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VisionDataset) KittiFlowSintelFlyingThings3D FlyingChairsHD1Kc e Zd ZdZddeeef dee ddf fd
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r' indexc | | j | d }| | j | d }| j r0| | j | }| j r|\ }}nd }nd x}}| j | |||| \ }}}}| j s|||||fS |||fS )Nr r )r/ r# r" r4 _has_builtin_flow_maskr )r$ r5 img1img2flowvalid_flow_masks r& __getitem__zFlowDataset.__getitem__6 s ~~d .u 5a 899~~d .u 5a 899 ? *??4?5#9::D*
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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 (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`.
traincleanNr split pass_namer r c t || t |dd t |dd |dk rdd gn|g}t | d
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r& r! zSintel.__init__} s dz:::ug4EFFFFy+`__ 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.
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NrV rS rW rY r ingimage_2z*_10.pngz*_11.pngzZCould not find the Kitti flow images. Please make sure the directory structure is correct.rQ flow_occ) r r! r r rc r rJ FileNotFoundErrorzipr# r" ) r$ r rS r images1images2r8 r9 r% s r& r! zKittiFlow.__init__ s=
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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"``.
rn ro s r& r< zKittiFlow.__getitem__ rp r' r( c t | S r1 )_read_16bits_png_with_flow_and_valid_maskr3 s r& r4 zKittiFlow._read_flow 8CCCr' rQ N)rG rH rI rv r7 r rJ r r
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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`.
rQ Nr rS r r c t || t |dd t | dz }t t t
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c_ bd S )NrV rS )rQ valrY r rD z*.ppmr` zFlyingChairs_train_val.txtzmThe FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring).)dtyperQ r r r )r r! r r rc r rJ ra pathexistsr~ rw loadtxtint32rd r> r" r# ) r$ r rS r imagesflowssplit_file_name
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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.
rn ro s r& r< zFlyingChairs.__getitem__ rp r' r( c t | S r1 rr r3 s r& r4 zFlyingChairs._read_flow( rt r' r ru rO s @r& r r s 2I IU39 - Ic IQYZbQc Ios I I I I I I.
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ded 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`.
rQ rR leftNr rS rT camerar r c
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c_ d S )NrV rS rW rY rT r[ frames_cleanpassframes_finalpassr )r rightr] r] r r r ) into_future into_pastz*/*c 3 < K | ]}t | z V d S r1 r ).0 image_dirr s r& z*FlyingThings3D.__init__..e s. U UYY& 8 U U U U U Ur' optical_flowc 3 B K | ]}t | z z V d S r1 r )r flow_dirr directions r& r z*FlyingThings3D.__init__..h s3 ] ]xtH~~ 9F B ] ] ] ] ] ]r' zcCould not find the FlyingThings3D flow images. Please make sure the directory structure is correct.r_ z*.pfmr r r )r r! r upperr itertoolsproductrc r rJ r~ r rd r> r# r" )r$ r rS rT r r re cameras
directions
image_dirs flow_dirsr r r r rk r r% s ` @r& r! zFlyingThings3D.__init__I s dz:::ug4EFFFF
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:
: : :r' r5 c F t | S rm rn ro s r& r< zFlyingThings3D.__getitem__{ rp r' r( c t | S r1 )r r3 s r& r4 zFlyingThings3D._read_flow rt r' )rQ rR r Nru rO s @r& r r , s > )-
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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.
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