tf.image.crop_and_resize

Extracts crops from the input image tensor and resizes them.

View aliases

Compat aliases for migration

tf.compat.v2.image.crop_and_resize

tf.image.crop_and_resize(
    image, boxes, box_indices, crop_size, method='bilinear', extrapolation_value=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.

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. 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].

Example:

import tensorflow as tf
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  #=> (5, 24, 24, 3)