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facerec_demo.py: Shortened code. Final version.

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This commit is contained in:
Philipp Wagner 2012-07-30 02:24:39 +02:00
parent 4a7e29b3f4
commit c0a4105467
1 changed files with 6 additions and 6 deletions
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12
samples/python2/facerec_demo.py
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@ -133,24 +133,24 @@ if __name__ == "__main__":
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_norm = normalize(mean, 0, 255, dtype=np.uint8)
mean_resized = mean_norm.reshape(X[0].shape)
if out_dir is None:
cv2.imshow("mean", np.asarray(mean_resized, dtype=np.uint8))
cv2.imshow("mean", mean_resized)
else:
cv2.imwrite("%s/mean.png" % (out_dir), np.asarray(mean_resized, dtype=np.uint8))
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)
eigenvector_i_norm = normalize(eigenvector_i, 0, 255, dtype=np.uint8)
# 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))
cv2.imshow("%s/eigenface_%d" % (out_dir,i), eigenvector_i_norm)
else:
cv2.imwrite("%s/eigenvector_%d.png" % (out_dir,i), np.asarray(eigenvector_i_norm, dtype=np.uint8))
cv2.imwrite("%s/eigenface_%d.png" % (out_dir,i), eigenvector_i_norm)
# Show the images:
if out_dir is None:
cv2.waitKey(0)