tf.image.crop_and_resize
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Extracts crops from the input image tensor and resizes them.
tf.image.crop_and_resize(
image,
boxes,
box_indices,
crop_size,
method='bilinear',
extrapolation_value=0.0,
name=None
)
Extracts crops from the input image tensor and resizes them using bilinear
sampling or nearest neighbor sampling (possibly with aspect ratio change) to a
common output size specified by crop_size. This is more general than the
crop_to_bounding_box op which extracts a fixed size slice from the input
image and does not allow resizing or aspect ratio change. The crops occur
first and then the resize.
Returns a tensor with crops from the input image at positions defined at
the bounding box locations in boxes. The cropped boxes are all resized (with
bilinear or nearest neighbor interpolation) to a fixed
size = [crop_height, crop_width]. The result is a 4-D tensor
[num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.
In particular, if boxes = [[0, 0, 1, 1]], the method will give identical
results to using tf.compat.v1.image.resize_bilinear() or
tf.compat.v1.image.resize_nearest_neighbor()(depends on the method
argument) with
align_corners=True.
Args |
|---|
image
|
A 4-D tensor of shape [batch, image_height, image_width, depth].
Both image_height and image_width need to be positive.
|
boxes
|
A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor
specifies the coordinates of a box in the box_ind[i] image and is
specified in normalized coordinates [y1, x1, y2, x2]. A normalized
coordinate value of y is mapped to the image coordinate at y *
(image_height - 1), so as the [0, 1] interval of normalized image
height is mapped to [0, image_height - 1] in image height coordinates.
We do allow y1 > y2, in which case the sampled crop is an up-down
flipped version of the original image. The width dimension is treated
similarly. Normalized coordinates outside the [0, 1] range are allowed,
in which case we use extrapolation_value to extrapolate the input image
values.
|
box_indices
|
A 1-D tensor of shape [num_boxes] with int32 values in [0,
batch). The value of box_ind[i] specifies the image that the i-th box
refers to.
|
crop_size
|
A 1-D tensor of 2 elements, size = [crop_height, crop_width].
All cropped image patches are resized to this size. The aspect ratio of
the image content is not preserved. Both crop_height and crop_width
need to be positive.
|
method
|
An optional string specifying the sampling method for resizing. It
can be either "bilinear" or "nearest" and default to "bilinear".
Currently two sampling methods are supported: Bilinear and Nearest
Neighbor.
|
extrapolation_value
|
An optional float. Defaults to 0.0. Value used for
extrapolation, when applicable.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
|
Usage example:
BATCH_SIZE = 1
NUM_BOXES = 5
IMAGE_HEIGHT = 256
IMAGE_WIDTH = 256
CHANNELS = 3
CROP_SIZE = (24, 24)
image = tf.random.normal(shape=(
BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS) )
boxes = tf.random.uniform(shape=(NUM_BOXES, 4))
box_indices = tf.random.uniform(shape=(NUM_BOXES,), minval=0,
maxval=BATCH_SIZE, dtype=tf.int32)
output = tf.image.crop_and_resize(image, boxes, box_indices, CROP_SIZE)
output.shape
TensorShape([5, 24, 24, 3])
Example with linear interpolation:
image = np.arange(0, 18, 2).astype('float32').reshape(3, 3)
result = tf.image.crop_and_resize(
image[None, :, :, None],
np.asarray([[0.5,0.5,1,1]]), [0], [3, 3], method='bilinear')
result[0][:, :, 0]
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[ 8., 9., 10.],
[11., 12., 13.],
[14., 15., 16.]], dtype=float32)>
Example with nearest interpolation:
image = np.arange(0, 18, 2).astype('float32').reshape(3, 3)
result = tf.image.crop_and_resize(
image[None, :, :, None],
np.asarray([[0.5,0.5,1,1]]), [0], [3, 3], method='nearest')
result[0][:, :, 0]
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[ 8., 10., 10.],
[14., 16., 16.],
[14., 16., 16.]], dtype=float32)>
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.image.crop_and_resize\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/image_ops_impl.py#L4814-L4920) |\n\nExtracts crops from the input image tensor and resizes them. \n\n tf.image.crop_and_resize(\n image,\n boxes,\n box_indices,\n crop_size,\n method='bilinear',\n extrapolation_value=0.0,\n name=None\n )\n\nExtracts crops from the input image tensor and resizes them using bilinear\nsampling or nearest neighbor sampling (possibly with aspect ratio change) to a\ncommon output size specified by `crop_size`. This is more general than the\n`crop_to_bounding_box` op which extracts a fixed size slice from the input\nimage and does not allow resizing or aspect ratio change. The crops occur\nfirst and then the resize.\n\nReturns a tensor with `crops` from the input `image` at positions defined at\nthe bounding box locations in `boxes`. The cropped boxes are all resized (with\nbilinear or nearest neighbor interpolation) to a fixed\n`size = [crop_height, crop_width]`. The result is a 4-D tensor\n`[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned.\nIn particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical\nresults to using [`tf.compat.v1.image.resize_bilinear()`](../../tf/compat/v1/image/resize_bilinear) or\n[`tf.compat.v1.image.resize_nearest_neighbor()`](../../tf/compat/v1/image/resize_nearest_neighbor)(depends on the `method`\nargument) with\n`align_corners=True`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `image` | A 4-D tensor of shape `[batch, image_height, image_width, depth]`. Both `image_height` and `image_width` need to be positive. |\n| `boxes` | A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor specifies the coordinates of a box in the `box_ind[i]` image and is specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image height is mapped to `[0, image_height - 1]` in image height coordinates. We do allow `y1` \\\u003e `y2`, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the `[0, 1]` range are allowed, in which case we use `extrapolation_value` to extrapolate the input image values. |\n| `box_indices` | A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. The value of `box_ind[i]` specifies the image that the `i`-th box refers to. |\n| `crop_size` | A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved. Both `crop_height` and `crop_width` need to be positive. |\n| `method` | An optional string specifying the sampling method for resizing. It can be either `\"bilinear\"` or `\"nearest\"` and default to `\"bilinear\"`. Currently two sampling methods are supported: Bilinear and Nearest Neighbor. |\n| `extrapolation_value` | An optional `float`. Defaults to `0.0`. Value used for extrapolation, when applicable. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. ||\n\n\u003cbr /\u003e\n\n#### Usage example:\n\n BATCH_SIZE = 1\n NUM_BOXES = 5\n IMAGE_HEIGHT = 256\n IMAGE_WIDTH = 256\n CHANNELS = 3\n CROP_SIZE = (24, 24)\n\n image = tf.random.normal(shape=(\n BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS) )\n boxes = tf.random.uniform(shape=(NUM_BOXES, 4))\n box_indices = tf.random.uniform(shape=(NUM_BOXES,), minval=0,\n maxval=BATCH_SIZE, dtype=tf.int32)\n output = tf.image.crop_and_resize(image, boxes, box_indices, CROP_SIZE)\n output.shape\n TensorShape([5, 24, 24, 3])\n\nExample with linear interpolation: \n\n image = np.arange(0, 18, 2).astype('float32').reshape(3, 3)\n result = tf.image.crop_and_resize(\n image[None, :, :, None],\n np.asarray([[0.5,0.5,1,1]]), [0], [3, 3], method='bilinear')\n result[0][:, :, 0]\n \u003ctf.Tensor: shape=(3, 3), dtype=float32, numpy=\n array([[ 8., 9., 10.],\n [11., 12., 13.],\n [14., 15., 16.]], dtype=float32)\u003e\n\nExample with nearest interpolation: \n\n image = np.arange(0, 18, 2).astype('float32').reshape(3, 3)\n result = tf.image.crop_and_resize(\n image[None, :, :, None],\n np.asarray([[0.5,0.5,1,1]]), [0], [3, 3], method='nearest')\n result[0][:, :, 0]\n \u003ctf.Tensor: shape=(3, 3), dtype=float32, numpy=\n array([[ 8., 10., 10.],\n [14., 16., 16.],\n [14., 16., 16.]], dtype=float32)\u003e"]]
View source on GitHub