tfm.vision.preprocess_ops.resize_and_crop_image

Resizes the input image to output size (RetinaNet style).

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.
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.