facerec_demo.py: Reworked demo to remove all matplotlib dependencies.
This commit is contained in:
Philipp Wagner
parent
508a029dae
commit
4a7e29b3f4
@ -34,15 +34,8 @@
|
||||
|
||||
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
|
||||
import numpy as np
|
||||
|
||||
def normalize(X, low, high, dtype=None):
|
||||
"""Normalizes a given array in X to a value between low and high."""
|
||||
@ -58,6 +51,7 @@ def normalize(X, low, high, dtype=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.
|
||||
|
||||
@ -78,11 +72,10 @@ def read_images(path, sz=None):
|
||||
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")
|
||||
im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
|
||||
# resize to given size (if given)
|
||||
if (sz is not None):
|
||||
im = im.resize(sz, Image.ANTIALIAS)
|
||||
im = cv2.resize(im, sz)
|
||||
X.append(np.asarray(im, dtype=np.uint8))
|
||||
y.append(c)
|
||||
except IOError, (errno, strerror):
|
||||
@ -92,49 +85,21 @@ def read_images(path, sz=None):
|
||||
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__":
|
||||
# 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>"
|
||||
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])
|
||||
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:
|
||||
@ -166,17 +131,26 @@ if __name__ == "__main__":
|
||||
# Now let's get some data:
|
||||
mean = model.getMat("mean")
|
||||
eigenvectors = model.getMat("eigenvectors")
|
||||
cv2.imwrite("test.png", X[0])
|
||||
# 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")
|
||||
if out_dir is None:
|
||||
cv2.imshow("mean", np.asarray(mean_resized, dtype=np.uint8))
|
||||
else:
|
||||
cv2.imwrite("%s/mean.png" % (out_dir), np.asarray(mean_resized, dtype=np.uint8))
|
||||
# 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")
|
||||
eigenvector_i_norm = normalize(eigenvector_i, 0, 255)
|
||||
# Show or save the images:
|
||||
if out_dir is None:
|
||||
cv2.imshow("%s/eigenvector_%d" % (out_dir,i), np.asarray(eigenvector_i_norm, dtype=np.uint8))
|
||||
else:
|
||||
cv2.imwrite("%s/eigenvector_%d.png" % (out_dir,i), np.asarray(eigenvector_i_norm, dtype=np.uint8))
|
||||
# Show the images:
|
||||
if out_dir is None:
|
||||
cv2.waitKey(0)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user