tfm.vision.preprocess_ops.resize_and_crop_image
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Resizes the input image to output size (RetinaNet style).
tfm.vision.preprocess_ops.resize_and_crop_image(
image,
desired_size,
padded_size,
aug_scale_min=1.0,
aug_scale_max=1.0,
seed=1,
method=tf.image.ResizeMethod.BILINEAR
)
Resize and pad images given the desired output size of the image and
stride size.
Here are the preprocessing steps.
- For a given image, keep its aspect ratio and rescale the image to make it
the largest rectangle to be bounded by the rectangle specified by the
desired_size
.
- Pad the rescaled image to the padded_size.
Args |
image
|
a Tensor of shape [height, width, 3] representing an image.
|
desired_size
|
a Tensor or int list/tuple of two elements representing
[height, width] of the desired actual output image size.
|
padded_size
|
a Tensor or int list/tuple of two elements representing
[height, width] of the padded output image size. Padding will be applied
after scaling the image to the desired_size. Can be None to disable
padding.
|
aug_scale_min
|
a float with range between [0, 1.0] representing minimum
random scale applied to desired_size for training scale jittering.
|
aug_scale_max
|
a float with range between [1.0, inf] representing maximum
random scale applied to desired_size for training scale jittering.
|
seed
|
seed for random scale jittering.
|
method
|
function to resize input image to scaled image.
|
Returns |
output_image
|
Tensor of shape [height, width, 3] where [height, width]
equals to output_size .
|
image_info
|
a 2D Tensor that encodes the information of the image and the
applied preprocessing. It is in the format of
[[original_height, original_width], [desired_height, desired_width],
[y_scale, x_scale], [y_offset, x_offset]], where [desired_height,
desired_width] is the actual scaled image size, and [y_scale, x_scale] is
the scaling factor, which is the ratio of
scaled dimension / original dimension.
|
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Last updated 2024-02-02 UTC.
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