/TensorFlow 2.4

# tf.image.central_crop

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).

#### Usage Example:

```x = [[[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]],
[[13.0, 14.0, 15.0],
[16.0, 17.0, 18.0],
[19.0, 20.0, 21.0],
[22.0, 23.0, 24.0]],
[[25.0, 26.0, 27.0],
[28.0, 29.0, 30.0],
[31.0, 32.0, 33.0],
[34.0, 35.0, 36.0]],
[[37.0, 38.0, 39.0],
[40.0, 41.0, 42.0],
[43.0, 44.0, 45.0],
[46.0, 47.0, 48.0]]]
tf.image.central_crop(x, 0.5)
<tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
array([[[16., 17., 18.],
[19., 20., 21.]],
[[28., 29., 30.],
[31., 32., 33.]]], dtype=float32)>
```
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.