# tf.compat.v1.image.crop_and_resize

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/image_ops_impl.py#L4923-L4940) |

Extracts crops from the input image tensor and resizes them.

    tf.compat.v1.image.crop_and_resize(
        image,
        boxes,
        box_ind=None,
        crop_size=None,
        method='bilinear',
        extrapolation_value=0,
        name=None,
        box_indices=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.

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.image.resize_bilinear()` or
`tf.image.resize_nearest_neighbor()`(depends on the `method` argument) with
`align_corners=True`.

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| ## Args ||
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| `image` | A `Tensor`. Must be one of the following types: `uint8`, `uint16`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`. A 4-D tensor of shape `[batch, image_height, image_width, depth]`. Both `image_height` and `image_width` need to be positive. |
| `boxes` | A `Tensor` of type `float32`. 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_ind` | A `Tensor` of type `int32`. 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 `Tensor` of type `int32`. 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` from: `"bilinear", "nearest"`. Defaults to `"bilinear"`. A 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`. Value used for extrapolation, when applicable. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
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| A `Tensor` of type `float32`. ||

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