Randomly crop the image and boxes, filtering labels.
tfm.vision.preprocess_ops.random_crop(
image,
boxes,
labels,
min_scale=0.3,
aspect_ratio_range=(0.5, 2.0),
min_overlap_params=(0.0, 1.4, 0.2, 0.1),
max_retry=50,
seed=None
)
Args |
image
|
a 'Tensor' of shape [height, width, 3] representing the input image.
|
boxes
|
a 'Tensor' of shape [N, 4] representing the ground-truth bounding
boxes with (ymin, xmin, ymax, xmax).
|
labels
|
a 'Tensor' of shape [N,] representing the class labels of the boxes.
|
min_scale
|
a 'float' in [0.0, 1.0) indicating the lower bound of the random
scale variable.
|
aspect_ratio_range
|
a list of two 'float' that specifies the lower and upper
bound of the random aspect ratio.
|
min_overlap_params
|
a list of four 'float' representing the min value, max
value, step size, and offset for the minimum overlap sample.
|
max_retry
|
an 'int' representing the number of trials for cropping. If it is
exhausted, no cropping will be performed.
|
seed
|
the random number seed of int, but could be None.
|
Returns |
image
|
a Tensor representing the random cropped image. Can be the
original image if max_retry is exhausted.
|
boxes
|
a Tensor representing the bounding boxes in the cropped image.
|
labels
|
a Tensor representing the new bounding boxes' labels.
|