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

Normalize line endings and whitespace

Browse Source
This commit is contained in:
OpenCV Buildbot
2012-10-17 11:12:04 +04:00
committed by Andrey Kamaev
parent 0442bca235
commit 81f826db2b
1511 changed files with 258678 additions and 258624 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
samples/python2/facerec_demo.py Normal file → Executable file
View File

@@ -41,7 +41,7 @@ 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].
# normalize to [0...1].
X = X - float(minX)
X = X / float((maxX - minX))
# scale to [low...high].
@@ -54,14 +54,14 @@ def normalize(X, low, high, dtype=None):
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
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.
"""
@@ -85,7 +85,7 @@ def read_images(path, sz=None):
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:
@@ -99,7 +99,7 @@ if __name__ == "__main__":
# 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
# 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)
@@ -115,10 +115,10 @@ if __name__ == "__main__":
# 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
# 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
@@ -126,7 +126,7 @@ if __name__ == "__main__":
[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
# 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
@@ -144,9 +144,9 @@ if __name__ == "__main__":
cv2.imshow("mean", mean_resized)
else:
cv2.imwrite("%s/mean.png" % (out_dir), mean_resized)
# Turn the first (at most) 16 eigenvectors into grayscale
# 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.
# 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)