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VisionDataset) KittiFlowSintelFlyingThings3D FlyingChairsHD1Kc s e Zd ZdZdefdeeef dee de ege
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transformsloaderreturnc s* t j|d || _g | _g | _|| _d S )N)r )super__init__r
_flow_list _image_list_loader)selfr r r __class__ h/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torchvision/datasets/_optical_flow.pyr $ s
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_read_flow5 s zFlowDataset._read_flowindexc C s | | j| d }| | j| d }| jr*| | j| }| jr'|\}}q.d }nd }}| jd ur?| ||||\}}}}| jsF|d urL||||fS |||fS )Nr r )r+ r! r r- _has_builtin_flow_maskr )r# r. img1img2flowvalid_flow_maskr& r& r' __getitem__: s
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__module__ __qualname__r/ r r
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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`.
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
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image_listir$ r& r' r s$ ""zSintel.__init__r. 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="test"``.
If a valid flow mask is generated within the ``transforms`` parameter,
a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned.
r r4 r# r. r$ r& r' r4
zSintel.__getitem__r( c C t |S r) _read_flor* r& r& r' r- zSintel._read_flowr= r> r? __doc__r r
r@ r r r r r rA rB rC r4 npndarrayr- rD r& r& r$ r' r X s* .
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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.
TrE Nr rG r r r c s t j|||d t|ddd t|d |d }ttt|d d }ttt|d d }|r6|s:td
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NrI rG rJ rL r Zingimage_2z*_10.pngz*_11.pngzZCould not find the Kitti flow images. Please make sure the directory structure is correct.rE flow_occ) r r r r rT r r@ FileNotFoundErrorzipr! r ) r# r rG r r images1images2r0 r1 r$ r& r' r s zKittiFlow.__init__r. c rX )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"``.
rZ r[ r$ r& r' r4 r\ zKittiFlow.__getitem__r( c C r] r) )_read_16bits_png_with_flow_and_valid_maskr* r& r& r' r- r` zKittiFlow._read_flow)r= r> r? rb r/ r r
r@ r r r r r rA rB rC r4 tuplerc rd r- rD r& r& r$ r' r s&
&r c sr e Zd ZdZddeeef dedee ddf fdd
Z d
e
dee e f f fd d Z
d
edejfddZ ZS )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`.
rE Nr rG r r c
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gg7 _qMd S )N)r r rG )rE valrL r r: z*.ppmrQ zFlyingChairs_train_val.txtzmThe FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring).)ZdtyperE r rn r )r r r r rT r r@ rR pathexistsrg rc ZloadtxtZint32rU r5 r r! )
r# r rG r imagesflowsZsplit_file_nameZ
split_listrW Zsplit_idr$ r& r' r s$ (zFlyingChairs.__init__r. c rX )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.
rZ r[ r$ r& r' r4 * r\ zFlyingChairs.__getitem__r( c C r] r) r^ r* r& r& r' r- 9 r` zFlyingChairs._read_flow)rE N)r= r> r? rb r
r@ r r r r rA rB rC r4 rc rd r- rD r& r& r$ r' r s
, r c s e Zd ZdZddddefdeeef deded ed
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f d df fd
d
Z de d ee
ef f fdd Zded ejfddZ 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 (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.
rE rF leftNr rG rH camerar r r c s t j|||d t|ddd | }t|ddd dgdgddgd| }t d d
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|d D ]A}dkr| j|| ||d gg7 _| j|| g7 _qdkr| j||d || gg7 _| j||d g7 _qqqHd S )NrI rG rJ rL rH rM Zframes_cleanpassZframes_finalpassrt )rs rightrO rO rs ru r ) into_future into_pastz*/*c 3 s | ] }t | V qd S r) r ).0 image_dir)rt r& r' z s z*FlyingThings3D.__init__..Z optical_flowc 3 s | ] }t | V qd S r) r )rx flow_dirrt directionr& r' rz } s zcCould not find the FlyingThings3D flow images. Please make sure the directory structure is correct.rP z*.pfmr rv rw )r r r upperr itertoolsproductrT r r@ rg rh rU r5 r! r )r# r rG rH rt r r rV ZcamerasZ
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image_dirsZ flow_dirsry r{ rq rr rW r$ r| r' r ] sJ zFlyingThings3D.__init__r. c rX rY rZ r[ r$ r& r' r4 r\ zFlyingThings3D.__getitem__r( c C r] r) )r r* r& r& r' r- r` zFlyingThings3D._read_flowra r& r& r$ r' r = s0 "
3 r c s e Zd ZdZdZddefdeeef dede e
de
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fd
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Z d ed e
ejejf fd
dZded eeef f fdd Z ZS )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.
TrE Nr rG r r r c
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hd1k_inputre r Zhd1k_challengez*10.pngz*11.pngzTCould not find the HD1K images. Please make sure the directory structure is correct.)
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HD1K.__init__r( c C r] r) rk r* r& r& r' r- r` zHD1K._read_flowr. c rX )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"``.
rZ r[ r$ r& r' r4 r\ zHD1K.__getitem__)r= r> r? rb r/ r r
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