157 lines
6.3 KiB
Python
157 lines
6.3 KiB
Python
#!/usr/bin/env python
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# Software License Agreement (BSD License)
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#
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# Copyright (c) 2012, Philipp Wagner <bytefish[at]gmx[dot]de>.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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# * Neither the name of the author nor the names of its
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# contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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import os
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import sys
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import cv2
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import numpy as np
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def normalize(X, low, high, dtype=None):
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"""Normalizes a given array in X to a value between low and high."""
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X = np.asarray(X)
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minX, maxX = np.min(X), np.max(X)
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# normalize to [0...1].
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X = X - float(minX)
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X = X / float((maxX - minX))
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# scale to [low...high].
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X = X * (high-low)
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X = X + low
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if dtype is None:
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return np.asarray(X)
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return np.asarray(X, dtype=dtype)
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def read_images(path, sz=None):
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"""Reads the images in a given folder, resizes images on the fly if size is given.
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Args:
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path: Path to a folder with subfolders representing the subjects (persons).
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sz: A tuple with the size Resizes
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Returns:
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A list [X,y]
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X: The images, which is a Python list of numpy arrays.
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y: The corresponding labels (the unique number of the subject, person) in a Python list.
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"""
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c = 0
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X,y = [], []
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for dirname, dirnames, filenames in os.walk(path):
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for subdirname in dirnames:
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subject_path = os.path.join(dirname, subdirname)
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for filename in os.listdir(subject_path):
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try:
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im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
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# resize to given size (if given)
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if (sz is not None):
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im = cv2.resize(im, sz)
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X.append(np.asarray(im, dtype=np.uint8))
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y.append(c)
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except IOError, (errno, strerror):
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print "I/O error({0}): {1}".format(errno, strerror)
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except:
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print "Unexpected error:", sys.exc_info()[0]
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raise
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c = c+1
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return [X,y]
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if __name__ == "__main__":
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# This is where we write the images, if an output_dir is given
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# in command line:
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out_dir = None
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# You'll need at least a path to your image data, please see
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# the tutorial coming with this source code on how to prepare
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# your image data:
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if len(sys.argv) < 2:
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print "USAGE: facerec_demo.py </path/to/images> [</path/to/store/images/at>]"
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sys.exit()
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# Now read in the image data. This must be a valid path!
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[X,y] = read_images(sys.argv[1])
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if len(sys.argv) == 3:
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out_dir = sys.argv[2]
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# Create the Eigenfaces model. We are going to use the default
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# parameters for this simple example, please read the documentation
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# for thresholding:
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model = cv2.createEigenFaceRecognizer()
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# Read
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# Learn the model. Remember our function returns Python lists,
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# so we use np.asarray to turn them into NumPy lists to make
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# the OpenCV wrapper happy:
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model.train(np.asarray(X), np.asarray(y))
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# We now get a prediction from the model! In reality you
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# should always use unseen images for testing your model.
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# But so many people were confused, when I sliced an image
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# off in the C++ version, so I am just using an image we
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# have trained with.
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#
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# model.predict is going to return the predicted label and
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# the associated confidence:
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[p_label, p_confidence] = model.predict(np.asarray(X[0]))
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# Print it:
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print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence)
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# Cool! Finally we'll plot the Eigenfaces, because that's
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# what most people read in the papers are keen to see.
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#
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# Just like in C++ you have access to all model internal
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# data, because the cv::FaceRecognizer is a cv::Algorithm.
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#
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# You can see the available parameters with getParams():
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print model.getParams()
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# Now let's get some data:
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mean = model.getMat("mean")
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eigenvectors = model.getMat("eigenvectors")
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cv2.imwrite("test.png", X[0])
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# We'll save the mean, by first normalizing it:
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mean_norm = normalize(mean, 0, 255, dtype=np.uint8)
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mean_resized = mean_norm.reshape(X[0].shape)
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if out_dir is None:
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cv2.imshow("mean", mean_resized)
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else:
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cv2.imwrite("%s/mean.png" % (out_dir), mean_resized)
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# Turn the first (at most) 16 eigenvectors into grayscale
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# images. You could also use cv::normalize here, but sticking
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# to NumPy is much easier for now.
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# Note: eigenvectors are stored by column:
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for i in xrange(min(len(X), 16)):
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eigenvector_i = eigenvectors[:,i].reshape(X[0].shape)
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eigenvector_i_norm = normalize(eigenvector_i, 0, 255, dtype=np.uint8)
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# Show or save the images:
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if out_dir is None:
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cv2.imshow("%s/eigenface_%d" % (out_dir,i), eigenvector_i_norm)
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else:
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cv2.imwrite("%s/eigenface_%d.png" % (out_dir,i), eigenvector_i_norm)
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# Show the images:
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if out_dir is None:
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cv2.waitKey(0)
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