def main():
if args.style_option == 0:
best_image_bgr = stylize(args, False)
result = Image.fromarray(np.uint8(np.clip(best_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(args.output_image)
elif args.style_option == 1:
best_image_bgr = stylize(args, True)
if not args.apply_smooth:
result = Image.fromarray(np.uint8(np.clip(best_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(args.output_image)
else:
# Pycuda runtime incompatible with Tensorflow
from smooth_local_affine import smooth_local_affine
content_input = np.array(Image.open(args.content_image_path).convert("RGB"), dtype=np.float32)
# RGB to BGR
content_input = content_input[:, :, ::-1]
# H * W * C to C * H * W
content_input = content_input.transpose((2, 0, 1))
input_ = np.ascontiguousarray(content_input, dtype=np.float32) / 255.
_, H, W = np.shape(input_)
output_ = np.ascontiguousarray(best_image_bgr.transpose((2, 0, 1)), dtype=np.float32) / 255.
best_ = smooth_local_affine(output_, input_, 1e-7, 3, H, W, args.f_radius, args.f_edge).transpose(1, 2, 0)
result = Image.fromarray(np.uint8(np.clip(best_ * 255., 0, 255.)))
result.save(args.output_image)
elif args.style_option == 2:
args.max_iter = 2 * args.max_iter
tmp_image_bgr = stylize(args, False)
result = Image.fromarray(np.uint8(np.clip(tmp_image_bgr[:, :, ::-1], 0, 255.0)))
args.init_image_path = os.path.join(args.serial, "tmp_result.png")
result.save(args.init_image_path)
best_image_bgr = stylize(args, True)
if not args.apply_smooth:
result = Image.fromarray(np.uint8(np.clip(best_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(args.output_image)
else:
from smooth_local_affine import smooth_local_affine
content_input = np.array(Image.open(args.content_image_path).convert("RGB"), dtype=np.float32)
# RGB to BGR
content_input = content_input[:, :, ::-1]
# H * W * C to C * H * W
content_input = content_input.transpose((2, 0, 1))
input_ = np.ascontiguousarray(content_input, dtype=np.float32) / 255.
_, H, W = np.shape(input_)
output_ = np.ascontiguousarray(best_image_bgr.transpose((2, 0, 1)), dtype=np.float32) / 255.
best_ = smooth_local_affine(output_, input_, 1e-7, 3, H, W, args.f_radius, args.f_edge).transpose(1, 2, 0)
result = Image.fromarray(np.uint8(np.clip(best_ * 255., 0, 255.)))
result.save(args.output_image)
def main():
if args.style_option == 0:
best_image_bgr = stylize(args, False)
result = Image.fromarray(np.uint8(np.clip(best_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(args.output_image)
elif args.style_option == 1:
best_image_bgr = stylize(args, True)
if not args.apply_smooth:
result = Image.fromarray(np.uint8(np.clip(best_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(args.output_image)
else:
# Pycuda runtime incompatible with Tensorflow
from smooth_local_affine import smooth_local_affine
content_input = np.array(Image.open(args.content_image_path).convert("RGB"), dtype=np.float32)
# RGB to BGR
content_input = content_input[:, :, ::-1]
# H * W * C to C * H * W
content_input = content_input.transpose((2, 0, 1))
input_ = np.ascontiguousarray(content_input, dtype=np.float32) / 255.
_, H, W = np.shape(input_)
output_ = np.ascontiguousarray(best_image_bgr.transpose((2, 0, 1)), dtype=np.float32) / 255.
best_ = smooth_local_affine(output_, input_, 1e-7, 3, H, W, args.f_radius, args.f_edge).transpose(1, 2, 0)
result = Image.fromarray(np.uint8(np.clip(best_ * 255., 0, 255.)))
result.save(args.output_image)
elif args.style_option == 2:
args.max_iter = 2 * args.max_iter
tmp_image_bgr = stylize(args, False)
result = Image.fromarray(np.uint8(np.clip(tmp_image_bgr[:, :, ::-1], 0, 255.0)))
args.init_image_path = os.path.join(args.serial, "tmp_result.png")
result.save(args.init_image_path)
best_image_bgr = stylize(args, True)
if not args.apply_smooth:
result = Image.fromarray(np.uint8(np.clip(best_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(args.output_image)
else:
from smooth_local_affine import smooth_local_affine
content_input = np.array(Image.open(args.content_image_path).convert("RGB"), dtype=np.float32)
# RGB to BGR
content_input = content_input[:, :, ::-1]
# H * W * C to C * H * W
content_input = content_input.transpose((2, 0, 1))
input_ = np.ascontiguousarray(content_input, dtype=np.float32) / 255.
_, H, W = np.shape(input_)
output_ = np.ascontiguousarray(best_image_bgr.transpose((2, 0, 1)), dtype=np.float32) / 255.
best_ = smooth_local_affine(output_, input_, 1e-7, 3, H, W, args.f_radius, args.f_edge).transpose(1, 2, 0)
result = Image.fromarray(np.uint8(np.clip(best_ * 255., 0, 255.)))
result.save(args.output_image)
def run_deep_photostyle(self):
"""
画風変換を実施する
:return:
"""
style_const = StyleConst()
# Neural Art WebAPIに関する情報も渡す
style_const.material_detail = self.material_detail
# Input Options
# 素材フォルダはあらかじめ決めている
style_const.content_image_path = "tmp/content_image.jpg"
style_const.style_image_path = "tmp/style_image.jpg"
style_const.content_seg_path = "tmp/content_seg.jpg"
style_const.style_seg_path = "tmp/style_seg.jpg"
# output Options
style_const.init_image_path = ""
style_const.output_image = "outputs/best_stylized/best_stylized.png"
style_const.serial = "outputs"
# Training Optimizer Options
#style_const.max_iter = 1000
style_const.max_iter = int(self.parameters["max_iter"])
style_const.learning_rate = 1.0
style_const.print_iter = 1
style_const.save_iter = 100
style_const.lbfgs = True
# Weight Options
#style_const.content_weight = 5e0
style_const.content_weight = float(self.parameters["content_weight"])
#style_const.style_weight = 1e2
style_const.style_weight = float(self.parameters["style_weight"])
style_const.tv_weight = 1e-3
style_const.affine_weight = 1e4
# style Options
style_const.style_option = 0
style_const.apply_smooth = True
# Smoothing Argment
style_const.f_radius = 15
style_const.f_edge = 1e-1
beset_image_bgr = stylize(style_const, False)
result = Image.fromarray(
np.uint8(np.clip(beset_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(style_const.output_image)
def main():
style_const = StyleConst()
# Input Options
#style_const.content_image_path = "inputs/content_image/neuralart_orange_logo.jpg"
#style_const.style_image_path = "inputs/style_image/hokusai.jpg"
#style_const.content_seg_path = "inputs/content_seg/neuralart_orange_logo.jpg"
#style_const.style_seg_path = "inputs/style_seg/hokusai_seg.jpg"
style_const.content_image_path = "tmp/content_image.jpg"
style_const.style_image_path = "tmp/style_image.jpg"
style_const.content_seg_path = "tmp/content_seg.jpg"
style_const.style_seg_path = "tmp/style_seg.jpg"
# output Options
style_const.init_image_path = ""
style_const.output_image = "outputs/best_stylized/best_stylized.png"
style_const.serial = "outputs"
# Training Optimizer Options
style_const.max_iter = 1000
style_const.learning_rate = 1.0
style_const.print_iter = 1
style_const.save_iter = 100
style_const.lbfgs = True
# Weight Options
style_const.content_weight = 5e0
style_const.style_weight = 1e2
style_const.tv_weight = 1e-3
style_const.affine_weight = 1e4
# style Options
style_const.style_option = 0
style_const.apply_smooth = True
# Smoothing Argment
style_const.f_radius = 15
style_const.f_edge = 1e-1
beset_image_bgr = stylize(style_const, False)
result = Image.fromarray(
np.uint8(np.clip(beset_image_bgr[:, :, ::-1], 0, 255.0)))
result.save(style_const.output_image)
