R/transforms-generics.R
transform_random_resized_crop.Rd
Crop the given image to a random size and aspect ratio. The image can be a
Magick Image or a Tensor, in which case it is expected to have
[..., H, W]
shape, where ... means an arbitrary number of leading
dimensions
A magick-image
, array
or torch_tensor
.
(sequence or int): Desired output size. If size is a sequence like (h, w), output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).
(tuple of float): range of size of the origin size cropped
(tuple of float): range of aspect ratio of the origin aspect ratio cropped.
(int, optional) Desired interpolation. An integer
0 = nearest
, 2 = bilinear
, and 3 = bicubic
or a name from
magick::filter_types()
.
A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop is finally resized to given size. This is popularly used to train the Inception networks.
Other transforms:
transform_adjust_brightness()
,
transform_adjust_contrast()
,
transform_adjust_gamma()
,
transform_adjust_hue()
,
transform_adjust_saturation()
,
transform_affine()
,
transform_center_crop()
,
transform_color_jitter()
,
transform_convert_image_dtype()
,
transform_crop()
,
transform_five_crop()
,
transform_grayscale()
,
transform_hflip()
,
transform_linear_transformation()
,
transform_normalize()
,
transform_pad()
,
transform_perspective()
,
transform_random_affine()
,
transform_random_apply()
,
transform_random_choice()
,
transform_random_crop()
,
transform_random_erasing()
,
transform_random_grayscale()
,
transform_random_horizontal_flip()
,
transform_random_order()
,
transform_random_perspective()
,
transform_random_rotation()
,
transform_random_vertical_flip()
,
transform_resize()
,
transform_resized_crop()
,
transform_rgb_to_grayscale()
,
transform_rotate()
,
transform_ten_crop()
,
transform_to_tensor()
,
transform_vflip()