Help protect the Great Barrier Reef with TensorFlow on Kaggle

Crops and/or pads an image to a target width and height.

### Used in the notebooks

Used in the tutorials

Resizes an image to a target width and height by either centrally cropping the image or padding it evenly with zeros.

If `width` or `height` is greater than the specified `target_width` or `target_height` respectively, this op centrally crops along that dimension.

#### For example:

````image = np.arange(75).reshape(5, 5, 3)  # create 3-D image input`
`image[:,:,0]  # print first channel just for demo purposes`
`array([[ 0,  3,  6,  9, 12],`
`       [15, 18, 21, 24, 27],`
`       [30, 33, 36, 39, 42],`
`       [45, 48, 51, 54, 57],`
`       [60, 63, 66, 69, 72]])`
`image = tf.image.resize_with_crop_or_pad(image, 3, 3)  # crop`
`# print first channel for demo purposes; centrally cropped output`
`image[:,:,0]`
`<tf.Tensor: shape=(3, 3), dtype=int64, numpy=`
`array([[18, 21, 24],`
`       [33, 36, 39],`
`       [48, 51, 54]])>`
```

If `width` or `height` is smaller than the specified `target_width` or `target_height` respectively, this op centrally pads with 0 along that dimension.

#### For example:

````image = np.arange(1, 28).reshape(3, 3, 3)  # create 3-D image input`
`image[:,:,0]  # print first channel just for demo purposes`
`array([[ 1,  4,  7],`
`       [10, 13, 16],`
`       [19, 22, 25]])`
`image = tf.image.resize_with_crop_or_pad(image, 5, 5)  # pad`
`# print first channel for demo purposes; we should see 0 paddings`
`image[:,:,0]`
`<tf.Tensor: shape=(5, 5), dtype=int64, numpy=`
`array([[ 0,  0,  0,  0,  0],`
`       [ 0,  1,  4,  7,  0],`
`       [ 0, 10, 13, 16,  0],`
`       [ 0, 19, 22, 25,  0],`
`       [ 0,  0,  0,  0,  0]])>`
```

`image` 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor of shape `[height, width, channels]`.
`target_height` Target height.
`target_width` Target width.

`ValueError` if `target_height` or `target_width` are zero or negative.

Cropped and/or padded image. If `images` was 4-D, a 4-D float Tensor of shape `[batch, new_height, new_width, channels]`. If `images` was 3-D, a 3-D float Tensor of shape `[new_height, new_width, channels]`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]