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pythonmachine-learningsvmlibsvm

SVM to gender recognition


i've been working this weeks in a gender recognition project (in python) using at first: Fisherfaces as Feature Extraction method and 1-NN classifier with Euclidean Distance but now i though it was not enough reliable (in my humble opinion) so i'm about to use SVM but im lost when i have to create and train a model to use it in my dataset of images, but i can't find the solution for the commands i need in http://scikit-learn.org. I've tried with this code but it doesn't work, dunno why i have this error while executing:

  File "prueba.py", line 46, in main
    clf.fit(R, r)
  File "/Users/Raul/anaconda/lib/python2.7/site-packages/sklearn/svm/base.py", line 139, in fit    
 X = check_array(X, accept_sparse='csr', dtype=np.float64, order='C')
  File "/Users/Raul/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py", line 350, in check_array
    array.ndim)
ValueError: Found array with dim 3. Expected <= 2

And this is my code:

import os, sys
import numpy as np
import PIL.Image as Image
import cv2
from sklearn import svm


def read_images(path, id, sz=None):
    c = id
    X,y = [], []
    for dirname, dirnames, filenames in os.walk(path):
        for subdirname in dirnames:
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):
                try:
                    im = Image.open(os.path.join(subject_path, filename))
                    im = im.convert("L")
                    # resize to given size (if given)
                    if (sz is not None):
                        im = im.resize(sz, Image.ANTIALIAS)
                    X.append(np.asarray(im, dtype=np.uint8))
                    y.append(c)
                except IOError as e:
                    print "I/O error({0}): {1}".format(e.errno, e.strerror)
                except:
                    print "Unexpected error:", sys.exc_info()[0]
                    raise
                        #c = c+1
    return [X,y]


def main():
    # check arguments
    if len(sys.argv) != 3:
        print "USAGE: example.py </path/to/images/males> </path/to/images/females>"
        sys.exit()
    # read images and put them into Vectors and id's
    [X,x] = read_images(sys.argv[1], 1)
    [Y, y] = read_images(sys.argv[2], 0)
    # R all images and r all id's
    [R, r] = [X+Y, x+y]
    clf = svm.SVC()
    clf.fit(R, r)





if __name__ == '__main__':
    main()

I'd appreciate any kind of help in how can i make gender recognition with SVM Thanks for reading


Solution

  • X.append(np.asarray(im, dtype=np.uint8))
    

    I guess this is appending a 2d-array. You might want to flatten it before appending so that each instance becomes this looking:

    array([255, 255, 255, ..., 255, 255, 255], dtype=uint8)
    

    instead of:

    array([
       [255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255],
       [255,   0,   0, ...,   0,   0,   0],
       ..., 
       [255,   0,   0, ...,   0,   0,   0],
       [255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)
    

    Try this:

    X.append(np.asarray(im, dtype=np.uint8).ravel())