def test_0(self):
# test if the code runs without errors
images = [cv.LoadImage(x)
for x in lfwcrop_data.get_unique_lfw_training_images('data')[:100]]
vectors = range(3, 64)
feat = face_feature.Eigenfaces(images, vectors)
out = imfeat.compute(feat, images[0])
self.assertEqual(len(feat.mean), feat.vectors.shape[0])
self.assertEqual(1, len(out))
self.assertEqual(61, feat.vectors.shape[1])
self.assertEqual(61, len(out[0]))
def test_0(self):
# test if the code runs without errors
images = [
cv.LoadImage(x)
for x in lfwcrop_data.get_unique_lfw_training_images('data')[:100]
]
vectors = range(3, 64)
feat = face_feature.Eigenfaces(images, vectors)
out = imfeat.compute(feat, images[0])
self.assertEqual(len(feat.mean), feat.vectors.shape[0])
self.assertEqual(1, len(out))
self.assertEqual(61, feat.vectors.shape[1])
self.assertEqual(61, len(out[0]))
def test_identity(self):
# for now, test if the code runs without errors
images = [cv.LoadImage(x)
for x in random.sample(
lfwcrop_data.get_unique_lfw_training_images('data'), 100)]
test_image = 'data/exemplar1.jpg'
im1 = cv.LoadImage(test_image)
im2 = pil_to_cv(open(test_image))
feat = face_feature.Eigenfaces(images)
out1 = imfeat.compute(feat, resize(im1, cv.GetSize(images[0])))[0]
out2 = imfeat.compute(feat, resize(im2, cv.GetSize(images[0])))[0]
print('||feat(cv) - feat(pil)|| = %g' % (
np.linalg.norm(out1 - out2)/len(out1)))
print('||cv - pil|| = %g' % (np.linalg.norm(
cv_to_array(im1)-cv_to_array(im2))/len(cv_to_array(im1))))
np.testing.assert_almost_equal(out1, out2)
if not cv.GetSize(im) == size:
im_resized = cv.CreateImage(size, im.depth, im.nChannels)
cv.Resize(im, im_resized, cv.INTER_LINEAR)
im = im_resized
return im
def train(training_fns, pickle_fn, max_train_ims=3000, size=(64, 64)):
# load the unique training images, and learn PCA
print('Training Eigenfaces feature space (%i training images)...' % (
len(training_fns)))
if len(training_fns) <= max_train_ims:
train_ims = [resize_im(cv.LoadImage(fn, 1), size) for fn in training_fns]
else:
train_ims = [resize_im(cv.LoadImage(fn, 1), size)
for fn in random.sample(training_fns, max_train_ims)]
feat = face_feature.Eigenfaces(train_ims)
with open(pickle_fn, 'w') as fp:
cPickle.dump(feat, fp)
if __name__ == '__main__':
if(len(sys.argv) > 1 and sys.argv[1] == 'flickr'):
pickle_fn = 'data/eigenfaces_flickr.pkl'
training_fns = get_flickr_training_images()
else:
import lfwcrop_data
pickle_fn = 'data/eigenfaces_lfw_cropped.pkl'
training_fns = lfwcrop_data.get_unique_lfw_training_images('data')
train(training_fns, pickle_fn)
return im
def train(training_fns, pickle_fn, max_train_ims=3000, size=(64, 64)):
# load the unique training images, and learn PCA
print('Training Eigenfaces feature space (%i training images)...' %
(len(training_fns)))
if len(training_fns) <= max_train_ims:
train_ims = [
resize_im(cv.LoadImage(fn, 1), size) for fn in training_fns
]
else:
train_ims = [
resize_im(cv.LoadImage(fn, 1), size)
for fn in random.sample(training_fns, max_train_ims)
]
feat = face_feature.Eigenfaces(train_ims)
with open(pickle_fn, 'w') as fp:
cPickle.dump(feat, fp)
if __name__ == '__main__':
if (len(sys.argv) > 1 and sys.argv[1] == 'flickr'):
pickle_fn = 'data/eigenfaces_flickr.pkl'
training_fns = get_flickr_training_images()
else:
import lfwcrop_data
pickle_fn = 'data/eigenfaces_lfw_cropped.pkl'
training_fns = lfwcrop_data.get_unique_lfw_training_images('data')
train(training_fns, pickle_fn)
