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__classcell__r# r# r! r$ r s r c sb e Zd ZdZdeeeee dd fdd
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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 (string): 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`.
traincleanN)r split pass_namer r c s t j||d t|ddd t|ddd |dkr`__ 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 (string): 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.
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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"``.
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zKittiFlow.__getitem__r% c C s t |S r, )_read_16bits_png_with_flow_and_valid_maskr- r# r# r$ r. s zKittiFlow._read_flow)rG N)r? r@ rA r` r1 rB r r r rC r rD rE r6 r
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`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 (string): 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`.
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gg7 _qd S )NrK rI )rG valrN r r< z*.ppmrS zFlyingChairs_train_val.txtzmThe FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring).)ZdtyperG r rl r
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split_listrY Zsplit_idr! r# r$ r s zFlyingChairs.__init__r/ c s t |S )a 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.
r[ r\ r! r# r$ r6 s
zFlyingChairs.__getitem__r% c C s t |S r, r] r- r# r# r$ r. ) s zFlyingChairs._read_flow)rG Nr_ r# r# r! r$ r s r c sd e Zd ZdZdeeeeee dd fdd
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dZ ZS )r a `FlyingThings3D `_ 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 (string): 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`.
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| gg7 _| j||d g7 _q^q qd S )NrK rI rL rN rJ rO Zframes_cleanpassZframes_finalpassrr )rq rightrQ rQ rq rs r ) into_future into_pastz*/*c 3 s | ]}t | V qd S r, r ).0 image_dir)rr r# r$ f s z*FlyingThings3D.__init__..Z optical_flowc 3 s | ]}t | V qd S r, r )rv flow_dirrr directionr# r$ rx i s zcCould not find the FlyingThings3D flow images. Please make sure the directory structure is correct.rR z*.pfmr rt ru )r r r upperr itertoolsproductrV r rB rf rg rW r7 r r )r r rI rJ rr r rX ZcamerasZ
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zFlyingThings3D.__getitem__r% c C s t |S r, )r r- r# r# r$ r. s zFlyingThings3D._read_flow)rG rH rq Nr_ r# r# r! r$ r - s 2 r c sn e Zd ZdZdZdeeee dd fdd
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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 (string): 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.
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HD1K.__init__r% c C s t |S r, rj r- r# r# r$ r. s zHD1K._read_flowr/ c s t |S )a 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"``.
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