feat_chans = 512
feat_style_shape = (feat_chans, 12, 18)
feat_style = np.random.uniform(0.0, 1.0, feat_style_shape)
feat_in_shape = (feat_chans, 17, 10)
feat_in = np.random.uniform(0.0, 1.0, feat_in_shape)
matcher = PatchMatcher(feat_in_shape[::-1],
feat_style,
patch_size=patch_size)
feat_in_normed = matcher.normalize_patches(
matcher.get_patches_for(feat_in))
for i in range(num_steps):
matcher.update_with_patches(feat_in_normed)
r = matcher.get_reconstruction()
content_img_img = load_image(content_image_path)
content_n_channels, content_n_rows, content_n_cols = content_img_img.shape[::
-1]
content_img = preprocess_image(content_img_img, content_n_cols,
content_n_rows)[0] #.transpose((2,1,0))
style_img = load_image(style_image_path)
style_n_channels, style_n_rows, style_n_cols = content_img_img.shape[::-1]
style_img = preprocess_image(load_image(style_image_path), style_n_cols,
style_n_rows)[0] #.transpose((2,1,0))
pg = make_patch_grid(content_img, patch_size)
result = combine_patches_grid(pg, content_img.shape[::-1])
outimg = deprocess_image(result, contrast_percent=0)
imsave(output_prefix + '_bestre.png', outimg)
# # #
matcher = PatchMatcher(
num_steps = 7
patch_size = 1
patch_stride = 1
feat_chans = 512
feat_style_shape = (feat_chans, 12, 18)
feat_style = np.random.uniform(0.0, 1.0, feat_style_shape)
feat_in_shape = (feat_chans, 17, 10)
feat_in = np.random.uniform(0.0, 1.0, feat_in_shape)
matcher = PatchMatcher(feat_in_shape[::-1], feat_style, patch_size=patch_size)
feat_in_normed = matcher.normalize_patches(matcher.get_patches_for(feat_in))
for i in range(num_steps):
matcher.update_with_patches(feat_in_normed)
r = matcher.get_reconstruction()
content_img_img = load_image(content_image_path)
content_n_channels, content_n_rows, content_n_cols = content_img_img.shape[::-1]
content_img = preprocess_image(content_img_img, content_n_cols, content_n_rows)[0]#.transpose((2,1,0))
style_img = load_image(style_image_path)
style_n_channels, style_n_rows, style_n_cols = content_img_img.shape[::-1]
style_img = preprocess_image(
load_image(style_image_path), style_n_cols, style_n_rows)[0]#.transpose((2,1,0))
pg = make_patch_grid(content_img, patch_size)
result = combine_patches_grid(pg, content_img.shape[::-1])
outimg = deprocess_image(result, contrast_percent=0)
imsave(output_prefix + '_bestre.png', outimg)
# # #
matcher = PatchMatcher((content_n_cols, content_n_rows, content_n_channels), style_img, patch_size=patch_size)
for i in range(num_steps):
start = time.time()
num_steps = 7
patch_size = 1
patch_stride = 1
feat_chans = 512
feat_style_shape = (feat_chans, 12, 18)
feat_style = np.random.uniform(0.0, 1.0, feat_style_shape)
feat_in_shape = (feat_chans, 17, 10)
feat_in = np.random.uniform(0.0, 1.0, feat_in_shape)
matcher = PatchMatcher(feat_in_shape[::-1], feat_style, patch_size=patch_size)
feat_in_normed = matcher.normalize_patches(matcher.get_patches_for(feat_in))
for i in range(num_steps):
matcher.update_with_patches(feat_in_normed)
r = matcher.get_reconstruction()
content_img_img = load_image(content_image_path)
content_n_channels, content_n_rows, content_n_cols = content_img_img.shape[::-1]
content_img = preprocess_image(content_img_img, content_n_cols, content_n_rows)[0]#.transpose((2,1,0))
style_img = load_image(style_image_path)
style_n_channels, style_n_rows, style_n_cols = content_img_img.shape[::-1]
style_img = preprocess_image(
load_image(style_image_path), style_n_cols, style_n_rows)[0]#.transpose((2,1,0))
pg = make_patch_grid(content_img, patch_size)
result = combine_patches_grid(pg, content_img.shape[::-1])
outimg = deprocess_image(result, contrast_percent=0)
imsave(output_prefix + '_bestre.png', outimg)
# # #
matcher = PatchMatcher((content_n_cols, content_n_rows, content_n_channels), style_img, patch_size=patch_size)
for i in range(num_steps):
start = time.time()
def main(args, model_class):
'''The main loop which does the things.'''
K.set_image_dim_ordering('th')
# calculate scales
if args.num_scales > 1:
step_scale_factor = (1 - args.min_scale) / (args.num_scales - 1)
else:
step_scale_factor = 0.0
args.min_scale = 1.0
# prepare the input images
full_asem_image = img_utils.load_image(
args.asem_image_path) #style_semlight image
full_a_image = img_utils.load_image(args.a_image_path) #style image
full_b_image = img_utils.load_image(args.b_image_path) #content image
full_bsem_image = img_utils.load_image(
args.bsem_image_path) #content_semlight image
# calculate the output size
full_img_width, full_img_height = calculate_image_dims(
args, full_bsem_image)
img_num_channels = 3 # TODO: allow alpha
b_scale_ratio_width = float(full_bsem_image.shape[1]) / full_img_width
b_scale_ratio_height = float(full_bsem_image.shape[0]) / full_img_height
# ensure the output dir exists
output_dir = os.path.dirname(args.result_prefix)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# multi-scale loop
x = None # this is going to hold our output image
optimizer = Optimizer()
for scale_i in range(args.num_scales):
scale_factor = (scale_i * step_scale_factor) + args.min_scale
# scale our inputs
img_width = int(round(full_img_width * scale_factor))
img_height = int(round(full_img_height * scale_factor))
# prepare the current optimizer state
if x is None: # we need to create an initial state
x = np.random.uniform(0, 255, (img_height, img_width, 3)).astype(
np.float32)
x = vgg16.img_to_vgg(x)
else: # resize the last state
zoom_ratio = img_width / float(x.shape[-1])
x = scipy.ndimage.zoom(x, (1, zoom_ratio, zoom_ratio), order=1)
img_height, img_width = x.shape[-2:]
# determine scaling of "A" images
if args.a_scale_mode == 'match':
a_img_width = img_width
a_img_height = img_height
elif args.a_scale_mode == 'none':
a_img_width = full_asem_image.shape[1] * scale_factor
a_img_height = full_asem_image.shape[0] * scale_factor
else: # should just be 'ratio'
a_img_width = full_asem_image.shape[
1] * scale_factor * b_scale_ratio_width
a_img_height = full_asem_image.shape[
0] * scale_factor * b_scale_ratio_height
a_img_width = int(round(args.a_scale * a_img_width))
a_img_height = int(round(args.a_scale * a_img_height))
# prepare images for use
a_sem_image = img_utils.preprocess_image(full_asem_image, a_img_width,
a_img_height)
a_image = img_utils.preprocess_image(full_a_image, a_img_width,
a_img_height)
b_sem_image = img_utils.preprocess_image(full_bsem_image, img_width,
img_height)
b_image = img_utils.preprocess_image(full_b_image, img_width,
img_height)
print('Scale factor {} "A" shape {} "B" shape {}'.format(
scale_factor, a_image.shape, b_image.shape))
# load up the net and create the model
net = vgg16.get_model(img_width,
img_height,
weights_path=args.vgg_weights,
pool_mode=args.pool_mode)
model = model_class(net, args)
model.build(a_sem_image, a_image, b_sem_image, b_image,
(1, img_num_channels, img_height, img_width))
for i in range(args.num_iterations_per_scale):
print('Start of iteration {} x {}'.format(scale_i, i))
start_time = time.time()
if args.color_jitter:
color_jitter = (args.color_jitter * 2) * (np.random.random(
(3, img_height, img_width)) - 0.5)
x += color_jitter
if args.jitter:
jitter = args.jitter * scale_factor
ox, oy = np.random.randint(-jitter, jitter + 1, 2)
x = np.roll(np.roll(x, ox, -1), oy, -2) # apply jitter shift
x, min_val, info = optimizer.optimize(
x, model) # update (go to evaluator, loss, grads calculation)
print('Current loss value: {}'.format(min_val))
x = x.reshape((3, img_height, img_width))
if args.jitter:
x = np.roll(np.roll(x, -ox, -1), -oy, -2) # unshift image
if args.color_jitter:
x -= color_jitter
# save the image
if args.output_full_size:
out_resize_shape = (full_img_height, full_img_width)
else:
out_resize_shape = None
img = img_utils.deprocess_image(
np.copy(x),
contrast_percent=args.contrast_percent,
resize=out_resize_shape)
fname = args.result_prefix + '_at_iteration_{}_{}.png'.format(
scale_i, i)
imsave(fname, img)
end_time = time.time()
print('Image saved as {}'.format(fname))
print('Iteration completed in {:.2f} seconds'.format(
end_time - start_time, ))
def main(args, model_class):
'''The main loop which does the things.'''
K.set_image_dim_ordering('th')
# calculate scales
if args.num_scales > 1:
step_scale_factor = (1 - args.min_scale) / (args.num_scales - 1)
else:
step_scale_factor = 0.0
args.min_scale = 1.0
# prepare the input images
full_ap_image = img_utils.load_image(args.ap_image_path)
full_a_image = img_utils.load_image(args.a_image_path)
full_b_image = img_utils.load_image(args.b_image_path)
# calculate the output size
full_img_width, full_img_height = calculate_image_dims(args, full_b_image)
img_num_channels = 3 # TODO: allow alpha
b_scale_ratio_width = float(full_b_image.shape[1]) / full_img_width
b_scale_ratio_height = float(full_b_image.shape[0]) / full_img_height
# ensure the output dir exists
output_dir = os.path.dirname(args.result_prefix)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# multi-scale loop
x = None # this is going to hold our output image
optimizer = Optimizer()
for scale_i in range(args.num_scales):
scale_factor = (scale_i * step_scale_factor) + args.min_scale
# scale our inputs
img_width = int(round(full_img_width * scale_factor))
img_height = int(round(full_img_height * scale_factor))
# prepare the current optimizer state
if x is None: # we need to create an initial state
x = np.random.uniform(0, 255, (img_height, img_width, 3)).astype(np.float32)
x = vgg16.img_to_vgg(x)
else: # resize the last state
zoom_ratio = img_width / float(x.shape[-1])
x = scipy.ndimage.zoom(x, (1, zoom_ratio, zoom_ratio), order=1)
img_height, img_width = x.shape[-2:]
# determine scaling of "A" images
if args.a_scale_mode == 'match':
a_img_width = img_width
a_img_height = img_height
elif args.a_scale_mode == 'none':
a_img_width = full_a_image.shape[1] * scale_factor
a_img_height = full_a_image.shape[0] * scale_factor
else: # should just be 'ratio'
a_img_width = full_a_image.shape[1] * scale_factor * b_scale_ratio_width
a_img_height = full_a_image.shape[0] * scale_factor * b_scale_ratio_height
a_img_width = int(round(args.a_scale * a_img_width))
a_img_height = int(round(args.a_scale * a_img_height))
# prepare images for use
a_image = img_utils.preprocess_image(full_a_image, a_img_width, a_img_height)
ap_image = img_utils.preprocess_image(full_ap_image, a_img_width, a_img_height)
b_image = img_utils.preprocess_image(full_b_image, img_width, img_height)
print('Scale factor {} "A" shape {} "B" shape {}'.format(scale_factor, a_image.shape, b_image.shape))
# load up the net and create the model
net = vgg16.get_model(img_width, img_height, weights_path=args.vgg_weights, pool_mode=args.pool_mode)
model = model_class(net, args)
model.build(a_image, ap_image, b_image, (1, img_num_channels, img_height, img_width))
for i in range(args.num_iterations_per_scale):
print('Start of iteration {} x {}'.format(scale_i, i))
start_time = time.time()
if args.color_jitter:
color_jitter = (args.color_jitter * 2) * (np.random.random((3, img_height, img_width)) - 0.5)
x += color_jitter
if args.jitter:
jitter = args.jitter * scale_factor
ox, oy = np.random.randint(-jitter, jitter+1, 2)
x = np.roll(np.roll(x, ox, -1), oy, -2) # apply jitter shift
# actually run the optimizer
x, min_val, info = optimizer.optimize(x, model)
print('Current loss value: {}'.format(min_val))
# unjitter the image
x = x.reshape((3, img_height, img_width))
if args.jitter:
x = np.roll(np.roll(x, -ox, -1), -oy, -2) # unshift image
if args.color_jitter:
x -= color_jitter
# save the image
if args.output_full_size:
out_resize_shape = (full_img_height, full_img_width)
else:
out_resize_shape = None
img = img_utils.deprocess_image(np.copy(x), contrast_percent=args.contrast_percent,resize=out_resize_shape)
fname = args.result_prefix + '_at_iteration_{}_{}.png'.format(scale_i, i)
imsave(fname, img)
end_time = time.time()
print('Image saved as {}'.format(fname))
print('Iteration completed in {:.2f} seconds'.format(end_time - start_time,))