tf.image.central_crop
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Crop the central region of the image(s).
tf.image.central_crop(
image, central_fraction
)
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
|
Usage Example: python >> import tensorflow as tf >> x =
tf.random.normal(shape=(256, 256, 3)) >> tf.image.central_crop(x, 0.5)
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 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.image.central_crop\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/image/central_crop) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/image_ops_impl.py#L617-L723) |\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`](/api_docs/python/tf/image/central_crop), \\`tf.compat.v2.image.central_crop\\`\n\n\u003cbr /\u003e\n\n tf.image.central_crop(\n image, central_fraction\n )\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. \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\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\nUsage Example: `python \u003e\u003e import tensorflow as tf \u003e\u003e x =\ntf.random.normal(shape=(256, 256, 3)) \u003e\u003e tf.image.central_crop(x, 0.5)`\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"]]
TensorFlow 2 version
View source on GitHub