import os, sys
import pydiffvg
import argparse
import torch
# import torch as th
import scipy.ndimage.filters as filters
# import numba
import numpy as np
from skimage import io
sys.path.append('./textureSyn')
from patchBasedTextureSynthesis import *
from make_gif import make_gif
import random
import ttools.modules
from svgpathtools import svg2paths2, Path, is_path_segment
"""
python texture_synthesis.py textureSyn/traced_1.png --svg-path textureSyn/traced_1.svg --case 1
"""
def texture_syn(img_path):
## get the width and height first
# input_img = io.imread(img_path) # returns an MxNx3 array
# output_size = [input_img.shape[1], input_img.shape[0]]
# output_path = "textureSyn/1/"
output_path = "results/texture_synthesis/%d"%(args.case)
patch_size = 40 # size of the patch (without the overlap)
overlap_size = 10 # the width of the overlap region
output_size = [300, 300]
pbts = patchBasedTextureSynthesis(img_path, output_path, output_size, patch_size, overlap_size, in_windowStep=5,
in_mirror_hor=True, in_mirror_vert=True, in_shapshots=False)
target_img = pbts.resolveAll()
return np.array(target_img)
def render(canvas_width, canvas_height, shapes, shape_groups, samples=2):
_render = pydiffvg.RenderFunction.apply
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = _render(canvas_width, # width
canvas_height, # height
samples, # num_samples_x
samples, # num_samples_y
0, # seed
None,
*scene_args)
return img
def big_bounding_box(paths_n_stuff):
"""Finds a BB containing a collection of paths, Bezier path segments, and
points (given as complex numbers)."""
bbs = []
for thing in paths_n_stuff:
if is_path_segment(thing) or isinstance(thing, Path):
bbs.append(thing.bbox())
elif isinstance(thing, complex):
bbs.append((thing.real, thing.real, thing.imag, thing.imag))
else:
try:
complexthing = complex(thing)
bbs.append((complexthing.real, complexthing.real,
complexthing.imag, complexthing.imag))
except ValueError:
raise TypeError(
"paths_n_stuff can only contains Path, CubicBezier, "
"QuadraticBezier, Line, and complex objects.")
xmins, xmaxs, ymins, ymaxs = list(zip(*bbs))
xmin = min(xmins)
xmax = max(xmaxs)
ymin = min(ymins)
ymax = max(ymaxs)
return xmin, xmax, ymin, ymax
def main(args):
## set device -> use cpu now since I haven't solved the nvcc issue
pydiffvg.set_use_gpu(False)
# pydiffvg.set_device(torch.device('cuda:1'))
## use L2 for now
# perception_loss = ttools.modules.LPIPS().to(pydiffvg.get_device())
## generate a texture synthesized
target_img = texture_syn(args.target)
tar_h, tar_w = target_img.shape[1], target_img.shape[0]
canvas_width, canvas_height, shapes, shape_groups = \
pydiffvg.svg_to_scene(args.svg_path)
## svgpathtools for checking the bounding box
# paths, _, _ = svg2paths2(args.svg_path)
# print(len(paths))
# xmin, xmax, ymin, ymax = big_bounding_box(paths)
# print(xmin, xmax, ymin, ymax)
# input("check")
print('tar h : %d tar w : %d'%(tar_h, tar_w))
print('canvas h : %d canvas w : %d' % (canvas_height, canvas_width))
scale_ratio = tar_h / canvas_height
print("scale ratio : ", scale_ratio)
# input("check")
for path in shapes:
path.points[..., 0] = path.points[..., 0] * scale_ratio
path.points[..., 1] = path.points[..., 1] * scale_ratio
init_img = render(tar_w, tar_h, shapes, shape_groups)
pydiffvg.imwrite(init_img.cpu(), 'results/texture_synthesis/%d/init.png'%(args.case), gamma=2.2)
# input("check")
random.seed(1234)
torch.manual_seed(1234)
points_vars = []
for path in shapes:
path.points.requires_grad = True
points_vars.append(path.points)
color_vars = []
for group in shape_groups:
group.fill_color.requires_grad = True
color_vars.append(group.fill_color)
# Optimize
points_optim = torch.optim.Adam(points_vars, lr=1.0)
color_optim = torch.optim.Adam(color_vars, lr=0.01)
target = torch.from_numpy(target_img).to(torch.float32) / 255.0
target = target.pow(2.2)
target = target.to(pydiffvg.get_device())
target = target.unsqueeze(0)
target = target.permute(0, 3, 1, 2) # NHWC -> NCHW
canvas_width, canvas_height = target.shape[3], target.shape[2]
# print('canvas h : %d canvas w : %d' % (canvas_height, canvas_width))
# input("check")
for t in range(args.max_iter):
print('iteration:', t)
points_optim.zero_grad()
color_optim.zero_grad()
cur_img = render(canvas_width, canvas_height, shapes, shape_groups)
pydiffvg.imwrite(cur_img.cpu(), 'results/texture_synthesis/%d/iter_%d.png'%(args.case, t), gamma=2.2)
cur_img = cur_img[:, :, :3]
cur_img = cur_img.unsqueeze(0)
cur_img = cur_img.permute(0, 3, 1, 2) # NHWC -> NCHW
## perceptual loss
# loss = perception_loss(cur_img, target)
## l2 loss
loss = (cur_img - target).pow(2).mean()
print('render loss:', loss.item())
loss.backward()
points_optim.step()
color_optim.step()
for group in shape_groups:
group.fill_color.data.clamp_(0.0, 1.0)
## write svg
if t % 10 == 0 or t == args.max_iter - 1:
pydiffvg.save_svg('results/texture_synthesis/%d/iter_%d.svg'%(args.case, t),
canvas_width, canvas_height, shapes, shape_groups)
## render final result
final_img = render(tar_h, tar_w, shapes, shape_groups)
pydiffvg.imwrite(final_img.cpu(), 'results/texture_synthesis/%d/final.png'%(args.case), gamma=2.2)
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/texture_synthesis/%d/iter_%d.png"%(args.case), "-vb", "20M",
"results/texture_synthesis/%d/out.mp4"%(args.case)])
## make gif
make_gif("results/texture_synthesis/%d"%(args.case), "results/texture_synthesis/%d/out.gif"%(args.case), frame_every_X_steps=1, repeat_ending=3, total_iter=args.max_iter)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
## target image path
parser.add_argument("target", help="target image path")
parser.add_argument("--svg-path", type=str, help="the corresponding svg file path")
parser.add_argument("--max-iter", type=int, default=500, help="the max optimization iterations")
parser.add_argument("--case", type=int, default=1, help="just the case id for a separate result folder")
args = parser.parse_args()
main(args)