Principal Component Analysis(PCA) of Images in Python

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Principal Component Analysis of Images. First we've to convert the images into gray scale images. I got the code from a book Programming Computer Vision with Python by Jan Erik Solem, I've just added few lines to give path for the images and perform the PCA and saved the data for the further analysis. In the next video I will show you the face recognition with this.

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It’s awesome, man! It can be applied in thermography images with really good results for thermal anomalies identification.

lucianostaffa

can't hear a word you are saying bro

kmillanr

Hey @casual_coding,

Thank you for your tutorial video. However, I've got an error and I hope you could help.
For this part of the code:
immatrix = for im in imlist], 'f')

The error:
immatrix = for im in imlist], 'f')
File "C:\Python27\lib\site-packages\numpy\core\numeric.py", line 531, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: setting an array element with a sequence.

Hope you could help. thank you

tanboiboi

Hello @casual_coding,

Thanks for the video. I would like to apply 2 PCAs in Python to a dataset I have that includes the daily rainfall in various places of a country for a period of more than 30 years. I would like to see which places have similar rainfall (first PCA) and if which years have had similar rainfall (second PCA). Thanks again!

fabiolaespinoza

What would have happened if you use images with different dimensions? (Pixels)

alvaronunezmendoza

@casual_coding
i think so the below part of the code in test.py
# perform PCA
V, S, immean = pca.pca(immatrix)
is wrong (i.e i didnt understand why used pca.pca in the above line)

mdbilal

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