tf.image.central_crop
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Crop the central region of the image(s).
tf.image.central_crop(
image, central_fraction
)
Used in the notebooks
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. The larger the value
of central_fraction, the larger the dimension of the region to be cropped
and retained.
--------
| |
| 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.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.image.central_crop\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/image_ops_impl.py#L858-L1002) |\n\nCrop the central region of the image(s).\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.image.central_crop`](https://www.tensorflow.org/api_docs/python/tf/image/central_crop)\n\n\u003cbr /\u003e\n\n tf.image.central_crop(\n image, central_fraction\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|--------------------------------------------------------------------------------------|\n| - [Data augmentation](https://www.tensorflow.org/tutorials/images/data_augmentation) |\n\nRemove the outer parts of an image but retain the central region of the image\nalong each dimension. If we specify `central_fraction = 0.5`, this function\nreturns the region marked with \"X\" in the below diagram. The larger the value\nof `central_fraction`, the larger the dimension of the region to be cropped\nand retained. \n\n --------\n | |\n | XXXX |\n | XXXX |\n | | where \"X\" is the central 50% of the image.\n --------\n\nThis function works on either a single image (`image` is a 3-D Tensor), or a\nbatch of images (`image` is a 4-D Tensor).\n\n#### Usage Example:\n\n x = [[[1.0, 2.0, 3.0],\n [4.0, 5.0, 6.0],\n [7.0, 8.0, 9.0],\n [10.0, 11.0, 12.0]],\n [[13.0, 14.0, 15.0],\n [16.0, 17.0, 18.0],\n [19.0, 20.0, 21.0],\n [22.0, 23.0, 24.0]],\n [[25.0, 26.0, 27.0],\n [28.0, 29.0, 30.0],\n [31.0, 32.0, 33.0],\n [34.0, 35.0, 36.0]],\n [[37.0, 38.0, 39.0],\n [40.0, 41.0, 42.0],\n [43.0, 44.0, 45.0],\n [46.0, 47.0, 48.0]]]\n tf.image.central_crop(x, 0.5)\n \u003ctf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=\n array([[[16., 17., 18.],\n [19., 20., 21.]],\n [[28., 29., 30.],\n [31., 32., 33.]]], dtype=float32)\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------------|-----------------------------------------------------------------------------------------------------------------------------|\n| `image` | Either a 3-D float Tensor of shape \\[height, width, depth\\], or a 4-D Tensor of shape \\[batch_size, height, width, depth\\]. |\n| `central_fraction` | float (0, 1\\], fraction of size to crop |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|-------------------------------------------------|\n| `ValueError` | if central_crop_fraction is not within (0, 1\\]. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| 3-D / 4-D float Tensor, as per the input. ||\n\n\u003cbr /\u003e"]]
View source on GitHub