File size: 5,631 Bytes
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from pathlib import Path
from PIL import Image
import cv2
import torch
from easydict import EasyDict
import torchvision.transforms.functional as tf
from torch.utils.data import Dataset
from ..utils.log import get_matlist
from . import augment as Aug
import numpy as np
def get_files_from_directory(dir: Path):
return [f for f in dir.iterdir() if f.is_dir()]
class FabricsDataset(Dataset):
def __init__(
self,
split,
transform,
renderer,
matlist,
dir: typing.Optional[Path] = None,
set_seed_render: bool = False,
**kwargs
):
assert dir.is_dir()
assert matlist.is_file()
assert split in ['train', 'valid', 'all']
self.set_seed_render = set_seed_render
folders = get_files_from_directory(dir)
#folders = [folder for folder in folders if (folder / 'height.png').is_file()]
folders = [folder for folder in folders if (folder / 'normal.png').is_file()
and (folder / 'roughness.png').is_file()
and (folder / 'basecolor.png').is_file()]
valid_folders = []
for folder in folders:
R_path = folder / 'roughness.png'
R = Image.open(R_path).convert('RGB')
R_array = np.array(R)
if np.all(R_array == 255):
continue
valid_folders.append(folder)
folders = valid_folders
print("Размер датасета: ", len(folders))
# train/val/ split
self.split = split
k = int(len(folders) * .95)
if split == 'train':
self.folders = folders[:k]
elif split == 'valid':
self.folders = folders[k:]
elif split == 'all':
self.folders = folders
print(f'FabricsDataset list={matlist}:{self.split}=[{len(self.folders)}/{len(folders)}]')
#dtypes = ['normals', 'albedo', 'input', 'input']
dtypes = ['normals', 'albedo', 'input']
self.tf = Aug.Pipeline(*transform, dtypes=dtypes)
self.renderer = renderer
def __getitem__(self, index, quick=False):
folder = self.folders[index]
N_path = folder / 'normal.png'
A_path = folder / 'basecolor.png'
R_path = folder / 'roughness.png'
#D_path = folder / 'height.png'
N = tf.to_tensor(Image.open(N_path).convert('RGB'))
A = tf.to_tensor(Image.open(A_path).convert('RGB'))
R = tf.to_tensor(Image.open(R_path).convert('RGB'))
#D_pil = cv2.imread(str(D_path), cv2.IMREAD_GRAYSCALE)
#D = torch.from_numpy(D_pil)[None].repeat(3, 1, 1) / 255
# augmentation
#N, A, R, D = self.tf([N, A, R, D])
N, A, R = self.tf([N, A, R])
if self.set_seed_render:
torch.manual_seed(hash(folder.name))
# I, params = self.renderer([N, A, R, D], n_samples=1)
I, params = self.renderer([N, A, R], n_samples=1)
params = torch.stack(params)
# return homogenous object whatever the source: acg or sd
return EasyDict(
input=I[0],
input_params=params[:, 0],
normals=N,
albedo=A,
roughness=R,
#displacement=D,
name=folder.name,
)
def __len__(self):
return len(self.folders)
class AmbientCG(Dataset):
def __init__(
self,
split,
transform,
renderer,
matlist,
dir: typing.Optional[Path] = None,
set_seed_render: bool = False,
**kwargs
):
assert dir.is_dir()
assert matlist.is_file()
assert split in ['train', 'valid', 'all']
self.set_seed_render = set_seed_render
files = get_matlist(matlist, dir)
# train/val/ split
self.split = split
k = int(len(files) * .95)
if split == 'train':
self.files = files[:k]
elif split == 'valid':
self.files = files[k:]
elif split == 'all':
self.files = files
print(f'AmbientCG list={matlist}:{self.split}=[{len(self.files)}/{len(files)}]')
dtypes = ['normals', 'albedo', 'input', 'input']
self.tf = Aug.Pipeline(*transform, dtypes=dtypes)
self.renderer = renderer
def __getitem__(self, index, quick=False):
path = self.files[index]
name = path.stem.split('_')[0]
root = path.parent
N_path = root / f'{name}_2K-PNG_NormalGL.png'
N = tf.to_tensor(Image.open(N_path).convert('RGB'))
A_path = root / f'{name}_2K-PNG_Color.png'
A = tf.to_tensor(Image.open(A_path).convert('RGB'))
R_path = root / f'{name}_2K-PNG_Roughness.png'
R = tf.to_tensor(Image.open(R_path).convert('RGB'))
D_path = root / f'{name}_2K-PNG_Displacement.png'
D_pil = cv2.imread(str(D_path), cv2.IMREAD_GRAYSCALE)
D = torch.from_numpy(D_pil)[None].repeat(3, 1, 1) / 255
# augmentation
N, A, R, D = self.tf([N, A, R, D])
if self.set_seed_render:
torch.manual_seed(hash(name))
I, params = self.renderer([N, A, R, D], n_samples=1)
params = torch.stack(params)
# return homogenous object whatever the source: acg or sd
return EasyDict(
input=I[0],
input_params=params[:, 0],
normals=N,
albedo=A,
roughness=R,
displacement=D,
name=name,
)
def __len__(self):
return len(self.files)
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