import json
import logging
import shutil
from collections.abc import Callable
from pathlib import Path
from typing import Literal, NamedTuple
import torch
from einops import rearrange, repeat
from PIL import Image
from torchvision import tv_tensors
from torchvision.datasets import VisionDataset
from torchvision.datasets.utils import (
download_and_extract_archive,
download_url,
)
from torchvision.transforms.v2 import functional as F
class CamVidClass(NamedTuple):
name: str
index: int
color: tuple[int, int, int]
[docs]class CamVid(VisionDataset):
# Notes: some classes are not used here
classes = [
CamVidClass("animal", 0, (64, 128, 64)),
CamVidClass("archway", 1, (192, 0, 128)),
CamVidClass("bicyclist", 2, (0, 128, 192)),
CamVidClass("bridge", 3, (0, 128, 64)),
CamVidClass("building", 4, (128, 0, 0)),
CamVidClass("car", 5, (64, 0, 128)),
CamVidClass("cart_luggage_pram", 6, (64, 0, 192)),
CamVidClass("child", 7, (192, 128, 64)),
CamVidClass("column_pole", 8, (192, 192, 128)),
CamVidClass("fence", 9, (64, 64, 128)),
CamVidClass("lane_mkgs_driv", 10, (128, 0, 192)),
CamVidClass("lane_mkgs_non_driv", 11, (192, 0, 64)),
CamVidClass("misc_text", 12, (128, 128, 64)),
CamVidClass("motorcycle_scooter", 13, (192, 0, 192)),
CamVidClass("othermoving", 14, (128, 64, 64)),
CamVidClass("parking_block", 15, (64, 192, 128)),
CamVidClass("pedestrian", 16, (64, 64, 0)),
CamVidClass("road", 17, (128, 64, 128)),
CamVidClass("road_shoulder", 18, (128, 128, 192)),
CamVidClass("sidewalk", 19, (0, 0, 192)),
CamVidClass("sign_symbol", 20, (192, 128, 128)),
CamVidClass("sky", 21, (128, 128, 128)),
CamVidClass("suv_pickup_truck", 22, (64, 128, 192)),
CamVidClass("traffic_cone", 23, (0, 0, 64)),
CamVidClass("traffic_light", 24, (0, 64, 64)),
CamVidClass("train", 25, (192, 64, 128)),
CamVidClass("tree", 26, (128, 128, 0)),
CamVidClass("truck_bus", 27, (192, 128, 192)),
CamVidClass("tunnel", 28, (64, 0, 64)),
CamVidClass("vegetation_misc", 29, (192, 192, 0)),
CamVidClass("void", 30, (0, 0, 0)),
CamVidClass("wall", 31, (64, 192, 0)),
]
superclasses = [
CamVidClass("sky", 0, (128, 128, 128)),
CamVidClass("building", 1, (128, 0, 0)),
CamVidClass("pole", 2, (192, 192, 128)),
CamVidClass("road", 3, (128, 64, 128)),
CamVidClass("pavement", 4, (0, 0, 192)),
CamVidClass("tree", 5, (128, 128, 0)),
CamVidClass("sign_symbol", 6, (192, 128, 128)),
CamVidClass("fence", 7, (64, 64, 128)),
CamVidClass("car", 8, (64, 0, 128)),
CamVidClass("pedestrian", 9, (64, 64, 0)),
CamVidClass("bicyclist", 10, (0, 128, 192)),
CamVidClass("void", None, (0, 0, 0)),
]
superclasses_indices = [
[21],
[3, 4, 31, 28, 1],
[8, 23],
[17, 10, 11],
[19, 15, 18],
[26, 29],
[20, 12, 24],
[9],
[5, 22, 27, 25, 14],
[16, 7, 6, 0],
[2, 13],
]
urls = {
"raw": "http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/files/701_StillsRaw_full.zip",
"label": "http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/data/LabeledApproved_full.zip",
"splits": "https://raw.githubusercontent.com/torch-uncertainty/dataset-metadata/main/segmentation/camvid/splits.json",
}
splits_md5 = "db45289aaa83c60201391b11e78c6382"
filenames = {
"raw": "701_StillsRaw_full.zip",
"label": "LabeledApproved_full.zip",
}
base_folder = "camvid"
num_samples = {
"train": 367,
"val": 101,
"test": 233,
"all": 701,
}
def __init__(
self,
root: str,
group_classes: bool = True,
split: Literal["train", "val", "test"] | None = None,
transforms: Callable | None = None,
download: bool = False,
) -> None:
"""`CamVid <http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/>`_ Dataset.
Args:
root (str): Root directory of dataset where ``camvid/`` exists or
will be saved to if download is set to ``True``.
group_classes (bool, optional): Whether to group the 32 classes into
11 superclasses. Default: ``True``.
split (str, optional): The dataset split, supports ``train``,
``val`` and ``test``. Default: ``None``.
transforms (callable, optional): A function/transform that takes
input sample and its target as entry and returns a transformed
version. Default: ``None``.
download (bool, optional): If true, downloads the dataset from the
internet and puts it in root directory. If dataset is already
downloaded, it is not downloaded again.
"""
if split not in ["train", "val", "test", None]:
raise ValueError(
f"Unknown split '{split}'. " "Supported splits are ['train', 'val', 'test', None]"
)
super().__init__(root, transforms, None, None)
self.group_classes = group_classes
self.class_to_superclass = []
for i in range(32):
if i == 30: # For void
self.class_to_superclass.append(None)
for j, superclass in enumerate(self.superclasses_indices):
if i in superclass:
self.class_to_superclass.append(j)
break
if download:
self.download()
if not self._check_integrity():
raise RuntimeError(
"Dataset not found or corrupted. " "You can use download=True to download it"
)
# get filenames for split
if split is None:
self.images = sorted((Path(self.root) / "camvid" / "raw").glob("*.png"))
self.targets = sorted((Path(self.root) / "camvid" / "label").glob("*.png"))
else:
with (Path(self.root) / "camvid" / "splits.json").open() as f:
filenames = json.load(f)[split]
self.images = sorted(
[
path
for path in (Path(self.root) / "camvid" / "raw").glob("*.png")
if path.stem in filenames
]
)
self.targets = sorted(
[
path
for path in (Path(self.root) / "camvid" / "label").glob("*.png")
if path.stem[:-2] in filenames
]
)
self.split = split if split is not None else "all"
[docs] def encode_target(self, target: Image.Image) -> torch.Tensor:
"""Encode target image to tensor.
Args:
target (Image.Image): Target PIL image.
Returns:
torch.Tensor: Encoded target.
"""
colored_target = F.pil_to_tensor(target)
colored_target = rearrange(colored_target, "c h w -> h w c")
target = torch.zeros_like(colored_target[..., :1])
# convert target color to index
for camvid_class in self.classes:
index = camvid_class.index if camvid_class.index != 30 else 255
if self.group_classes and index != 255:
index = self.class_to_superclass[index]
target[
(colored_target == torch.tensor(camvid_class.color, dtype=target.dtype)).all(dim=-1)
] = index
return rearrange(target, "h w c -> c h w")
[docs] def decode_target(self, target: torch.Tensor) -> Image.Image:
"""Decode target tensor to image.
Args:
target (torch.Tensor): Target tensor.
Returns:
Image.Image: Decoded target as a PIL.Image.
"""
colored_target = repeat(target.clone(), "h w -> h w 3", c=3)
if not self.group_classes:
for camvid_class in self.classes:
colored_target[
(target == torch.tensor(camvid_class.index, dtype=target.dtype)).all(dim=0)
] = torch.tensor(camvid_class.color, dtype=target.dtype)
else:
for camvid_class in self.superclasses:
colored_target[
(target == torch.tensor(camvid_class.index, dtype=target.dtype)).all(dim=0)
] = torch.tensor(camvid_class.color, dtype=target.dtype)
return F.to_pil_image(rearrange(colored_target, "h w c -> c h w"))
@property
def color_palette(self) -> list[tuple[int, int, int]]:
"""Return the color palette of the dataset."""
if self.group_classes:
return [camvid_class.color for camvid_class in self.superclasses]
return [camvid_class.color for camvid_class in self.classes]
def __getitem__(self, index: int) -> tuple[tv_tensors.Image, tv_tensors.Mask]:
"""Get the image and target at the given index.
Args:
index (int): Sample index.
Returns:
tuple[tv_tensors.Image, tv_tensors.Mask]: Image and target.
"""
image = tv_tensors.Image(Image.open(self.images[index]).convert("RGB"))
target = tv_tensors.Mask(self.encode_target(Image.open(self.targets[index])))
if self.transforms is not None:
image, target = self.transforms(image, target)
return image, target
def __len__(self) -> int:
"""Return the number of samples."""
return self.num_samples[self.split]
def _check_integrity(self) -> bool:
"""Check if the dataset exists."""
if len(list((Path(self.root) / "camvid" / "raw").glob("*.png"))) != self.num_samples["all"]:
return False
if (
len(list((Path(self.root) / "camvid" / "label").glob("*.png")))
!= self.num_samples["all"]
):
return False
return (Path(self.root) / "camvid" / "splits.json").exists()
[docs] def download(self) -> None:
"""Download the CamVid data if it doesn't exist already."""
if self._check_integrity():
logging.info("Files already downloaded and verified")
return
if (Path(self.root) / self.base_folder).exists():
shutil.rmtree(Path(self.root) / self.base_folder)
download_and_extract_archive(
self.urls["raw"],
self.root,
extract_root=Path(self.root) / "camvid",
filename=self.filenames["raw"],
)
(Path(self.root) / "camvid" / "701_StillsRaw_full").replace(
Path(self.root) / "camvid" / "raw"
)
download_and_extract_archive(
self.urls["label"],
self.root,
extract_root=Path(self.root) / "camvid" / "label",
filename=self.filenames["label"],
)
download_url(
self.urls["splits"],
Path(self.root) / "camvid",
filename="splits.json",
md5=self.splits_md5,
)
