class TestGetData(TestCase):
layer = 'conv3_1'
net = ml.NetModels.setup_vgg('../CommonCaffe/TrainedModels/') # Slow
style_data = gd.get_layers_data(net, 'test/diScaled.jpg', layer)
subject_data = gd.get_layers_data(net, 'test/us.jpg', layer)
def test_get_subject_data(self):
assert self.subject_data.shape == (256, 55, 72)
def test_get_style_data(self):
assert self.style_data.shape == (256, 55, 72)
def test_get_different_data(self):
# They should be different, if you don't use .copy they end up equal
self.assertFalse(np.allclose(self.subject_data, self.style_data), .001)
def test_functional_again(self):
layer = 'inception_3b/output'
su.SetupCaffe.gpu_on()
net = ml.NetModels.setup_googlenet_model('../CommonCaffe/TrainedModels/')
print 'loaded net'
style_data = gd.get_layers_data(net, 'test/elephants2.jpg', layer)
subject_data = gd.get_layers_data(net, 'test/soft-grey.jpg', layer)
print 'loaded data'
stl = styles.Styles()
vis = stl.content_plus_style(net, iterator, style_data, subject_data, layer)
# np.save('test/test2_result', vis)
# They should be the same iff the two input images are the same size
self.assertFalse(style_data.shape == subject_data.shape)
test_vis = np.load('test/test2_result.npy')
assert np.allclose(vis, test_vis, 0.02)
def start_dreaming(self, frame):
layers = ['inception_3b/5x5_reduce', 'inception_4a/5x5_reduce', 'inception_4c/3x3_reduce',
'inception_5b/3x3_reduce']
self.layer = layers[self.__i_layer]
self.__i_layer += 1
if self.__i_layer >= len(layers):
self.__i_layer = 0
self.style_data = gd.get_layers_data(self.net, 'ImagesIn/elephants2.jpg', self.layer)
self.subject_data = gd.get_layers_data_image(self.net, self.input_filter(frame), self.layer)
self.stl.setup_style_iterator(self.iterator[0])
vis = self.stl.next_frame(self.net, self.style_data, self.subject_data, self.layer)
return vis
def start_dreaming(self, frame):
layers = [
'inception_3b/5x5_reduce', 'inception_4a/5x5_reduce',
'inception_4c/3x3_reduce', 'inception_5b/3x3_reduce'
]
self.layer = layers[self.__i_layer]
self.__i_layer += 1
if self.__i_layer >= len(layers):
self.__i_layer = 0
self.style_data = gd.get_layers_data(self.net,
'ImagesIn/elephants2.jpg',
self.layer)
self.subject_data = gd.get_layers_data_image(self.net,
self.input_filter(frame),
self.layer)
self.stl.setup_style_iterator(self.iterator[0])
vis = self.stl.next_frame(self.net, self.style_data, self.subject_data,
self.layer)
return vis
{
'iter_n': 40,
'start_sigma': 1.5,
'end_sigma': 0.0,
'start_step_size': 6.0,
'end_step_size': 1.0
},
]
layer = 'inception_4b/output'
net = ml.NetModels.setup_googlenet_model('../CommonCaffe/TrainedModels/')
style_data = gd.get_layers_data(net, 'ImagesIn/elephants2.jpg', layer)
img = cv2.imread('Paintings/wondering.jpg')
img = images.Images.resize_image(480, 640, img)
dreamer.Dreamer.input_filter(img)
subject_data = gd.get_layers_data_image(net, img, layer)
stl = dream_styles.Styles()
stl.setup_style_iterator(iterator[0])
for i in range(0, iterator[0]['iter_n']):
vis = stl.next_frame(net, style_data, subject_data, layer)
cv2.imshow('Video', vis)
cv2.waitKey(1)
vis = cv2.cvtColor(vis, cv2.COLOR_RGB2BGR)
iterator = [
{
'iter_n': 40,
'start_sigma': 1.5,
'end_sigma': 0.0,
'start_step_size': 6.0,
'end_step_size': 1.0
},
]
layer = 'inception_4b/output'
net = ml.NetModels.setup_googlenet_model('../CommonCaffe/TrainedModels/')
style_data = gd.get_layers_data(net, 'ImagesIn/elephants2.jpg', layer)
img = cv2.imread('Paintings/wondering.jpg')
img = images.Images.resize_image(480, 640, img)
dreamer.Dreamer.input_filter(img)
subject_data = gd.get_layers_data_image(net, img, layer)
stl = dream_styles.Styles()
stl.setup_style_iterator(iterator[0])
for i in range(0, iterator[0]['iter_n']):
vis = stl.next_frame(net, style_data, subject_data, layer)
cv2.imshow('Video', vis)
cv2.waitKey(1)
vis = cv2.cvtColor(vis, cv2.COLOR_RGB2BGR)
import styles
import setup_caffe_network as su
import models as ml
import get_layer_data as gd
iterator = [
{
'iter_n': 150,
'start_sigma': 3.0,
'end_sigma': 0.0,
'start_step_size': 6.0,
'end_step_size': 1.0
},
]
layer = 'conv5_4'
su.SetupCaffe.gpu_on()
net = ml.NetModels.setup_vgg('../CommonCaffe/TrainedModels/')
style_data = gd.get_layers_data(net, 'ImagesIn/Class 67.jpg', layer)
subject_data = gd.get_layers_data(net, 'ImagesIn/smallwonder.jpg', layer)
stl = styles.Styles()
stl.content_plus_style(net, iterator, style_data, subject_data, layer)
import styles
import setup_caffe_network as su
import models as ml
import get_layer_data as gd
iterator = [
{
'iter_n': 100,
'start_sigma': 1.0,
'end_sigma': 0.0,
'start_step_size': 6.0,
'end_step_size': 1.0
},
]
layer = 'inception_4c/3x3_reduce'
su.SetupCaffe.gpu_on()
net = ml.NetModels.setup_places_model('../CommonCaffe/TrainedModels/')
print 'loaded net'
style_data = gd.get_layers_data(net, 'ImagesIn/Class 67.jpg', layer)
subject_data = gd.get_layers_data(net, 'ImagesIn/vvg.jpg', layer)
print 'loaded data'
stl = styles.Styles()
stl.content_plus_style(net, iterator, style_data, subject_data, layer)
import styles
import setup_caffe_network as su
import models as ml
import get_layer_data as gd
iterator = [
{
'iter_n': 100,
'start_sigma': 6.0,
'end_sigma': 0.0,
'start_step_size': 6.0,
'end_step_size': 1.0
},
]
layer = 'inception_4c/3x3_reduce'
su.SetupCaffe.gpu_on()
net = ml.NetModels.setup_googlenet_model('../CommonCaffe/TrainedModels/')
style_data = gd.get_layers_data(net, 'ImagesIn/elephants2.jpg', layer)
subject_data = gd.get_layers_data(net, 'ImagesIn/smallwonder.jpg', layer)
stl = styles.Styles()
vis = stl.content_plus_style(net, iterator, style_data, subject_data, layer)
import styles
import setup_caffe_network as su
import models as ml
import get_layer_data as gd
iterator = [
{
'iter_n': 150,
'start_sigma': 3.0,
'end_sigma': 0.0,
'start_step_size': 6.0,
'end_step_size': 1.0
},
]
layer = 'conv5_4'
su.SetupCaffe.gpu_on()
net = ml.NetModels.setup_vgg('../CommonCaffe/TrainedModels/')
style_data = gd.get_layers_data(net, 'ImagesIn/Class 67.jpg', layer)
subject_data = gd.get_layers_data(net, 'ImagesIn/smallwonder.jpg', layer)
stl = styles.Styles()
stl.content_plus_style(net, iterator, style_data, subject_data, layer)
