tfm.vision.preprocess_ops.resize_and_crop_image

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.

  1. 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.
  2. Pad the rescaled image to the padded_size.

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.

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.