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Z G d dee Z! G d de! Z" G d de! Z# G d de! Z$ G d de! Z% G d de! Z&de'dej: fd Z(de'de ej: ej: f fd Z)y) N)ABCabstractmethod)globPath)AnyCallableOptionalUnion)Image )
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VisionDataset) KittiFlowSintelFlyingThings3D FlyingChairsHD1Kc e Zd ZdZdefdeeef dee de ege
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transformsloaderreturnc ^ t | | || _ g | _ g | _ || _ y )N)r )super__init__r
_flow_list _image_list_loader)selfr r r __class__s `/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/torchvision/datasets/_optical_flow.pyr" zFlowDataset.__init__$ s2 d#$%',. file_namec $ | j | S N)r% r& r* s r( _read_imgzFlowDataset._read_img2 s ||I&&r) c y r, r- s r(
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| j |||| \ }}}}| j s|||||fS |||fS )Nr r )r. r$ r# r1 _has_builtin_flow_maskr )r& r2 img1img2flowvalid_flow_masks r( __getitem__zFlowDataset.__getitem__: s ~~d . .u 5a 89~~d . .u 5a 89 ????4??5#9:D**(,%o"&%) )D? ?? &04dDRa0b -D$o & &/*Et_4 4t# #r) c , t | j S r, )lenr$ )r& s r( __len__zFlowDataset.__len__Q s 4##$$r) vc \ t j j j | g|z S r, )torchutilsdata
ConcatDataset)r& r= s r( __rmul__zFlowDataset.__rmul__T s# {{ --tfqj99r) ) __name__
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ej" fdZ xZS )r a `Sintel `_ 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`.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
traincleanNr split pass_namer r r c B t
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image_listir' s r( r" zSintel.__init__ sA dz&Iug4EFy+`__ 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.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
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NrT rQ rU rW r ingimage_2z*_10.pngz*_11.pngzZCould not find the Kitti flow images. Please make sure the directory structure is correct.rO flow_occ) r! r" r r ra r rG FileNotFoundErrorzipr$ r# )
r& r rQ r r images1images2r5 r6 r' s
r( r" zKittiFlow.__init__ s dz&Iug4EFDzK'55=9c$"2Z"? @ABc$"2Z"? @ABg#l
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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"``.
rl rm s r( r9 zKittiFlow.__getitem__ rn r) r* c t | S r, )_read_16bits_png_with_flow_and_valid_maskr- s r( r1 zKittiFlow._read_flow
8CCr) )rD rE rF rt r4 r r rG r r
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r a `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`.
Nr rQ r r c t
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c_ _ y )N)r r rQ )rO valrW r rA z*.ppmr^ zFlyingChairs_train_val.txtzmThe FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring).)dtyperO r r r
)r! r" r r ra r rG r_ pathexistsr| ru loadtxtint32rb r; r# r$ ) r& r rQ r imagesflowssplit_file_name
split_listri split_idr' s r( r" zFlyingChairs.__init__ s4
dz:ug4DEDzN*S !8 9:;tCv
7 89:6ww~~d_ 45#
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s5z" IA!!}H X]8WX=E!H:- fQUmVAEAI5F%G$HH Ir) r2 c " 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.
rl rm s r( r9 zFlyingChairs.__getitem__* rn r) r* c t | S r, rp r- s r( r1 zFlyingChairs._read_flow9 rr r) )rO N)rD rE rF rt r rG r r
r r" rI rJ rK r9 ru rv r1 rL rM s @r( r r sq 2IU39 - Ic IQYZbQc Ios I.
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* $C $BJJ $r) r c e Zd ZdZddddefdeeef deded ed
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f d df fd
Z de d ee
ef f fd Zded ej" fdZ xZS )r a `FlyingThings3D `_ dataset for optical flow.
The dataset is expected to have the following structure: ::
root
FlyingThings3D
frames_cleanpass
TEST
TRAIN
frames_finalpass
TEST
TRAIN
optical_flow
TEST
TRAIN
Args:
root (str or ``pathlib.Path``): Root directory of the intel FlyingThings3D Dataset.
split (string, optional): The dataset split, either "train" (default) or "test"
pass_name (string, optional): The pass to use, either "clean" (default) or "final" or "both". See link above for
details on the different passes.
camera (string, optional): Which camera to return images from. Can be either "left" (default) or "right" or "both".
transforms (callable, optional): A function/transform that takes in
``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
``valid_flow_mask`` is expected for consistency with other datasets which
return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
rO rP leftNr rQ rR camerar r r c
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c_ y )NrT rQ rU rW rR rY frames_cleanpassframes_finalpassr )r rightr[ r[ r r r ) into_future into_pastz*/*c 3 : K | ] }t | z y wr, r ).0 image_dirr s r( z*FlyingThings3D.__init__..z s UYY& 8 Us optical_flowc 3 @ K | ] }t | z z y wr, r )r flow_dirr directions r( r z*FlyingThings3D.__init__..} s ]xtH~ 9F B ]s zcCould not find the FlyingThings3D flow images. Please make sure the directory structure is correct.r] z*.pfmr r r )r! r" r upperr itertoolsproductra r rG r| r} rb r; r$ r# )r& r rQ rR r r r rc cameras
directions
image_dirs flow_dirsr r r r ri r r' s ` @r( r" zFlyingThings3D.__init__] s1 dz&Iug4EF
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:
: :r) r2 c " t | | S rk rl rm s r( r9 zFlyingThings3D.__getitem__ rn r) r* c t | S r, )r r- s r( r1 zFlyingThings3D._read_flow rr r) rs rM s @r( r r = s D )-'51:CI 1: 1: 1:
1: X&
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Z d ed e
ej ej f fd Zd
ed eeef f fd Z xZS )r a `HD1K `__ 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.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
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hd1k_inputrz r hd1k_challengez*10.pngz*11.pngzTCould not find the HD1K images. Please make sure the directory structure is correct.)
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