eigensave.py

#!/usr/bin/env python
# Adapted from http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/python2/facerec_demo.py
# By Terence Eden (edent)
#
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Philipp Wagner <bytefish[at]gmx[dot]de>.
# All rights reserved.
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# modification, are permitted provided that the following conditions
# are met:
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# ------------------------------------------------------------------------------------------------
# Note:
# When using the FaceRecognizer interface in combination with Python, please stick to Python 2.
# Some underlying scripts like create_csv will not work in other versions, like Python 3.
# ------------------------------------------------------------------------------------------------

import os
import sys
import cv2
import numpy as np
import pickle

def normalize(X, low, high, dtype=None):
    """Normalizes a given array in X to a value between low and high."""
    X = np.asarray(X)
    minX, maxX = np.min(X), np.max(X)
    # normalize to [0...1].
    X = X - float(minX)
    X = X / float((maxX - minX))
    # scale to [low...high].
    X = X * (high-low)
    X = X + low
    if dtype is None:
        return np.asarray(X)
    return np.asarray(X, dtype=dtype)


def read_images(path, sz=None):
    namelookup = {}
    X,y = [], []
    count = 0
    for dirname, dirnames, filenames in os.walk(path):
        for subdirname in dirnames:
            namelookup[str(count)] = subdirname
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):
                try:
                    im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
                    # resize to given size (if given)
                    if (sz is not None):
                        im = cv2.resize(im, sz)
                    X.append(np.asarray(im, dtype=np.uint8))
                    y.append(count)
                except IOError, (errno, strerror):
                    print "I/O error({0}): {1}".format(errno, strerror)
                except:
                    print "Unexpected error:", sys.exc_info()[0]
                    raise
            count = count+1
    pickle.dump(namelookup, open('namelookup.pkl', "wb"))
    return [X,y]

if __name__ == "__main__":
    if len(sys.argv) < 1:
        print "USAGE: eigensave.py </path/to/images>"
        sys.exit()
    # Now read in the image data. This must be a valid path!
    [X,y] = read_images(sys.argv[1], (256,256))
    # Convert labels to 32bit integers. This is a workaround for 64bit machines,
    y = np.asarray(y, dtype=np.int32)

    # Create the Eigenfaces model.
    model = cv2.createEigenFaceRecognizer()
    # Learn the model. Remember our function returns Python lists,
    # so we use np.asarray to turn them into NumPy lists to make
    # the OpenCV wrapper happy:
    model.train(np.asarray(X), np.asarray(y))

    # Save the model for later use
    model.save("eigenModel.xml")