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# tf.image.central_crop

Crop the central region of the image(s).

``````tf.image.central_crop(
image, central_fraction
)
``````

### Used in the notebooks

Used in the tutorials

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.