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"""
Scream: python painterly_rendering.py imgs/scream.jpg --num_paths 2048 --max_width 4.0
Fallingwater: python painterly_rendering.py imgs/fallingwater.jpg --num_paths 2048 --max_width 4.0
Fallingwater: python painterly_rendering.py imgs/fallingwater.jpg --num_paths 2048 --max_width 4.0 --use_lpips_loss
Baboon: python painterly_rendering.py imgs/baboon.png --num_paths 1024 --max_width 4.0 --num_iter 250
Baboon Lpips: python painterly_rendering.py imgs/baboon.png --num_paths 1024 --max_width 4.0 --num_iter 500 --use_lpips_loss
smile: python painterly_rendering.py ../LIVE/figures/smile.png --num_paths 5 --use_blob --num_iter 500
"""
import pydiffvg
import torch
import skimage
import skimage.io
import random
import ttools.modules
import argparse
import math
pydiffvg.set_print_timing(True)
gamma = 1.0
def main(args):
# Use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())
perception_loss = ttools.modules.LPIPS().to(pydiffvg.get_device())
#target = torch.from_numpy(skimage.io.imread('imgs/lena.png')).to(torch.float32) / 255.0
target = torch.from_numpy(skimage.io.imread(args.target)).to(torch.float32) / 255.0
target = target.pow(gamma)
target = target.to(pydiffvg.get_device())
target = target.unsqueeze(0)
target = target.permute(0, 3, 1, 2) # NHWC -> NCHW
#target = torch.nn.functional.interpolate(target, size = [256, 256], mode = 'area')
canvas_width, canvas_height = target.shape[3], target.shape[2]
num_paths = args.num_paths
max_width = args.max_width
random.seed(1234)
torch.manual_seed(1234)
shapes = []
shape_groups = []
if args.use_blob:
for i in range(num_paths):
num_segments = random.randint(3, 5)
num_control_points = torch.zeros(num_segments, dtype = torch.int32) + 2
points = []
p0 = (random.random(), random.random())
points.append(p0)
for j in range(num_segments):
radius = 0.05
p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5))
p2 = (p1[0] + radius * (random.random() - 0.5), p1[1] + radius * (random.random() - 0.5))
p3 = (p2[0] + radius * (random.random() - 0.5), p2[1] + radius * (random.random() - 0.5))
points.append(p1)
points.append(p2)
if j < num_segments - 1:
points.append(p3)
p0 = p3
points = torch.tensor(points)
points[:, 0] *= canvas_width
points[:, 1] *= canvas_height
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
stroke_width = torch.tensor(1.0),
is_closed = True)
shapes.append(path)
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(shapes) - 1]),
fill_color = torch.tensor([random.random(),
random.random(),
random.random(),
random.random()]))
shape_groups.append(path_group)
else:
for i in range(num_paths):
num_segments = random.randint(1, 3)
num_control_points = torch.zeros(num_segments, dtype = torch.int32) + 2
points = []
p0 = (random.random(), random.random())
points.append(p0)
for j in range(num_segments):
radius = 0.05
p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5))
p2 = (p1[0] + radius * (random.random() - 0.5), p1[1] + radius * (random.random() - 0.5))
p3 = (p2[0] + radius * (random.random() - 0.5), p2[1] + radius * (random.random() - 0.5))
points.append(p1)
points.append(p2)
points.append(p3)
p0 = p3
points = torch.tensor(points)
points[:, 0] *= canvas_width
points[:, 1] *= canvas_height
#points = torch.rand(3 * num_segments + 1, 2) * min(canvas_width, canvas_height)
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
stroke_width = torch.tensor(1.0),
is_closed = False)
shapes.append(path)
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(shapes) - 1]),
fill_color = None,
stroke_color = torch.tensor([random.random(),
random.random(),
random.random(),
random.random()]))
shape_groups.append(path_group)
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
render = pydiffvg.RenderFunction.apply
img = render(canvas_width, # width
canvas_height, # height
2, # num_samples_x
2, # num_samples_y
0, # seed
None,
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/painterly_rendering/init.png', gamma=gamma)
points_vars = []
stroke_width_vars = []
color_vars = []
for path in shapes:
path.points.requires_grad = True
points_vars.append(path.points)
if not args.use_blob:
for path in shapes:
path.stroke_width.requires_grad = True
stroke_width_vars.append(path.stroke_width)
if args.use_blob:
for group in shape_groups:
group.fill_color.requires_grad = True
color_vars.append(group.fill_color)
else:
for group in shape_groups:
group.stroke_color.requires_grad = True
color_vars.append(group.stroke_color)
# Optimize
points_optim = torch.optim.Adam(points_vars, lr=1.0)
if len(stroke_width_vars) > 0:
width_optim = torch.optim.Adam(stroke_width_vars, lr=0.1)
color_optim = torch.optim.Adam(color_vars, lr=0.01)
# Adam iterations.
for t in range(args.num_iter):
print('iteration:', t)
points_optim.zero_grad()
if len(stroke_width_vars) > 0:
width_optim.zero_grad()
color_optim.zero_grad()
# Forward pass: render the image.
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(canvas_width, # width
canvas_height, # height
2, # num_samples_x
2, # num_samples_y
t, # seed
None,
*scene_args)
# Compose img with white background
img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device = pydiffvg.get_device()) * (1 - img[:, :, 3:4])
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/painterly_rendering/iter_{}.png'.format(t), gamma=gamma)
img = img[:, :, :3]
# Convert img from HWC to NCHW
img = img.unsqueeze(0)
img = img.permute(0, 3, 1, 2) # NHWC -> NCHW
if args.use_lpips_loss:
loss = perception_loss(img, target) + (img.mean() - target.mean()).pow(2)
else:
loss = (img - target).pow(2).mean()
print('render loss:', loss.item())
# Backpropagate the gradients.
loss.backward()
# Take a gradient descent step.
points_optim.step()
if len(stroke_width_vars) > 0:
width_optim.step()
color_optim.step()
if len(stroke_width_vars) > 0:
for path in shapes:
path.stroke_width.data.clamp_(1.0, max_width)
if args.use_blob:
for group in shape_groups:
group.fill_color.data.clamp_(0.0, 1.0)
else:
for group in shape_groups:
group.stroke_color.data.clamp_(0.0, 1.0)
if t % 10 == 0 or t == args.num_iter - 1:
pydiffvg.save_svg('results/painterly_rendering/iter_{}.svg'.format(t),
canvas_width, canvas_height, shapes, shape_groups)
# Render the final result.
img = render(target.shape[1], # width
target.shape[0], # height
2, # num_samples_x
2, # num_samples_y
0, # seed
None,
*scene_args)
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/painterly_rendering/final.png'.format(t), gamma=gamma)
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/painterly_rendering/iter_%d.png", "-vb", "20M",
"results/painterly_rendering/out.mp4"])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("target", help="target image path")
parser.add_argument("--num_paths", type=int, default=512)
parser.add_argument("--max_width", type=float, default=2.0)
parser.add_argument("--use_lpips_loss", dest='use_lpips_loss', action='store_true')
parser.add_argument("--num_iter", type=int, default=500)
parser.add_argument("--use_blob", dest='use_blob', action='store_true')
args = parser.parse_args()
main(args)
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