File size: 6,410 Bytes
d643072 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import os
import pathlib
from typing import Any, Callable, Optional, Union
import numpy as np
from PIL import Image
from torch.utils.data.dataset import Dataset
from torchvision.datasets import ImageFolder
__all__ = ["load_image", "load_image_from_dir", "DMCrop", "CustomImageFolder", "ImageDataset"]
def load_image(data_path: str, mode="rgb") -> Image.Image:
img = Image.open(data_path)
if mode == "rgb":
img = img.convert("RGB")
return img
def load_image_from_dir(
dir_path: str,
suffix: Union[str, tuple[str, ...], list[str]] = (".jpg", ".JPEG", ".png"),
return_mode="path",
k: Optional[int] = None,
shuffle_func: Optional[Callable] = None,
) -> Union[list, tuple[list, list]]:
suffix = [suffix] if isinstance(suffix, str) else suffix
file_list = []
for dirpath, _, fnames in os.walk(dir_path):
for fname in fnames:
if pathlib.Path(fname).suffix not in suffix:
continue
image_path = os.path.join(dirpath, fname)
file_list.append(image_path)
if shuffle_func is not None and k is not None:
shuffle_file_list = shuffle_func(file_list)
file_list = shuffle_file_list or file_list
file_list = file_list[:k]
file_list = sorted(file_list)
if return_mode == "path":
return file_list
else:
files = []
path_list = []
for file_path in file_list:
try:
files.append(load_image(file_path))
path_list.append(file_path)
except Exception:
print(f"Fail to load {file_path}")
if return_mode == "image":
return files
else:
return path_list, files
class DMCrop:
"""center/random crop used in diffusion models"""
def __init__(self, size: int) -> None:
self.size = size
def __call__(self, pil_image: Image.Image) -> Image.Image:
"""
Center cropping implementation from ADM.
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
"""
image_size = self.size
if pil_image.size == (image_size, image_size):
return pil_image
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(tuple(x // 2 for x in pil_image.size), resample=Image.BOX)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return Image.fromarray(arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size])
class CustomImageFolder(ImageFolder):
def __init__(self, root: str, transform: Optional[Callable] = None, return_dict: bool = False):
root = os.path.expanduser(root)
self.return_dict = return_dict
super().__init__(root, transform)
def __getitem__(self, index: int) -> Union[dict[str, Any], tuple[Any, Any]]:
path, target = self.samples[index]
image = load_image(path)
if self.transform is not None:
image = self.transform(image)
if self.return_dict:
return {
"index": index,
"image_path": path,
"image": image,
"label": target,
}
else:
return image, target
class ImageDataset(Dataset):
def __init__(
self,
data_dirs: Union[str, list[str]],
splits: Optional[Union[str, list[Optional[str]]]] = None,
transform: Optional[Callable] = None,
suffix=(".jpg", ".JPEG", ".png"),
pil=True,
return_dict=True,
) -> None:
super().__init__()
self.data_dirs = [data_dirs] if isinstance(data_dirs, str) else data_dirs
if isinstance(splits, list):
assert len(splits) == len(self.data_dirs)
self.splits = splits
elif isinstance(splits, str):
assert len(self.data_dirs) == 1
self.splits = [splits]
else:
self.splits = [None for _ in range(len(self.data_dirs))]
self.transform = transform
self.pil = pil
self.return_dict = return_dict
# load all images [image_path]
self.samples = []
for data_dir, split in zip(self.data_dirs, self.splits):
if split is None:
samples = load_image_from_dir(data_dir, suffix, return_mode="path")
else:
samples = []
with open(split) as fin:
for line in fin.readlines():
relative_path = line[:-1]
full_path = os.path.join(data_dir, relative_path)
samples.append(full_path)
self.samples += samples
def __len__(self) -> int:
return len(self.samples)
def __getitem__(self, index: int, skip_image=False) -> dict[str, Any]:
image_path = self.samples[index]
if skip_image:
image = None
else:
try:
image = load_image(image_path, return_pil=self.pil)
except Exception:
print(f"Fail to load {image_path}")
raise OSError
if self.transform is not None:
image = self.transform(image)
if self.return_dict:
return {
"index": index,
"image_path": image_path,
"image_name": os.path.basename(image_path),
"data": image,
}
else:
return image
|