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Support for only specifying output file, then automatically using the…
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… '_sem.png' file accordingly. Tweaks to formatting, printing, saving...
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@alexjc
alexjc committed Mar 7, 2016
1 parent cfb610c commit 2022942
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Showing 2 changed files with 39 additions and 21 deletions.
8 changes: 4 additions & 4 deletions README.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,11 @@ Image Analogy

.. code:: bash
# Synthesize a coastline in the style of Monet. Uses "*_sem.png" files for both images.
python3 doodle.py --style samples/Monet.jpg --output samples/Coastline.jpg
# Generate a scene around a lake in the style of Renoir.
python3.4 doodle.py --output samples/Landscape.jpg --content-weight=0.0 --style samples/Renoir.jpg
# Synthesize a coastline in the style of Monet.
python3.4 doodle.py --output samples/Coastline.jpg --content-weight=0.0 --style samples/Monet.jpg
python3 doodle.py --style samples/Renoir.jpg --output samples/Landscape.jpg
Installation & Setup
--------------------
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52 changes: 35 additions & 17 deletions doodle.py
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Expand Up @@ -16,21 +16,21 @@
parser = argparse.ArgumentParser(description='Generate a new image by applying style onto a content image.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)

parser.add_argument('--content', required=True, type=str, help='Content image path as optimization target.')
parser.add_argument('--content', default=None, type=str, help='Content image path as optimization target.')
parser.add_argument('--content-weight', default=10.0, type=float, help='Weight of content relative to style.')
parser.add_argument('--content-layers', default='4_2', type=str, help='The layer with which to match content.')
parser.add_argument('--style', required=True, type=str, help='Style image path to extract patches.')
parser.add_argument('--style-weight', default=50.0, type=float, help='Weight of style relative to content.')
parser.add_argument('--style-layers', default='3_1,4_1', type=str, help='The layers to match style patches.')
parser.add_argument('--semantic-ext', default='_sem.png', type=str, help='File extension for the semantic maps.')
parser.add_argument('--semantic-weight', default=50.0, type=float, help='Global weight of semantics vs. features.')
parser.add_argument('--semantic-weight', default=1.0, type=float, help='Global weight of semantics vs. features.')
parser.add_argument('--output', default='output.png', type=str, help='Output image path to save once done.')
parser.add_argument('--resolutions', default=3, type=int, help='Number of image scales to process.')
parser.add_argument('--smoothness', default=1E+0, type=float, help='Weight of image smoothing scheme.')
parser.add_argument('--seed', default='noise', type=str, help='Seed image path, "noise" or "content".')
parser.add_argument('--iterations', default=100, type=int, help='Number of iterations to run each resolution.')
parser.add_argument('--device', default='gpu0', type=str, help='Index of the GPU number to use, for theano.')
parser.add_argument('--print-every', default=1, type=int, help='How often to log statistics to stdout.')
parser.add_argument('--iterations', default=50, type=int, help='Number of iterations to run each resolution.')
parser.add_argument('--device', default='cpu', type=str, help='Index of the GPU number to use, for theano.')
parser.add_argument('--print-every', default=10, type=int, help='How often to log statistics to stdout.')
parser.add_argument('--save-every', default=0, type=int, help='How frequently to save PNG into `frames`.')
args = parser.parse_args()

Expand Down Expand Up @@ -123,9 +123,24 @@ class NeuralGenerator(object):

def __init__(self):
self.model = Model()

self.content_image_original = scipy.ndimage.imread(args.content, mode='RGB')
self.content_map_original = scipy.ndimage.imread(os.path.splitext(args.content)[0]+args.semantic_ext, mode='RGB')

filename_image = args.content or args.output
filename_map = os.path.splitext(filename_image)[0]+args.semantic_ext

if os.path.exists(filename_image):
self.content_image_original = scipy.ndimage.imread(filename_image, mode='RGB')
else:
self.content_image_original = None
args.content_weight = 0.0

if os.path.exists(filename_map):
self.content_map_original = scipy.ndimage.imread(filename_map, mode='RGB')
if self.content_image_original is None:
self.content_image_original = np.zeros(self.content_map_original.shape[:2]+(3,))
else:
self.content_map_original = np.zeros(self.content_image_original.shape[:2]+(1,))
args.semantic_weight = 0.0

self.style_image_original = scipy.ndimage.imread(args.style, mode='RGB')
self.style_map_original = scipy.ndimage.imread(os.path.splitext(args.style)[0]+args.semantic_ext, mode='RGB')

Expand Down Expand Up @@ -169,6 +184,7 @@ def prepare_optimization(self):
content_features = self.model.tensor_outputs['conv'+layer].eval({self.model.tensor_img: self.content_image})
content_loss = T.mean((self.model.tensor_outputs['conv'+layer] - content_features) ** 2.0)
self.content_loss.append(('content', layer, args.content_weight * content_loss))
print(' - Content layer conv{}: {} features in {:,}kb.'.format(layer, content_features.shape[0], content_features.size//1000))

def style_loss(l):
layer = self.model.network['nn'+l]
Expand All @@ -195,10 +211,14 @@ def evaluate(self, Xn):
grads, *losses = self.compute_grad_and_losses(current_img, self.content_map)
loss = sum(losses)

scipy.misc.toimage(self.finalize_image(Xn), cmin=0, cmax=255).save('frames/%04d.png'%self.frame)
if args.save_every and self.frame % args.save_every == 0:
scipy.misc.toimage(self.finalize_image(Xn), cmin=0, cmax=255).save('frames/%04d.png'%self.frame)

# Use gradients as an estimate for overall quality.
self.error = self.error * 0.9 + 0.1 * np.abs(grads).max()

if self.frame % args.print_every == 0:
print('{:< 3} {}error{} {:8.2e} '.format(self.frame, ansi.BOLD, ansi.ENDC, loss), end='')
if args.print_every and self.frame % args.print_every == 0:
print('{:>3} {}error{} {:8.2e} '.format(self.frame, ansi.BOLD, ansi.ENDC, loss / 1000.0), end='')
category = ''
for v, l in zip(losses, self.losses):
if l[0] == 'smooth':
Expand All @@ -208,9 +228,7 @@ def evaluate(self, Xn):
print(' {}{}{}'.format(ansi.BOLD, l[0], ansi.ENDC), end='')
category = l[0]
print(' {}{}{} {:8.2e} '.format(ansi.BOLD, l[1], ansi.ENDC, v / 1000.0), end='')

adjust = np.abs(grads).max()
self.error = self.error * 0.9 + 0.1 * adjust

quality = 100.0 - 100.0 * np.sqrt(self.error / 255.0)
print(' {}quality{} {:3.1f}% '.format(ansi.BOLD, ansi.ENDC, quality, flush=True))

Expand All @@ -219,9 +237,9 @@ def evaluate(self, Xn):

def run(self):
self.frame = 0
self.error = 255.0

for i in range(args.resolutions):
self.error = 255.0
scale = 1.0 / 2.0 ** (args.resolutions - 1 - i)

shape = self.content_image_original.shape
Expand All @@ -240,7 +258,7 @@ def run(self):
if args.seed == 'content':
Xn = self.content_image[0] + self.model.pixel_mean
if args.seed == 'noise':
Xn = np.random.uniform(64, 192, shape + (3,)).astype(np.float32)
Xn = np.random.uniform(32, 224, shape + (3,)).astype(np.float32)
if args.seed == 'previous':
Xn = scipy.misc.imresize(Xn[0], shape)
Xn = Xn.transpose((2, 0, 1))[np.newaxis]
Expand All @@ -253,7 +271,7 @@ def run(self):
Xn.astype(np.float64).flatten(),
bounds=data_bounds,
factr=0.0, pgtol=0.0, # Disable automatic termination by setting low threshold.
m=16, # Maximum correlations kept in memory by algorithm.
m=4, # Maximum correlations kept in memory by algorithm.
maxfun=args.iterations-1, # Limit number of calls to evaluate().
iprint=-1) # Handle our own logging of information.

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