generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

removed contrib, legacy and softcsscade modules; removed latentsvm and datamatrix detector from objdetect. removed haartraining and sft apps.

Browse Source 
some of the stuff will be moved to opencv_contrib module.
in order to make this PR pass buildbot, please, comment off opencv_legacy, opencv_contrib and opencv_softcascade test runs.
This commit is contained in:
Vadim Pisarevsky
2014-06-24 19:18:51 +04:00
parent 6d4c4dcd94
commit 3858f2291d
398 changed files with 12 additions and 150885 deletions
Download Patch File Download Diff File Expand all files Collapse all files

166
samples/python2/facerec_demo.py
View File

@@ -1,166 +0,0 @@
#!/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)

72
samples/python2/video_dmtx.py
View File

@@ -1,72 +0,0 @@
#!/usr/bin/env python
'''
Data matrix detector sample.
Usage:
video_dmtx {<video device number>|<video file name>}
Generate a datamatrix from from http://datamatrix.kaywa.com/ and print it out.
NOTE: This only handles data matrices, generated for text strings of max 3 characters
Resize the screen to be large enough for your camera to see, and it should find an read it.
Keyboard shortcuts:
q or ESC - exit
space - save current image as datamatrix<frame_number>.jpg
'''
import cv2
import numpy as np
# built-in modules
import sys
def data_matrix_demo(cap):
window_name = "Data Matrix Detector"
frame_number = 0
need_to_save = False
while 1:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
codes, corners, dmtx = cv2.findDataMatrix(gray)
cv2.drawDataMatrixCodes(frame, codes, corners)
cv2.imshow(window_name, frame)
key = cv2.waitKey(30)
c = chr(key & 255)
if c in ['q', 'Q', chr(27)]:
break
if c == ' ':
need_to_save = True
if need_to_save and codes:
filename = ("datamatrix%03d.jpg" % frame_number)
cv2.imwrite(filename, frame)
print "Saved frame to " + filename
need_to_save = False
frame_number += 1
if __name__ == '__main__':
print __doc__
if len(sys.argv) == 1:
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(sys.argv[1])
if not cap.isOpened():
cap = cv2.VideoCapture(int(sys.argv[1]))
if not cap.isOpened():
print 'Cannot initialize video capture'
sys.exit(-1)
data_matrix_demo(cap)