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# -*- coding: utf-8 -*-
# Copyright (c) XiMing Xing. All rights reserved.
# Author: XiMing Xing
# Description:
import random
import pathlib
import omegaconf
import pydiffvg
import numpy as np
import torch
from pytorch_svgrender.libs.modules.edge_map.DoG import XDoG
from pytorch_svgrender.diffvg_warp import DiffVGState
class Painter(DiffVGState):
def __init__(
self,
cfg: omegaconf.DictConfig,
diffvg_cfg: omegaconf.DictConfig,
num_strokes: int = 4,
num_segments: int = 4,
canvas_size: int = 224,
device: torch.device = None,
target_im: torch.Tensor = None,
attention_map: torch.Tensor = None,
mask: torch.Tensor = None,
):
super(Painter, self).__init__(device, print_timing=diffvg_cfg.print_timing,
canvas_width=canvas_size, canvas_height=canvas_size)
self.num_paths = num_strokes
self.num_segments = num_segments
self.width = cfg.width
self.max_width = cfg.max_width
self.optim_width = cfg.optim_width
self.control_points_per_seg = cfg.control_points_per_seg
self.optim_rgba = cfg.optim_rgba
self.optim_alpha = cfg.optim_opacity
self.num_stages = cfg.num_stages
self.softmax_temp = cfg.softmax_temp
self.shapes = []
self.shape_groups = []
self.num_control_points = 0
self.color_vars_threshold = cfg.color_vars_threshold
self.path_svg = cfg.path_svg
self.strokes_per_stage = self.num_paths
self.optimize_flag = []
# attention related for strokes initialisation
self.attention_init = cfg.attention_init
self.xdog_intersec = cfg.xdog_intersec
self.GT_input = target_im
self.mask = mask
self.attention_map = attention_map if self.attention_init else None
self.thresh = self.set_attention_threshold_map() if self.attention_init else None
self.strokes_counter = 0 # counts the number of calls to "get_path"
def init_image(self, stage=0):
if stage > 0:
# Noting: if multi stages training than add new strokes on existing ones
# don't optimize on previous strokes
self.optimize_flag = [False for i in range(len(self.shapes))]
for i in range(self.strokes_per_stage):
stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0])
path = self.get_path()
self.shapes.append(path)
path_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([len(self.shapes) - 1]),
fill_color=None,
stroke_color=stroke_color)
self.shape_groups.append(path_group)
self.optimize_flag.append(True)
else:
num_paths_exists = 0
if self.path_svg is not None and pathlib.Path(self.path_svg).exists():
print(f"-> init svg from `{self.path_svg}` ...")
self.canvas_width, self.canvas_height, self.shapes, self.shape_groups = self.load_svg(self.path_svg)
# if you want to add more strokes to existing ones and optimize on all of them
num_paths_exists = len(self.shapes)
for i in range(num_paths_exists, self.num_paths):
stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0])
path = self.get_path()
self.shapes.append(path)
path_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([len(self.shapes) - 1]),
fill_color=None,
stroke_color=stroke_color)
self.shape_groups.append(path_group)
self.optimize_flag = [True for i in range(len(self.shapes))]
img = self.render_warp()
img = img[:, :, 3:4] * img[:, :, :3] + \
torch.ones(img.shape[0], img.shape[1], 3, device=self.device) * (1 - img[:, :, 3:4])
img = img[:, :, :3]
img = img.unsqueeze(0) # convert img from HWC to NCHW
img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW
return img
def get_image(self):
img = self.render_warp()
opacity = img[:, :, 3:4]
img = opacity * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=self.device) * (1 - opacity)
img = img[:, :, :3]
img = img.unsqueeze(0) # convert img from HWC to NCHW
img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW
return img
def get_path(self):
self.num_control_points = torch.zeros(self.num_segments, dtype=torch.int32) + (self.control_points_per_seg - 2)
points = []
p0 = self.inds_normalised[self.strokes_counter] if self.attention_init else (random.random(), random.random())
points.append(p0)
for j in range(self.num_segments):
radius = 0.05
for k in range(self.control_points_per_seg - 1):
p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5))
points.append(p1)
p0 = p1
points = torch.tensor(points).to(self.device)
points[:, 0] *= self.canvas_width
points[:, 1] *= self.canvas_height
path = pydiffvg.Path(num_control_points=self.num_control_points,
points=points,
stroke_width=torch.tensor(self.width),
is_closed=False)
self.strokes_counter += 1
return path
def clip_curve_shape(self):
if self.optim_width:
for path in self.shapes:
path.stroke_width.data.clamp_(1.0, self.max_width)
if self.optim_rgba:
for group in self.shape_groups:
group.stroke_color.data.clamp_(0.0, 1.0)
else:
if self.optim_alpha:
for group in self.shape_groups:
# group.stroke_color.data: RGBA
group.stroke_color.data[:3].clamp_(0., 0.) # to force black stroke
group.stroke_color.data[-1].clamp_(0., 1.) # opacity
def path_pruning(self):
for group in self.shape_groups:
group.stroke_color.data[-1] = (group.stroke_color.data[-1] >= self.color_vars_threshold).float()
def set_points_parameters(self):
# stoke`s location optimization
self.point_vars = []
for i, path in enumerate(self.shapes):
if self.optimize_flag[i]:
path.points.requires_grad = True
self.point_vars.append(path.points)
def get_points_params(self):
return self.point_vars
def set_width_parameters(self):
# stroke`s width optimization
self.width_vars = []
for i, path in enumerate(self.shapes):
if self.optimize_flag[i]:
path.stroke_width.requires_grad = True
self.width_vars.append(path.stroke_width)
def get_width_parameters(self):
return self.width_vars
def set_color_parameters(self):
# for strokes color optimization (opacity)
self.color_vars = []
for i, group in enumerate(self.shape_groups):
if self.optimize_flag[i]:
group.stroke_color.requires_grad = True
self.color_vars.append(group.stroke_color)
def get_color_parameters(self):
return self.color_vars
def save_svg(self, output_dir, fname):
pydiffvg.save_svg(f'{output_dir}/{fname}.svg',
self.canvas_width,
self.canvas_height,
self.shapes,
self.shape_groups)
@staticmethod
def softmax(x, tau=0.2):
e_x = np.exp(x / tau)
return e_x / e_x.sum()
def set_inds_ldm(self):
attn_map = (self.attention_map - self.attention_map.min()) / \
(self.attention_map.max() - self.attention_map.min())
if self.xdog_intersec:
xdog = XDoG(k=10)
im_xdog = xdog(self.GT_input[0].permute(1, 2, 0).cpu().numpy())
print(f"use XDoG, shape: {im_xdog.shape}")
intersec_map = (1 - im_xdog) * attn_map
attn_map = intersec_map
attn_map_soft = np.copy(attn_map)
attn_map_soft[attn_map > 0] = self.softmax(attn_map[attn_map > 0], tau=self.softmax_temp)
# select points
k = self.num_stages * self.num_paths
self.inds = np.random.choice(range(attn_map.flatten().shape[0]),
size=k,
replace=False,
p=attn_map_soft.flatten())
self.inds = np.array(np.unravel_index(self.inds, attn_map.shape)).T
self.inds_normalised = np.zeros(self.inds.shape)
self.inds_normalised[:, 0] = self.inds[:, 1] / self.canvas_width
self.inds_normalised[:, 1] = self.inds[:, 0] / self.canvas_height
self.inds_normalised = self.inds_normalised.tolist()
return attn_map_soft
def set_attention_threshold_map(self):
return self.set_inds_ldm()
def get_attn(self):
return self.attention_map
def get_thresh(self):
return self.thresh
def get_inds(self):
return self.inds
def get_mask(self):
return self.mask
class SketchPainterOptimizer:
def __init__(
self,
renderer: Painter,
points_lr: float,
optim_alpha: bool,
optim_rgba: bool,
color_lr: float,
optim_width: bool,
width_lr: float
):
self.renderer = renderer
self.points_lr = points_lr
self.optim_color = optim_alpha or optim_rgba
self.color_lr = color_lr
self.optim_width = optim_width
self.width_lr = width_lr
self.points_optimizer, self.width_optimizer, self.color_optimizer = None, None, None
def init_optimizers(self):
self.renderer.set_points_parameters()
self.points_optimizer = torch.optim.Adam(self.renderer.get_points_params(), lr=self.points_lr)
if self.optim_color:
self.renderer.set_color_parameters()
self.color_optimizer = torch.optim.Adam(self.renderer.get_color_parameters(), lr=self.color_lr)
if self.optim_width:
self.renderer.set_width_parameters()
self.width_optimizer = torch.optim.Adam(self.renderer.get_width_parameters(), lr=self.width_lr)
def update_lr(self, step, decay_steps=(500, 750)):
if step % decay_steps[0] == 0 and step > 0:
for param_group in self.points_optimizer.param_groups:
param_group['lr'] = 0.4
if step % decay_steps[1] == 0 and step > 0:
for param_group in self.points_optimizer.param_groups:
param_group['lr'] = 0.1
def zero_grad_(self):
self.points_optimizer.zero_grad()
if self.optim_color:
self.color_optimizer.zero_grad()
if self.optim_width:
self.width_optimizer.zero_grad()
def step_(self):
self.points_optimizer.step()
if self.optim_color:
self.color_optimizer.step()
if self.optim_width:
self.width_optimizer.step()
def get_lr(self):
return self.points_optimizer.param_groups[0]['lr']
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