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import pydiffvg
import torch
import torchvision
from PIL import Image
import numpy as np
# Use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())
def inv_exp(a,x,xpow=1):
return pow(a,pow(1.-x,xpow))
import math
import numbers
import torch
from torch import nn
from torch.nn import functional as F
import visdom
class GaussianSmoothing(nn.Module):
"""
Apply gaussian smoothing on a
1d, 2d or 3d tensor. Filtering is performed seperately for each channel
in the input using a depthwise convolution.
Arguments:
channels (int, sequence): Number of channels of the input tensors. Output will
have this number of channels as well.
kernel_size (int, sequence): Size of the gaussian kernel.
sigma (float, sequence): Standard deviation of the gaussian kernel.
dim (int, optional): The number of dimensions of the data.
Default value is 2 (spatial).
"""
def __init__(self, channels, kernel_size, sigma, dim=2):
super(GaussianSmoothing, self).__init__()
if isinstance(kernel_size, numbers.Number):
kernel_size = [kernel_size] * dim
if isinstance(sigma, numbers.Number):
sigma = [sigma] * dim
# The gaussian kernel is the product of the
# gaussian function of each dimension.
kernel = 1
meshgrids = torch.meshgrid(
[
torch.arange(size, dtype=torch.float32)
for size in kernel_size
]
)
for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
mean = (size - 1) / 2
kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \
torch.exp(-((mgrid - mean) / std) ** 2 / 2)
# Make sure sum of values in gaussian kernel equals 1.
kernel = kernel / torch.sum(kernel)
# Reshape to depthwise convolutional weight
kernel = kernel.view(1, 1, *kernel.size())
kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
self.register_buffer('weight', kernel)
self.groups = channels
if dim == 1:
self.conv = F.conv1d
elif dim == 2:
self.conv = F.conv2d
elif dim == 3:
self.conv = F.conv3d
else:
raise RuntimeError(
'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(dim)
)
def forward(self, input):
"""
Apply gaussian filter to input.
Arguments:
input (torch.Tensor): Input to apply gaussian filter on.
Returns:
filtered (torch.Tensor): Filtered output.
"""
return self.conv(input, weight=self.weight, groups=self.groups)
vis=visdom.Visdom(port=8080)
smoothing = GaussianSmoothing(4, 5, 1)
settings=pydiffvg.SvgOptimizationSettings()
settings.global_override(["optimize_color"],False)
settings.global_override(["optimize_alpha"],False)
settings.global_override(["gradients","optimize_color"],False)
settings.global_override(["gradients","optimize_alpha"],False)
settings.global_override(["gradients","optimize_stops"],False)
settings.global_override(["gradients","optimize_location"],False)
settings.global_override(["optimizer"],"Adam")
settings.global_override(["paths","optimize_points"],False)
settings.global_override(["transforms","transform_lr"],1e-2)
settings.undefault("linearGradient3152")
settings.retrieve("linearGradient3152")[0]["transforms"]["optimize_transforms"]=False
#optim=pydiffvg.OptimizableSvg("note_small.svg",settings,verbose=True)
optim=pydiffvg.OptimizableSvg("heart_green.svg",settings,verbose=True)
#img=torchvision.transforms.ToTensor()(Image.open("note_transformed.png")).permute(1,2,0)
img=torchvision.transforms.ToTensor()(Image.open("heart_green_90.png")).permute(1,2,0)
name="heart_green_90"
pydiffvg.imwrite(img.cpu(), 'results/simple_transform_svg/target.png')
target = img.clone().detach().requires_grad_(False)
img=optim.render()
pydiffvg.imwrite(img.cpu(), 'results/simple_transform_svg/init.png')
def smooth(input, kernel):
input=torch.nn.functional.pad(input.permute(2,0,1).unsqueeze(0), (2, 2, 2, 2), mode='reflect')
output=kernel(input)
return output
def printimg(optim):
img=optim.render()
comp = img.clone().detach()
bg = torch.tensor([[[1., 1., 1.]]])
comprgb = comp[:, :, 0:3]
compalpha = comp[:, :, 3].unsqueeze(2)
comp = comprgb * compalpha \
+ bg * (1 - compalpha)
return comp
def comp_loss_and_grad(img, tgt, it, sz):
dif=img-tgt
loss=dif.pow(2).mean()
dif=dif.detach()
cdif=dif.clone().abs()
cdif[:,:,3]=1.
resdif=torch.nn.functional.interpolate(cdif.permute(2,0,1).unsqueeze(0),sz,mode='bilinear').squeeze().permute(1,2,0).abs()
pydiffvg.imwrite(resdif[:,:,0:4], 'results/simple_transform_svg/dif_{:04}.png'.format(it))
dif=dif.numpy()
padded=np.pad(dif,[(1,1),(1,1),(0,0)],mode='edge')
#print(padded[:-2,:,:].shape)
grad_x=(padded[:-2,:,:]-padded[2:,:,:])[:,1:-1,:]
grad_y=(padded[:,:-2,:]-padded[:,2:,:])[1:-1,:,:]
resshape=dif.shape
resshape=(resshape[0],resshape[1],2)
res=np.zeros(resshape)
for x in range(resshape[0]):
for y in range(resshape[1]):
A=np.concatenate((grad_x[x,y,:][:,np.newaxis],grad_y[x,y,:][:,np.newaxis]),axis=1)
b=-dif[x,y,:]
v=np.linalg.lstsq(np.dot(A.T,A),np.dot(A.T,b))
res[x,y,:]=v[0]
return loss, res
import colorsys
def print_gradimg(gradimg,it,shape=None):
out=torch.zeros((gradimg.shape[0],gradimg.shape[1],3),requires_grad=False,dtype=torch.float32)
for x in range(gradimg.shape[0]):
for y in range(gradimg.shape[1]):
h=math.atan2(gradimg[x,y,1],gradimg[x,y,0])
s=math.tanh(np.linalg.norm(gradimg[x,y,:]))
v=1.
vec=(gradimg[x,y,:].clip(min=-1,max=1)/2)+.5
#out[x,y,:]=torch.tensor(colorsys.hsv_to_rgb(h,s,v),dtype=torch.float32)
out[x,y,:]=torch.tensor([vec[0],vec[1],0])
if shape is not None:
out=torch.nn.functional.interpolate(out.permute(2,0,1).unsqueeze(0),shape,mode='bilinear').squeeze().permute(1,2,0)
pydiffvg.imwrite(out.cpu(), 'results/simple_transform_svg/grad_{:04}.png'.format(it))
# Run 150 Adam iterations.
for t in range(1000):
print('iteration:', t)
optim.zero_grad()
with open('results/simple_transform_svg/viter_{:04}.svg'.format(t),"w") as f:
f.write(optim.write_xml())
scale=inv_exp(1/16,math.pow(t/1000,1),0.5)
#print(scale)
#img = optim.render(seed=t+1,scale=scale)
img = optim.render(seed=t + 1, scale=None)
vis.line(torch.tensor([img.shape[0]]), X=torch.tensor([t]), win=name + " size", update="append",
opts={"title": name + " size"})
#print(img.shape)
#img = optim.render(seed=t + 1)
ptgt=target.permute(2,0,1).unsqueeze(0)
sz=img.shape[0:2]
restgt=torch.nn.functional.interpolate(ptgt,size=sz,mode='bilinear').squeeze().permute(1,2,0)
# Compute the loss function. Here it is L2.
#loss = (smooth(img,smoothing) - smooth(restgt,smoothing)).pow(2).mean()
#loss = (img - restgt).pow(2).mean()
#loss=(img-target).pow(2).mean()
loss,gradimg=comp_loss_and_grad(img, restgt,t,target.shape[0:2])
print_gradimg(gradimg,t,target.shape[0:2])
print('loss:', loss.item())
vis.line(loss.unsqueeze(0), X=torch.tensor([t]), win=name+" loss", update="append",
opts={"title": name + " loss"})
# Backpropagate the gradients.
loss.backward()
# Take a gradient descent step.
optim.step()
# Save the intermediate render.
comp=printimg(optim)
pydiffvg.imwrite(comp.cpu(), 'results/simple_transform_svg/iter_{:04}.png'.format(t))
# Render the final result.
img = optim.render()
# Save the images and differences.
pydiffvg.imwrite(img.cpu(), 'results/simple_transform_svg/final.png')
with open('results/simple_transform_svg/final.svg', "w") as f:
f.write(optim.write_xml())
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/simple_transform_svg/iter_%04d.png", "-vb", "20M",
"results/simple_transform_svg/out.mp4"])
call(["ffmpeg", "-framerate", "24", "-i",
"results/simple_transform_svg/grad_%04d.png", "-vb", "20M",
"results/simple_transform_svg/out_grad.mp4"])
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