package torch
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doc/torch.vision/Torch_vision/Image/index.html
Module Torch_vision.Image
Source
val load_image :
?resize:(Base.int * Base.int) ->
Base.string ->
Torch.Tensor.t Base.Or_error.t
load_image ?resize filename
returns a tensor containing the pixels for the image in filename
. Supported image formats are JPEG and PNG. The resulting tensor has dimensions NCHW (with N = 1) with values between 0 and 255. When resize
is set, the image is first resized preserving its original ratio then a center crop is taken.
load_images ?resize dir_name
is similar to applying load_image
to all the images in dir_name
. The resulting tensor has dimensions NCHW where N is the number of images.
val load_dataset :
dir:Base.string ->
classes:Base.string Base.list ->
with_cache:Base.string Base.option ->
resize:(Base.int * Base.int) ->
Torch.Dataset_helper.t
load_dataset ~dir ~classes ~with_cache ~resize
loads the images contained in directories dir/class
where class ranges over classes
. The class is used to determine the labels in the resulting dataset. resize
should be used if the images don't have all the same size.
write_image tensor ~filename
writes tensor
as an image to the disk. The format is determined by filename
's extension, defaulting to png. Supported formats are jpg
, tga
, bmp
, and png
. The tensor values should be between 0 and 255, the shape of the tensor can be 1; channels; height; width
or channels; height; width
where channels is either 1 or 3.
resize t ~height ~width
resizes the given tensor to height
and width
. This does not preserve the aspect ratio. t
can have dimensions NCHW with C set to 1 or 3, the returned tensor will have dimensions NCH'W' with H' = height
and W' = width
. The input and output tensors have values between 0 and 255.