layer = 'inception_4c/output'
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Computer dreams')
parser.add_argument('image', metavar='IMAGE',
help='the image to dream about')
parser.add_argument('--model', dest='model', default='bvlc_googlenet',
help='the model to use')
parser.add_argument('--layer', dest='layer', default=layer,
help='the layer to optimise')
parser.add_argument('--scale', dest='scale', type=float, default=0.05,
help='the scale coefficient')
parser.add_argument('--iterations', dest='iterations', type=int, default=100,
help='the number of iterations to run')
parser.add_argument('--output', dest='output', default='output',
help='the directory to output frames to')
args = parser.parse_args()
frame = np.float32(PIL.Image.open(args.image))
net = loadnet(args.model)
h, w = frame.shape[:2]
s = args.scale
for i in xrange(args.iterations):
frame = deepdream(net, frame, args.layer)
saveimage(frame, os.path.join(args.output, "frame-%04d" % i))
frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Computer dreams')
parser.add_argument('image', metavar='IMAGE',
help='the image to dream about')
parser.add_argument('--model', dest='model', default='bvlc_googlenet',
help='the model to use')
parser.add_argument('--output', dest='output', default='output',
help='the directory to output results to')
args = parser.parse_args()
image = np.float32(PIL.Image.open(args.image))
(name, ext) = os.path.splitext(os.path.basename(args.image))
model = loadmodel(args.model)
net = loadnet(args.model)
for layer in model.layer:
# Seem to be invalid somehow
if layer.type in ["Dropout", "ReLU"]:
continue
# Cause crashes
if layer.type in ["Softmax", "InnerProduct"]:
continue
if layer.type == "Pooling" and layer.pooling_param.pool == 1:
continue
frame = deepdream(net, image, layer.name)
saveimage(frame, os.path.join(args.output, name + '_' + '-'.join(layer.name.split('/'))))
default='bvlc_googlenet',
help='the model to use')
parser.add_argument('--output',
dest='output',
default='output',
help='the directory to output results to')
args = parser.parse_args()
image = np.float32(PIL.Image.open(args.image))
(name, ext) = os.path.splitext(os.path.basename(args.image))
model = loadmodel(args.model)
net = loadnet(args.model)
for layer in model.layer:
# Seem to be invalid somehow
if layer.type in ["Dropout", "ReLU"]:
continue
# Cause crashes
if layer.type in ["Softmax", "InnerProduct"]:
continue
if layer.type == "Pooling" and layer.pooling_param.pool == 1:
continue
frame = deepdream(net, image, layer.name)
saveimage(
frame,
os.path.join(args.output,
name + '_' + '-'.join(layer.name.split('/'))))
default=layer,
help='the layer to optimise')
parser.add_argument('--scale',
dest='scale',
type=float,
default=0.05,
help='the scale coefficient')
parser.add_argument('--iterations',
dest='iterations',
type=int,
default=100,
help='the number of iterations to run')
parser.add_argument('--output',
dest='output',
default='output',
help='the directory to output frames to')
args = parser.parse_args()
frame = np.float32(PIL.Image.open(args.image))
net = loadnet(args.model)
h, w = frame.shape[:2]
s = args.scale
for i in xrange(args.iterations):
frame = deepdream(net, frame, args.layer)
saveimage(frame, os.path.join(args.output, "frame-%04d" % i))
frame = nd.affine_transform(frame, [1 - s, 1 - s, 1],
[h * s / 2, w * s / 2, 0],
order=1)
