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French PhD student
Website : http://matthieu-brucher.developpez.com/
Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92
LinkedIn : http://www.linkedin.com/in/matthieubrucher
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RE: using PIL for PCA analysis
Since nobody has responded to this:
I know nothing about PIL, but you can do this using numpy and scipy
fairly easily, and you can transform PIL arrays into Numpy arrays pretty
quickly as well.
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Sent: Thursday, February 21, 2008 2:41 AM
To: python-list@python.org
Subject: using PIL for PCA analysis
hi guys
i am trying out PCA analysis using python.I have a set of
jpeg(rgbcolor) images whose pixel data i need to extract and make a
matrix .( rows =num of images and cols=num of pixels) For this i need to
represent an image as an array.
i was able to do this using java's BufferedImage as below
javacode
int[] rgbdata = new int[width * height];
image.getRGB(0,0,width,height,rgbdata,0,width);
doubles = new double[rgbdata.length];
int i;
for ( i = 0; i bytes.length; i++) {
doubles[i] = (double)(rgbdata[i]);
}
/javacode
this doubles[] now represent a single image's pixels
then i can get a matrix of say 4 images ..(each of 4X3 size)
sampledata images[][] rows=4,cols=12 [ [-4413029.0, -1.0463919E7,...
-5201255.0]
[-5399916.0, -9411231.0, ... -6583163.0]
[-3886937.0, -1.0202292E7,... -6648444.0]
[-5597295.0, -7901339.0,... -5989995.0]
]
/sampledata
i can normalise the above matrix to zeromean and then find covariance
matrix by images * transpose(images)
my problem is how i can use PIL to do the same thing..if i extract
imagedata using im.getdata() i will get sampledata [ [(188, 169, 155),
(96, 85, 81),.. (176, 162, 153)]
[(173, 154, 148), (112, 101, 97),.. (155, 140, 133)]
[(196, 176, 167), (100, 83, 76), ... (154, 141, 132)]
[(170, 151, 145), (135, 111, 101), ... (164, 153, 149)] ] /sampledata
i donot know how to find covariance matrix from such a matrix..it
would'v been ideal if they were single values instead of tuples..i can't
use greyscale images since the unput images are all rgb jpeg
can someone suggest a solution?
thanks
dn
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Re: using PIL for PCA analysis
On Feb 21, 7:35 pm, Bronner, Gregory [EMAIL PROTECTED] wrote: you can do this using numpy and scipy fairly easily, and you can transform PIL arrays into Numpy arrays pretty quickly as well. i can use numpy ndarray or matrix once i have a PIL array with elements in the correct format(ie a single number for each pixel instead of a tuple of integers) it is the image data extraction step that is giving me the problem ie i want PIL to return an image as something like [-4413029.0, -1.0463919E7,... -5201255.0] instead of [(188, 169, 155), (96, 85, 81),.. (176, 162, 153)] Any PIL experts please help dn -- http://mail.python.org/mailman/listinfo/python-list
Re: using PIL for PCA analysis
On Feb 21, 1:41 am, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:
hi guys
i am trying out PCA analysis using python.I have a set of
jpeg(rgbcolor) images whose pixel data i need to extract and make a
matrix .( rows =num of images and cols=num of pixels)
For this i need to represent an image as an array.
i was able to do this using java's BufferedImage as below
javacode
int[] rgbdata = new int[width * height];
image.getRGB(0,0,width,height,rgbdata,0,width);
doubles = new double[rgbdata.length];
int i;
for ( i = 0; i bytes.length; i++) {
doubles[i] = (double)(rgbdata[i]);}
/javacode
this doubles[] now represent a single image's pixels
then i can get a matrix of say 4 images ..(each of 4X3 size)
sampledata
images[][] rows=4,cols=12
[
[-4413029.0, -1.0463919E7,... -5201255.0]
[-5399916.0, -9411231.0, ... -6583163.0]
[-3886937.0, -1.0202292E7,... -6648444.0]
[-5597295.0, -7901339.0,... -5989995.0]
]
/sampledata
i can normalise the above matrix to zeromean and then find covariance
matrix by
images * transpose(images)
my problem is how i can use PIL to do the same thing..if i extract
imagedata using im.getdata()
i will get
sampledata
[
[(188, 169, 155), (96, 85, 81),.. (176, 162, 153)]
[(173, 154, 148), (112, 101, 97),.. (155, 140, 133)]
[(196, 176, 167), (100, 83, 76), ... (154, 141, 132)]
[(170, 151, 145), (135, 111, 101), ... (164, 153, 149)]
]
/sampledata
i donot know how to find covariance matrix from such a matrix..it
would'v been ideal if they were single values instead of tuples..i
can't use greyscale images since the unput images are all rgb jpeg
can someone suggest a solution?
thanks
dn
I'm surprised PIL doesn't have a grayscale conversion, but here is one
that can manipulate your RGB values:
sampledata = [
[(188, 169, 155), (96, 85, 81), (176, 162, 153)],
[(173, 154, 148), (112, 101, 97), (155, 140, 133)],
[(196, 176, 167), (100, 83, 76), (154, 141, 132)],
[(170, 151, 145), (135, 111, 101), (164, 153, 149)],
]
# following approx from http://www.dfanning.com/ip_tips/color2gray.html
grayscale = lambda (R,G,B) : int(0.3*R + 0.59*G + 0.11*B)
print [ [ grayscale(rgb) for rgb in row ] for row in sampledata ]
prints (reformatted to match your sampledata):
[
[173, 87, 165],
[159, 103, 143],
[181, 87, 143],
[156, 117, 155]
]
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