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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 4D 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
.
Returns  

A 4D 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)>