tf.image.crop_and_resize

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.

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).

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(&#x27;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=&#x27;nearest')
result[0][:, :, 0]
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
  array([[ 8., 10., 10.],
         [14., 16., 16.],
         [14., 16., 16.]], dtype=float32)>