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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) | |
| 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'] | |