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| resize_images(images, size, method=0, align_corners=False, preserve_aspect_ratio=False)
Resize `images` to `size` using the specified `method`.
Resized images will be distorted if their original aspect ratio is not
the same as `size`. To avoid distortions see
@{tf.image.resize_image_with_pad}.
`method` can be one of:
* <b>`ResizeMethod.BILINEAR`</b>: [Bilinear interpolation.](
https://en.wikipedia.org/wiki/Bilinear_interpolation)
* <b>`ResizeMethod.NEAREST_NEIGHBOR`</b>: [Nearest neighbor interpolation.](
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* <b>`ResizeMethod.BICUBIC`</b>: [Bicubic interpolation.](
https://en.wikipedia.org/wiki/Bicubic_interpolation)
* <b>`ResizeMethod.AREA`</b>: Area interpolation.
The return value has the same type as `images` if `method` is
`ResizeMethod.NEAREST_NEIGHBOR`. It will also have the same type as `images`
if the size of `images` can be statically determined to be the same as `size`,
because `images` is returned in this case. Otherwise, the return value has
type `float32`.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
method: ResizeMethod. Defaults to `ResizeMethod.BILINEAR`.
align_corners: bool. If True, the centers of the 4 corner pixels of the
input and output tensors are aligned, preserving the values at the
corner pixels. Defaults to `False`.
preserve_aspect_ratio: Whether to preserve the aspect ratio. If this is set,
then `images` will be resized to a size that fits in `size` while
preserving the aspect ratio of the original image. Scales up the image if
`size` is bigger than the current size of the `image`. Defaults to False.
Raises:
ValueError: if the shape of `images` is incompatible with the
shape arguments to this function
ValueError: if `size` has invalid shape or type.
ValueError: if an unsupported resize method is specified.
Returns:
If `images` was 4-D, a 4-D float Tensor of shape
`[batch, new_height, new_width, channels]`.
If `images` was 3-D, a 3-D float Tensor of shape
`[new_height, new_width, channels]`.
resize_image_with_crop_or_pad(image, target_height, target_width)
Crops and/or pads an image to a target width and height.
Resizes an image to a target width and height by either centrally
cropping the image or padding it evenly with zeros.
If `width` or `height` is greater than the specified `target_width` or
`target_height` respectively, this op centrally crops along that dimension.
If `width` or `height` is smaller than the specified `target_width` or
`target_height` respectively, this op centrally pads with 0 along that
dimension.
Args:
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
target_height: Target height.
target_width: Target width.
Raises:
ValueError: if `target_height` or `target_width` are zero or negative.
Returns:
Cropped and/or padded image.
If `images` was 4-D, a 4-D float Tensor of shape
`[batch, new_height, new_width, channels]`.
If `images` was 3-D, a 3-D float Tensor of shape
`[new_height, new_width, channels]`.
central_crop(image, central_fraction)
Crop the central region of the image(s).
Remove the outer parts of an image but retain the central region of the image
along each dimension. If we specify central_fraction = 0.5, this function
returns the region marked with "X" in the below diagram.
--------
| |
| XXXX |
| XXXX |
| | where "X" is the central 50% of the image.
--------
This function works on either a single image (`image` is a 3-D Tensor), or a
batch of images (`image` is a 4-D Tensor).
Args:
image: Either a 3-D float Tensor of shape [height, width, depth], or a 4-D
Tensor of shape [batch_size, height, width, depth].
central_fraction: float (0, 1], fraction of size to crop
Raises:
ValueError: if central_crop_fraction is not within (0, 1].
Returns:
3-D / 4-D float Tensor, as per the input.
|