mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 01:39:13 +08:00
167 lines
7.0 KiB
Python
Executable File
167 lines
7.0 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# Software License Agreement (BSD License)
|
|
#
|
|
# Copyright (c) 2012, Philipp Wagner <bytefish[at]gmx[dot]de>.
|
|
# 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.
|
|
|
|
# ------------------------------------------------------------------------------------------------
|
|
# 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
|
|
|
|
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 = 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(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]
|
|
|
|
if __name__ == "__main__":
|
|
# This is where we write the images, if an output_dir is given
|
|
# in command line:
|
|
out_dir = None
|
|
# 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> [</path/to/store/images/at>]"
|
|
sys.exit()
|
|
# Now read in the image data. This must be a valid path!
|
|
[X,y] = read_images(sys.argv[1])
|
|
# Convert labels to 32bit integers. This is a workaround for 64bit machines,
|
|
# because the labels will truncated else. This will be fixed in code as
|
|
# soon as possible, so Python users don't need to know about this.
|
|
# Thanks to Leo Dirac for reporting:
|
|
y = np.asarray(y, dtype=np.int32)
|
|
# If a out_dir is given, set it:
|
|
if len(sys.argv) == 3:
|
|
out_dir = sys.argv[2]
|
|
# 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, dtype=np.uint8)
|
|
mean_resized = mean_norm.reshape(X[0].shape)
|
|
if out_dir is None:
|
|
cv2.imshow("mean", mean_resized)
|
|
else:
|
|
cv2.imwrite("%s/mean.png" % (out_dir), mean_resized)
|
|
# 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:
|
|
for i in xrange(min(len(X), 16)):
|
|
eigenvector_i = eigenvectors[:,i].reshape(X[0].shape)
|
|
eigenvector_i_norm = normalize(eigenvector_i, 0, 255, dtype=np.uint8)
|
|
# Show or save the images:
|
|
if out_dir is None:
|
|
cv2.imshow("%s/eigenface_%d" % (out_dir,i), eigenvector_i_norm)
|
|
else:
|
|
cv2.imwrite("%s/eigenface_%d.png" % (out_dir,i), eigenvector_i_norm)
|
|
# Show the images:
|
|
if out_dir is None:
|
|
cv2.waitKey(0)
|