Added the facerec_demo.py to show how to perform Face Recognition with the Python module.
This commit is contained in:
Philipp Wagner
parent
f29d73fe86
commit
1454f3d391
182
samples/python/facerec_demo.py
Normal file
182
samples/python/facerec_demo.py
Normal file
@ -0,0 +1,182 @@
|
||||
#!/usr/bin/env python
|
||||
# Software License Agreement (BSD License)
|
||||
#
|
||||
# Copyright (c) 2012, Philipp Wagner
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions
|
||||
# are met:
|
||||
#
|
||||
# * Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
# * Redistributions in binary form must reproduce the above
|
||||
# copyright notice, this list of conditions and the following
|
||||
# disclaimer in the documentation and/or other materials provided
|
||||
# with the distribution.
|
||||
# * Neither the name of the author nor the names of its
|
||||
# contributors may be used to endorse or promote products derived
|
||||
# from this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
# POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
import PIL.Image as Image
|
||||
|
||||
import numpy as np
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.cm as cm
|
||||
|
||||
import cv2
|
||||
|
||||
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):
|
||||
"""Reads the images in a given folder, resizes images on the fly if size is given.
|
||||
|
||||
Args:
|
||||
path: Path to a folder with subfolders representing the subjects (persons).
|
||||
sz: A tuple with the size Resizes
|
||||
|
||||
Returns:
|
||||
A list [X,y]
|
||||
|
||||
X: The images, which is a Python list of numpy arrays.
|
||||
y: The corresponding labels (the unique number of the subject, person) in a Python list.
|
||||
"""
|
||||
c = 0
|
||||
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, (errno, strerror):
|
||||
print "I/O error({0}): {1}".format(errno, strerror)
|
||||
except:
|
||||
print "Unexpected error:", sys.exc_info()[0]
|
||||
raise
|
||||
c = c+1
|
||||
return [X,y]
|
||||
|
||||
def create_font(fontname='Tahoma', fontsize=10):
|
||||
"""Creates a font for the subplot."""
|
||||
return { 'fontname': fontname, 'fontsize':fontsize }
|
||||
|
||||
def subplot(title, images, rows, cols, sptitle="subplot", sptitles=[], colormap=cm.gray, ticks_visible=True, filename=None):
|
||||
"""This will ease creating a subplot with matplotlib a lot for us."""
|
||||
fig = plt.figure()
|
||||
# main title
|
||||
fig.text(.5, .95, title, horizontalalignment='center')
|
||||
for i in xrange(len(images)):
|
||||
ax0 = fig.add_subplot(rows,cols,(i+1))
|
||||
plt.setp(ax0.get_xticklabels(), visible=False)
|
||||
plt.setp(ax0.get_yticklabels(), visible=False)
|
||||
if len(sptitles) == len(images):
|
||||
plt.title("%s #%s" % (sptitle, str(sptitles[i])), create_font('Tahoma',10))
|
||||
else:
|
||||
plt.title("%s #%d" % (sptitle, (i+1)), create_font('Tahoma',10))
|
||||
plt.imshow(np.asarray(images[i]), cmap=colormap)
|
||||
if filename is None:
|
||||
plt.show()
|
||||
else:
|
||||
fig.savefig(filename)
|
||||
|
||||
def imsave(image, title="", filename=None):
|
||||
"""Saves or shows (if no filename is given) an image."""
|
||||
fig = plt.figure()
|
||||
plt.imshow(np.asarray(image))
|
||||
plt.title(title, create_font('Tahoma',10))
|
||||
if filename is None:
|
||||
plt.show()
|
||||
else:
|
||||
fig.savefig(filename)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# You'll need at least a path to your image data, please see
|
||||
# the tutorial coming with this source code on how to prepare
|
||||
# your image data:
|
||||
if len(sys.argv) != 2:
|
||||
print "USAGE: facerec_demo.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])
|
||||
# Create the Eigenfaces model. We are going to use the default
|
||||
# parameters for this simple example, please read the documentation
|
||||
# for thresholding:
|
||||
model = cv2.createEigenFaceRecognizer()
|
||||
# Read
|
||||
# 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))
|
||||
# We now get a prediction from the model! In reality you
|
||||
# should always use unseen images for testing your model.
|
||||
# But so many people were confused, when I sliced an image
|
||||
# off in the C++ version, so I am just using an image we
|
||||
# have trained with.
|
||||
#
|
||||
# model.predict is going to return the predicted label and
|
||||
# the associated confidence:
|
||||
[p_label, p_confidence] = model.predict(np.asarray(X[0]))
|
||||
# Print it:
|
||||
print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence)
|
||||
# Cool! Finally we'll plot the Eigenfaces, because that's
|
||||
# what most people read in the papers are keen to see.
|
||||
#
|
||||
# Just like in C++ you have access to all model internal
|
||||
# data, because the cv::FaceRecognizer is a cv::Algorithm.
|
||||
#
|
||||
# You can see the available parameters with getParams():
|
||||
print model.getParams()
|
||||
# Now let's get some data:
|
||||
mean = model.getMat("mean")
|
||||
eigenvectors = model.getMat("eigenvectors")
|
||||
# We'll save the mean, by first normalizing it:
|
||||
mean_norm = normalize(mean, 0, 255)
|
||||
mean_resized = mean_norm.reshape(X[0].shape)
|
||||
imsave(mean_resized, "Mean Face", "mean.png")
|
||||
# Turn the first (at most) 16 eigenvectors into grayscale
|
||||
# images. You could also use cv::normalize here, but sticking
|
||||
# to NumPy is much easier for now.
|
||||
# Note: eigenvectors are stored by column:
|
||||
SubplotData = []
|
||||
for i in xrange(min(len(X), 16)):
|
||||
eigenvector_i = eigenvectors[:,i].reshape(X[0].shape)
|
||||
SubplotData.append(normalize(eigenvector_i, 0, 255))
|
||||
# Plot them and store the plot to "python_eigenfaces.png"
|
||||
subplot(title="Eigenfaces AT&T Facedatabase", images=SubplotData, rows=4, cols=4, sptitle="Eigenface", colormap=cm.jet, filename="eigenfaces.png")
|
||||
Loading…
Reference in New Issue
Block a user