def preprocess_image(x, img_width, img_height):
img = imresize(x, (img_height, img_width),
interp='bicubic').astype('float64')
img = vgg16.img_to_vgg(img)
img = np.expand_dims(img, axis=0)
return img
full_img_width = int(round(args.out_height / float(full_img_height) * full_img_width))
full_img_height = args.out_height
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
x = None
for scale_i in range(num_scales):
scale_factor = (scale_i * step_scale_factor) + min_scale_factor
# scale our inputs
img_width = int(round(full_img_width * scale_factor))
img_height = int(round(full_img_height * scale_factor))
img_width, img_height = img_width, img_height
if x is None:
x = np.random.uniform(0, 255, (img_height, img_width, 3))
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:]
if a_scale_mode == 'match':
a_img_width = img_width
a_img_height = img_height
elif 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))
full_img_width))
full_img_height = args.out_height
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
x = None
for scale_i in range(num_scales):
scale_factor = (scale_i * step_scale_factor) + min_scale_factor
# scale our inputs
img_width = int(round(full_img_width * scale_factor))
img_height = int(round(full_img_height * scale_factor))
img_width, img_height = img_width, img_height
if x is None:
x = np.random.uniform(0, 255, (img_height, img_width, 3))
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:]
if a_scale_mode == 'match':
a_img_width = img_width
a_img_height = img_height
elif 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
def preprocess_image(x, img_width, img_height):
img = imresize(x, (img_height, img_width), interp='bicubic').astype('float64')
img = vgg16.img_to_vgg(img)
img = np.expand_dims(img, axis=0)
return img