From c0a41054674ea92834d7a8b98e1cf4b360aacd6d Mon Sep 17 00:00:00 2001 From: Philipp Wagner Date: Mon, 30 Jul 2012 02:24:39 +0200 Subject: [PATCH] facerec_demo.py: Shortened code. Final version. --- samples/python2/facerec_demo.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/samples/python2/facerec_demo.py b/samples/python2/facerec_demo.py index 9fc2258..74f2681 100644 --- a/samples/python2/facerec_demo.py +++ b/samples/python2/facerec_demo.py @@ -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) -- 2.7.4