def connected_components(images):
"""Labels the connected components in a batch of images.
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 True 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 boolean image(s).
Returns:
Components with the same shape as `images`. False entries in `images` have
value 0, and all True entries map to a component id > 0.
Raises:
TypeError: if `images` is not 2D or 3D.
"""
with ops.name_scope("connected_components"):
image_or_images = ops.convert_to_tensor(images, name="images")
if len(image_or_images.get_shape()) == 2:
images = image_or_images[None, :, :]
elif len(image_or_images.get_shape()) == 3:
images = image_or_images
else:
raise TypeError(
"images should have rank 2 (HW) or 3 (NHW). Static shape is %s" %
image_or_images.get_shape())
components = gen_image_ops.image_connected_components(images)
# TODO(ringwalt): Component id renaming should be done in the op, to avoid
# constructing multiple additional large tensors.
components_flat = array_ops.reshape(components, [-1])
unique_ids, id_index = array_ops.unique(components_flat)
id_is_zero = array_ops.where(math_ops.equal(unique_ids, 0))[:, 0]
# Map each nonzero id to consecutive values.
nonzero_consecutive_ids = math_ops.range(
array_ops.shape(unique_ids)[0] - array_ops.shape(id_is_zero)[0]) + 1
def no_zero():
# No need to insert a zero into the ids.
return nonzero_consecutive_ids
def has_zero():
# Insert a zero in the consecutive ids where zero appears in unique_ids.
# id_is_zero has length 1.
zero_id_ind = math_ops.to_int32(id_is_zero[0])
ids_before = nonzero_consecutive_ids[:zero_id_ind]
ids_after = nonzero_consecutive_ids[zero_id_ind:]
return array_ops.concat([ids_before, [0], ids_after], axis=0)
new_ids = control_flow_ops.cond(
math_ops.equal(array_ops.shape(id_is_zero)[0], 0), no_zero, has_zero)
components = array_ops.reshape(
array_ops.gather(new_ids, id_index), array_ops.shape(components))
if len(image_or_images.get_shape()) == 2:
return components[0, :, :]
else:
return components
def connected_components(images):
"""Labels the connected components in a batch of images.
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 True 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 boolean image(s).
Returns:
Components with the same shape as `images`. False entries in `images` have
value 0, and all True entries map to a component id > 0.
Raises:
TypeError: if `images` is not 2D or 3D.
"""
with ops.name_scope("connected_components"):
image_or_images = ops.convert_to_tensor(images, name="images")
if len(image_or_images.get_shape()) == 2:
images = image_or_images[None, :, :]
elif len(image_or_images.get_shape()) == 3:
images = image_or_images
else:
raise TypeError(
"images should have rank 2 (HW) or 3 (NHW). Static shape is %s" %
image_or_images.get_shape())
components = gen_image_ops.image_connected_components(images)
# TODO(ringwalt): Component id renaming should be done in the op, to avoid
# constructing multiple additional large tensors.
components_flat = array_ops.reshape(components, [-1])
unique_ids, id_index = array_ops.unique(components_flat)
id_is_zero = array_ops.where(math_ops.equal(unique_ids, 0))[:, 0]
# Map each nonzero id to consecutive values.
nonzero_consecutive_ids = math_ops.range(
array_ops.shape(unique_ids)[0] - array_ops.shape(id_is_zero)[0]) + 1
def no_zero():
# No need to insert a zero into the ids.
return nonzero_consecutive_ids
def has_zero():
# Insert a zero in the consecutive ids where zero appears in unique_ids.
# id_is_zero has length 1.
zero_id_ind = math_ops.to_int32(id_is_zero[0])
ids_before = nonzero_consecutive_ids[:zero_id_ind]
ids_after = nonzero_consecutive_ids[zero_id_ind:]
return array_ops.concat([ids_before, [0], ids_after], axis=0)
new_ids = control_flow_ops.cond(
math_ops.equal(array_ops.shape(id_is_zero)[0], 0), no_zero, has_zero)
components = array_ops.reshape(
array_ops.gather(new_ids, id_index), array_ops.shape(components))
if len(image_or_images.get_shape()) == 2:
return components[0, :, :]
else:
return components