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import os
import torch as th
import torch.multiprocessing as mp
import threading as mt
import numpy as np
import random
import ttools
import pydiffvg
import time
def render(canvas_width, canvas_height, shapes, shape_groups, samples=2,
seed=None):
if seed is None:
seed = random.randint(0, 1000000)
_render = pydiffvg.RenderFunction.apply
scene_args = pydiffvg.RenderFunction.serialize_scene(
canvas_width, canvas_height, shapes, shape_groups)
img = _render(canvas_width, canvas_height, samples, samples,
seed, # seed
None, # background image
*scene_args)
return img
def opacityStroke2diffvg(strokes, canvas_size=128, debug=False, relative=True,
force_cpu=True):
dev = strokes.device
if force_cpu:
strokes = strokes.to("cpu")
# pydiffvg.set_use_gpu(False)
# if strokes.is_cuda:
# pydiffvg.set_use_gpu(True)
"""Rasterize strokes given in (dx, dy, opacity) sequence format."""
bs, nsegs, dims = strokes.shape
out = []
start = time.time()
for batch_idx, stroke in enumerate(strokes):
if relative: # Absolute coordinates
all_points = stroke[..., :2].cumsum(0)
else:
all_points = stroke[..., :2]
all_opacities = stroke[..., 2]
# Transform from [-1, 1] to canvas coordinates
# Make sure points are in canvas
all_points = 0.5*(all_points + 1.0) * canvas_size
# all_points = th.clamp(0.5*(all_points + 1.0), 0, 1) * canvas_size
# Avoid overlapping points
eps = 1e-4
all_points = all_points + eps*th.randn_like(all_points)
shapes = []
shape_groups = []
for start_idx in range(0, nsegs-1):
points = all_points[start_idx:start_idx+2].contiguous().float()
opacity = all_opacities[start_idx]
num_ctrl_pts = th.zeros(points.shape[0] - 1, dtype=th.int32)
width = th.ones(1)
path = pydiffvg.Path(
num_control_points=num_ctrl_pts, points=points,
stroke_width=width, is_closed=False)
shapes.append(path)
color = th.cat([th.ones(3, device=opacity.device),
opacity.unsqueeze(0)], 0)
path_group = pydiffvg.ShapeGroup(
shape_ids=th.tensor([len(shapes) - 1]),
fill_color=None,
stroke_color=color)
shape_groups.append(path_group)
# Rasterize only if there are shapes
if shapes:
inner_start = time.time()
out.append(render(canvas_size, canvas_size, shapes, shape_groups,
samples=4))
if debug:
inner_elapsed = time.time() - inner_start
print("diffvg call took %.2fms" % inner_elapsed)
else:
out.append(th.zeros(canvas_size, canvas_size, 4,
device=strokes.device))
if debug:
elapsed = (time.time() - start)*1000
print("rendering took %.2fms" % elapsed)
images = th.stack(out, 0).permute(0, 3, 1, 2).contiguous()
# Return data on the same device as input
return images.to(dev)
def stroke2diffvg(strokes, canvas_size=128):
"""Rasterize strokes given some sequential data."""
bs, nsegs, dims = strokes.shape
out = []
for stroke_idx, stroke in enumerate(strokes):
end_of_stroke = stroke[:, 4] == 1
last = end_of_stroke.cpu().numpy().argmax()
stroke = stroke[:last+1, :]
# stroke = stroke[~end_of_stroke]
# TODO: stop at the first end of stroke
# import ipdb; ipdb.set_trace()
split_idx = stroke[:, 3].nonzero().squeeze(1)
# Absolute coordinates
all_points = stroke[..., :2].cumsum(0)
# Transform to canvas coordinates
all_points[..., 0] += 0.5
all_points[..., 0] *= canvas_size
all_points[..., 1] += 0.5
all_points[..., 1] *= canvas_size
# Make sure points are in canvas
all_points[..., :2] = th.clamp(all_points[..., :2], 0, canvas_size)
shape_groups = []
shapes = []
start_idx = 0
for count, end_idx in enumerate(split_idx):
points = all_points[start_idx:end_idx+1].contiguous().float()
if points.shape[0] <= 2: # we need at least 2 points for a line
continue
num_ctrl_pts = th.zeros(points.shape[0] - 1, dtype=th.int32)
width = th.ones(1)
path = pydiffvg.Path(
num_control_points=num_ctrl_pts, points=points,
stroke_width=width, is_closed=False)
start_idx = end_idx+1
shapes.append(path)
color = th.ones(4, 1)
path_group = pydiffvg.ShapeGroup(
shape_ids=th.tensor([len(shapes) - 1]),
fill_color=None,
stroke_color=color)
shape_groups.append(path_group)
# Rasterize
if shapes:
# draw only if there are shapes
out.append(render(canvas_size, canvas_size, shapes, shape_groups, samples=2))
else:
out.append(th.zeros(canvas_size, canvas_size, 4,
device=strokes.device))
return th.stack(out, 0).permute(0, 3, 1, 2)[:, :3].contiguous()
def line_render(all_points, all_widths, all_alphas, force_cpu=True,
canvas_size=32, colors=None):
dev = all_points.device
if force_cpu:
all_points = all_points.to("cpu")
all_widths = all_widths.to("cpu")
all_alphas = all_alphas.to("cpu")
if colors is not None:
colors = colors.to("cpu")
all_points = 0.5*(all_points + 1.0) * canvas_size
eps = 1e-4
all_points = all_points + eps*th.randn_like(all_points)
bs, num_segments, _, _ = all_points.shape
n_out = 3 if colors is not None else 1
output = th.zeros(bs, n_out, canvas_size, canvas_size,
device=all_points.device)
scenes = []
for k in range(bs):
shapes = []
shape_groups = []
for p in range(num_segments):
points = all_points[k, p].contiguous().cpu()
num_ctrl_pts = th.zeros(1, dtype=th.int32)
width = all_widths[k, p].cpu()
alpha = all_alphas[k, p].cpu()
if colors is not None:
color = colors[k, p]
else:
color = th.ones(3, device=alpha.device)
color = th.cat([color, alpha.view(1,)])
path = pydiffvg.Path(
num_control_points=num_ctrl_pts, points=points,
stroke_width=width, is_closed=False)
shapes.append(path)
path_group = pydiffvg.ShapeGroup(
shape_ids=th.tensor([len(shapes) - 1]),
fill_color=None,
stroke_color=color)
shape_groups.append(path_group)
# Rasterize
scenes.append((canvas_size, canvas_size, shapes, shape_groups))
raster = render(canvas_size, canvas_size, shapes, shape_groups,
samples=2)
raster = raster.permute(2, 0, 1).view(4, canvas_size, canvas_size)
alpha = raster[3:4]
if colors is not None: # color output
image = raster[:3]
alpha = alpha.repeat(3, 1, 1)
else:
image = raster[:1]
# alpha compositing
image = image*alpha
output[k] = image
output = output.to(dev)
return output, scenes
def bezier_render(all_points, all_widths, all_alphas, force_cpu=True,
canvas_size=32, colors=None):
dev = all_points.device
if force_cpu:
all_points = all_points.to("cpu")
all_widths = all_widths.to("cpu")
all_alphas = all_alphas.to("cpu")
if colors is not None:
colors = colors.to("cpu")
all_points = 0.5*(all_points + 1.0) * canvas_size
eps = 1e-4
all_points = all_points + eps*th.randn_like(all_points)
bs, num_strokes, num_pts, _ = all_points.shape
num_segments = (num_pts - 1) // 3
n_out = 3 if colors is not None else 1
output = th.zeros(bs, n_out, canvas_size, canvas_size,
device=all_points.device)
scenes = []
for k in range(bs):
shapes = []
shape_groups = []
for p in range(num_strokes):
points = all_points[k, p].contiguous().cpu()
# bezier
num_ctrl_pts = th.zeros(num_segments, dtype=th.int32) + 2
width = all_widths[k, p].cpu()
alpha = all_alphas[k, p].cpu()
if colors is not None:
color = colors[k, p]
else:
color = th.ones(3, device=alpha.device)
color = th.cat([color, alpha.view(1,)])
path = pydiffvg.Path(
num_control_points=num_ctrl_pts, points=points,
stroke_width=width, is_closed=False)
shapes.append(path)
path_group = pydiffvg.ShapeGroup(
shape_ids=th.tensor([len(shapes) - 1]),
fill_color=None,
stroke_color=color)
shape_groups.append(path_group)
# Rasterize
scenes.append((canvas_size, canvas_size, shapes, shape_groups))
raster = render(canvas_size, canvas_size, shapes, shape_groups,
samples=2)
raster = raster.permute(2, 0, 1).view(4, canvas_size, canvas_size)
alpha = raster[3:4]
if colors is not None: # color output
image = raster[:3]
alpha = alpha.repeat(3, 1, 1)
else:
image = raster[:1]
# alpha compositing
image = image*alpha
output[k] = image
output = output.to(dev)
return output, scenes
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