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I have a folder with a lot of images that I want to use to bild a classificator using a SVM model in python with sklearn. I've always used csv file as train/test set with sklearn, how can I make it? (a csv file with records corrisponding to images and a variable for every pixel)

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    $\begingroup$ Two questions: 1) What is the format (.png, .jpeg, .pdf, etc) of your images? 2) How are the labels stored (is it the name of the folder they are in)? $\endgroup$ Nov 30 '18 at 19:38
  • $\begingroup$ 1)jpeg 2)yes, there are two classes stored in two different folders $\endgroup$
    – user254087
    Dec 1 '18 at 8:29
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You are describing a one-time pre-processing step that will crawl through your folder and turn each image into a line of data and then save the entire collection in a CSV file. In turn, that file becomes your gold standard dataset.

If I was in your position, I would look into the Keras pre-processing tools that already provide python libraries to quickly do this task. It's a common need for image processing, the Keras library is very mature and can do this for you.

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It should be something like this:

  1. Read image with Image.open()
  2. Convert to np.array()
  3. Flat the previous 3D array (height x width x channels) into 1D array
  4. Collect all the 1D arrays into list
  5. Convert list into np.array, resulting in 2D array (images x pixels)

Note: the code below is not tested

import glob
import PIL
import numpy

data = np.array([ np.array(PIL.Image.open(f).convert("RGB")).ravel() 
                  for f in glob.glob("./folder/*.jpeg") ])
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