Labels the connected components in a batch of images.
@tf.function
tfa.image.connected_components(
images: tfa.types.TensorLike,
name: Optional[Text] = None
) -> tf.Tensor
A component is a set of pixels in a single input image, which are
all adjacent and all have the same non-zero value. The components
using a squared connectivity of one (all equal entries are joined with
their neighbors above,below, left, and right). Components across all
images have consecutive ids 1 through n.
Components are labeled according to the first pixel of the
component appearing in row-major order (lexicographic order by
image_index_in_batch, row, col).
Zero entries all have an output id of 0.
This op is equivalent with scipy.ndimage.measurements.label
on a 2D array with the default structuring element
(which is the connectivity used here).
Args |
|---|
images
|
A 2D (H, W) or 3D (N, H, W) Tensor of image (integer,
floating point and boolean types are supported).
|
name
|
The name of the op.
|
Returns |
|---|
Components with the same shape as images.
entries that evaluate to False (e.g. 0/0.0f, False) in images have
value 0, and all other entries map to a component id > 0.
|
Raises |
|---|
TypeError
|
if images is not 2D or 3D.
|
View source on GitHub