Source code for torchvision.datasets._optical_flow
import itertools
import os
from abc import ABC, abstractmethod
from glob import glob
from pathlib import Path
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ..io.image import decode_png, read_file
from .utils import _read_pfm, verify_str_arg
from .vision import VisionDataset
T1 = Tuple[Image.Image, Image.Image, Optional[np.ndarray], Optional[np.ndarray]]
T2 = Tuple[Image.Image, Image.Image, Optional[np.ndarray]]
__all__ = (
"KittiFlow",
"Sintel",
"FlyingThings3D",
"FlyingChairs",
"HD1K",
)
class FlowDataset(ABC, VisionDataset):
# Some datasets like Kitti have a built-in valid_flow_mask, indicating which flow values are valid
# For those we return (img1, img2, flow, valid_flow_mask), and for the rest we return (img1, img2, flow),
# and it's up to whatever consumes the dataset to decide what valid_flow_mask should be.
_has_builtin_flow_mask = False
def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None:
super().__init__(root=root)
self.transforms = transforms
self._flow_list: List[str] = []
self._image_list: List[List[str]] = []
def _read_img(self, file_name: str) -> Image.Image:
img = Image.open(file_name)
if img.mode != "RGB":
img = img.convert("RGB") # type: ignore[assignment]
return img
@abstractmethod
def _read_flow(self, file_name: str):
# Return the flow or a tuple with the flow and the valid_flow_mask if _has_builtin_flow_mask is True
pass
def __getitem__(self, index: int) -> Union[T1, T2]:
img1 = self._read_img(self._image_list[index][0])
img2 = self._read_img(self._image_list[index][1])
if self._flow_list: # it will be empty for some dataset when split="test"
flow = self._read_flow(self._flow_list[index])
if self._has_builtin_flow_mask:
flow, valid_flow_mask = flow
else:
valid_flow_mask = None
else:
flow = valid_flow_mask = None
if self.transforms is not None:
img1, img2, flow, valid_flow_mask = self.transforms(img1, img2, flow, valid_flow_mask)
if self._has_builtin_flow_mask or valid_flow_mask is not None:
# The `or valid_flow_mask is not None` part is here because the mask can be generated within a transform
return img1, img2, flow, valid_flow_mask
else:
return img1, img2, flow
def __len__(self) -> int:
return len(self._image_list)
def __rmul__(self, v: int) -> torch.utils.data.ConcatDataset:
return torch.utils.data.ConcatDataset([self] * v)
[docs]class Sintel(FlowDataset):
"""`Sintel <http://sintel.is.tue.mpg.de/>`_ Dataset for optical flow.
The dataset is expected to have the following structure: ::
root
Sintel
testing
clean
scene_1
scene_2
...
final
scene_1
scene_2
...
training
clean
scene_1
scene_2
...
final
scene_1
scene_2
...
flow
scene_1
scene_2
...
Args:
root (str or ``pathlib.Path``): Root directory of the Sintel Dataset.
split (string, optional): The dataset split, either "train" (default) or "test"
pass_name (string, optional): The pass to use, either "clean" (default), "final", or "both". See link above for
details on the different passes.
transforms (callable, optional): A function/transform that takes in
``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
``valid_flow_mask`` is expected for consistency with other datasets which
return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
"""
def __init__(
self,
root: Union[str, Path],
split: str = "train",
pass_name: str = "clean",
transforms: Optional[Callable] = None,
) -> None:
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both"))
passes = ["clean", "final"] if pass_name == "both" else [pass_name]
root = Path(root) / "Sintel"
flow_root = root / "training" / "flow"
for pass_name in passes:
split_dir = "training" if split == "train" else split
image_root = root / split_dir / pass_name
for scene in os.listdir(image_root):
image_list = sorted(glob(str(image_root / scene / "*.png")))
for i in range(len(image_list) - 1):
self._image_list += [[image_list[i], image_list[i + 1]]]
if split == "train":
self._flow_list += sorted(glob(str(flow_root / scene / "*.flo")))
[docs] def __getitem__(self, index: int) -> Union[T1, T2]:
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 3-tuple with ``(img1, img2, flow)``.
The flow is a numpy array of shape (2, H, W) and the images are PIL images.
``flow`` is None if ``split="test"``.
If a valid flow mask is generated within the ``transforms`` parameter,
a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned.
"""
return super().__getitem__(index)
def _read_flow(self, file_name: str) -> np.ndarray:
return _read_flo(file_name)
[docs]class KittiFlow(FlowDataset):
"""`KITTI <http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow>`__ dataset for optical flow (2015).
The dataset is expected to have the following structure: ::
root
KittiFlow
testing
image_2
training
image_2
flow_occ
Args:
root (str or ``pathlib.Path``): Root directory of the KittiFlow Dataset.
split (string, optional): The dataset split, either "train" (default) or "test"
transforms (callable, optional): A function/transform that takes in
``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
"""
_has_builtin_flow_mask = True
def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
root = Path(root) / "KittiFlow" / (split + "ing")
images1 = sorted(glob(str(root / "image_2" / "*_10.png")))
images2 = sorted(glob(str(root / "image_2" / "*_11.png")))
if not images1 or not images2:
raise FileNotFoundError(
"Could not find the Kitti flow images. Please make sure the directory structure is correct."
)
for img1, img2 in zip(images1, images2):
self._image_list += [[img1, img2]]
if split == "train":
self._flow_list = sorted(glob(str(root / "flow_occ" / "*_10.png")))
[docs] def __getitem__(self, index: int) -> Union[T1, T2]:
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 4-tuple with ``(img1, img2, flow, valid_flow_mask)``
where ``valid_flow_mask`` is a numpy boolean mask of shape (H, W)
indicating which flow values are valid. The flow is a numpy array of
shape (2, H, W) and the images are PIL images. ``flow`` and ``valid_flow_mask`` are None if
``split="test"``.
"""
return super().__getitem__(index)
def _read_flow(self, file_name: str) -> Tuple[np.ndarray, np.ndarray]:
return _read_16bits_png_with_flow_and_valid_mask(file_name)
[docs]class FlyingChairs(FlowDataset):
"""`FlyingChairs <https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs>`_ Dataset for optical flow.
You will also need to download the FlyingChairs_train_val.txt file from the dataset page.
The dataset is expected to have the following structure: ::
root
FlyingChairs
data
00001_flow.flo
00001_img1.ppm
00001_img2.ppm
...
FlyingChairs_train_val.txt
Args:
root (str or ``pathlib.Path``): Root directory of the FlyingChairs Dataset.
split (string, optional): The dataset split, either "train" (default) or "val"
transforms (callable, optional): A function/transform that takes in
``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
``valid_flow_mask`` is expected for consistency with other datasets which
return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
"""
def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "val"))
root = Path(root) / "FlyingChairs"
images = sorted(glob(str(root / "data" / "*.ppm")))
flows = sorted(glob(str(root / "data" / "*.flo")))
split_file_name = "FlyingChairs_train_val.txt"
if not os.path.exists(root / split_file_name):
raise FileNotFoundError(
"The FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring)."
)
split_list = np.loadtxt(str(root / split_file_name), dtype=np.int32)
for i in range(len(flows)):
split_id = split_list[i]
if (split == "train" and split_id == 1) or (split == "val" and split_id == 2):
self._flow_list += [flows[i]]
self._image_list += [[images[2 * i], images[2 * i + 1]]]
[docs] def __getitem__(self, index: int) -> Union[T1, T2]:
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 3-tuple with ``(img1, img2, flow)``.
The flow is a numpy array of shape (2, H, W) and the images are PIL images.
``flow`` is None if ``split="val"``.
If a valid flow mask is generated within the ``transforms`` parameter,
a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned.
"""
return super().__getitem__(index)
def _read_flow(self, file_name: str) -> np.ndarray:
return _read_flo(file_name)
[docs]class FlyingThings3D(FlowDataset):
"""`FlyingThings3D <https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html>`_ dataset for optical flow.
The dataset is expected to have the following structure: ::
root
FlyingThings3D
frames_cleanpass
TEST
TRAIN
frames_finalpass
TEST
TRAIN
optical_flow
TEST
TRAIN
Args:
root (str or ``pathlib.Path``): Root directory of the intel FlyingThings3D Dataset.
split (string, optional): The dataset split, either "train" (default) or "test"
pass_name (string, optional): The pass to use, either "clean" (default) or "final" or "both". See link above for
details on the different passes.
camera (string, optional): Which camera to return images from. Can be either "left" (default) or "right" or "both".
transforms (callable, optional): A function/transform that takes in
``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
``valid_flow_mask`` is expected for consistency with other datasets which
return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
"""
def __init__(
self,
root: Union[str, Path],
split: str = "train",
pass_name: str = "clean",
camera: str = "left",
transforms: Optional[Callable] = None,
) -> None:
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
split = split.upper()
verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both"))
passes = {
"clean": ["frames_cleanpass"],
"final": ["frames_finalpass"],
"both": ["frames_cleanpass", "frames_finalpass"],
}[pass_name]
verify_str_arg(camera, "camera", valid_values=("left", "right", "both"))
cameras = ["left", "right"] if camera == "both" else [camera]
root = Path(root) / "FlyingThings3D"
directions = ("into_future", "into_past")
for pass_name, camera, direction in itertools.product(passes, cameras, directions):
image_dirs = sorted(glob(str(root / pass_name / split / "*/*")))
image_dirs = sorted(Path(image_dir) / camera for image_dir in image_dirs)
flow_dirs = sorted(glob(str(root / "optical_flow" / split / "*/*")))
flow_dirs = sorted(Path(flow_dir) / direction / camera for flow_dir in flow_dirs)
if not image_dirs or not flow_dirs:
raise FileNotFoundError(
"Could not find the FlyingThings3D flow images. "
"Please make sure the directory structure is correct."
)
for image_dir, flow_dir in zip(image_dirs, flow_dirs):
images = sorted(glob(str(image_dir / "*.png")))
flows = sorted(glob(str(flow_dir / "*.pfm")))
for i in range(len(flows) - 1):
if direction == "into_future":
self._image_list += [[images[i], images[i + 1]]]
self._flow_list += [flows[i]]
elif direction == "into_past":
self._image_list += [[images[i + 1], images[i]]]
self._flow_list += [flows[i + 1]]
[docs] def __getitem__(self, index: int) -> Union[T1, T2]:
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 3-tuple with ``(img1, img2, flow)``.
The flow is a numpy array of shape (2, H, W) and the images are PIL images.
``flow`` is None if ``split="test"``.
If a valid flow mask is generated within the ``transforms`` parameter,
a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned.
"""
return super().__getitem__(index)
def _read_flow(self, file_name: str) -> np.ndarray:
return _read_pfm(file_name)
[docs]class HD1K(FlowDataset):
"""`HD1K <http://hci-benchmark.iwr.uni-heidelberg.de/>`__ dataset for optical flow.
The dataset is expected to have the following structure: ::
root
hd1k
hd1k_challenge
image_2
hd1k_flow_gt
flow_occ
hd1k_input
image_2
Args:
root (str or ``pathlib.Path``): Root directory of the HD1K Dataset.
split (string, optional): The dataset split, either "train" (default) or "test"
transforms (callable, optional): A function/transform that takes in
``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
"""
_has_builtin_flow_mask = True
def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
root = Path(root) / "hd1k"
if split == "train":
# There are 36 "sequences" and we don't want seq i to overlap with seq i + 1, so we need this for loop
for seq_idx in range(36):
flows = sorted(glob(str(root / "hd1k_flow_gt" / "flow_occ" / f"{seq_idx:06d}_*.png")))
images = sorted(glob(str(root / "hd1k_input" / "image_2" / f"{seq_idx:06d}_*.png")))
for i in range(len(flows) - 1):
self._flow_list += [flows[i]]
self._image_list += [[images[i], images[i + 1]]]
else:
images1 = sorted(glob(str(root / "hd1k_challenge" / "image_2" / "*10.png")))
images2 = sorted(glob(str(root / "hd1k_challenge" / "image_2" / "*11.png")))
for image1, image2 in zip(images1, images2):
self._image_list += [[image1, image2]]
if not self._image_list:
raise FileNotFoundError(
"Could not find the HD1K images. Please make sure the directory structure is correct."
)
def _read_flow(self, file_name: str) -> Tuple[np.ndarray, np.ndarray]:
return _read_16bits_png_with_flow_and_valid_mask(file_name)
[docs] def __getitem__(self, index: int) -> Union[T1, T2]:
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` where ``valid_flow_mask``
is a numpy boolean mask of shape (H, W)
indicating which flow values are valid. The flow is a numpy array of
shape (2, H, W) and the images are PIL images. ``flow`` and ``valid_flow_mask`` are None if
``split="test"``.
"""
return super().__getitem__(index)
def _read_flo(file_name: str) -> np.ndarray:
"""Read .flo file in Middlebury format"""
# Code adapted from:
# http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
# Everything needs to be in little Endian according to
# https://vision.middlebury.edu/flow/code/flow-code/README.txt
with open(file_name, "rb") as f:
magic = np.fromfile(f, "c", count=4).tobytes()
if magic != b"PIEH":
raise ValueError("Magic number incorrect. Invalid .flo file")
w = int(np.fromfile(f, "<i4", count=1))
h = int(np.fromfile(f, "<i4", count=1))
data = np.fromfile(f, "<f4", count=2 * w * h)
return data.reshape(h, w, 2).transpose(2, 0, 1)
def _read_16bits_png_with_flow_and_valid_mask(file_name: str) -> Tuple[np.ndarray, np.ndarray]:
flow_and_valid = decode_png(read_file(file_name)).to(torch.float32)
flow, valid_flow_mask = flow_and_valid[:2, :, :], flow_and_valid[2, :, :]
flow = (flow - 2**15) / 64 # This conversion is explained somewhere on the kitti archive
valid_flow_mask = valid_flow_mask.bool()
# For consistency with other datasets, we convert to numpy
return flow.numpy(), valid_flow_mask.numpy()
