def z_position_projection(source_stack: Image,
z_position: Image,
destination: Image = None) -> Image:
"""Project a defined Z-slice of a 3D stack into a 2D image.
Which Z-slice is defined as the z_position image, which represents an altitude map.
Parameters
----------
source_stack : Image
Input image stack
z_position : Image
altitude map
destination : Image, optional
Projected image
Returns
-------
destination
See Also
--------
..[1] https://clij.github.io/clij2-docs/reference_zPositionProjection
"""
parameters = {
"dst": destination,
"position": z_position,
"src": source_stack,
}
execute(__file__, 'z_position_projection_x.cl', 'z_position_projection',
destination.shape, parameters)
return destination
def z_position_of_minimum_z_projection(source: Image, destination: Image = None) -> Image:
"""Determines a Z-position of the minimum intensity along Z and writes it into the resulting image.
If there are multiple z-slices with the same value, the smallest Z will be chosen.
Parameters
----------
source : Image
Input image stack
destination : Image, optional
altitude map
Returns
-------
destination
See Also
--------
..[1] https://clij.github.io/clij2-docs/reference_zPositionOfMinimumZProjection
"""
parameters = {
"dst_arg": destination,
"src": source,
}
execute(__file__, 'z_position_of_minimum_z_projection_x.cl', 'z_position_of_minimum_z_projection',
destination.shape, parameters)
return destination
def variance_box(source: Image,
destination: Image = None,
radius_x: int = 1,
radius_y: int = 1,
radius_z: int = 1) -> Image:
"""Computes the local variance of a pixels box neighborhood. \n\nThe box size is specified by
its half-width, half-height and half-depth (radius). If 2D images are given, radius_z will be ignored.
Parameters
----------
source : Image
destination : Image
radius_x : int
radius_y : int
radius_z : int
Returns
-------
destination
Examples
--------
>>> import pyclesperanto_prototype as cle
>>> cle.variance_box(source, destination, 10, 10, 10)
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_varianceBox
"""
parameters = {
"dst": destination,
"src": source,
"Nx": radius_to_kernel_size(radius_x),
"Ny": radius_to_kernel_size(radius_y),
}
if (len(destination.shape) == 3):
parameters.update({"Nz": radius_to_kernel_size(radius_z)})
execute(__file__, 'variance_box_' + str(len(destination.shape)) + 'd_x.cl',
'variance_box_' + str(len(destination.shape)) + 'd',
destination.shape, parameters)
return destination
def exclude_labels_on_edges(labels_source: Image,
labels_destination: Image = None):
num_labels = int(maximum_of_all_pixels(labels_source))
label_indices = range(0, num_labels + 1)
label_index_map = push(np.asarray(label_indices))
print(label_index_map)
parameters = {"src": labels_source, "label_index_dst": label_index_map}
if (len(labels_source.shape) == 3):
dimensions = [
labels_source.shape[0], labels_source.shape[1],
labels_source.shape[2]
]
else:
dimensions = [1, labels_source.shape[0], labels_source.shape[1]]
if (len(labels_source.shape) == 3):
global_sizes = [1, dimensions[1], dimensions[2]]
execute(__file__, "exclude_labels_on_edges_3d_x.cl",
"exclude_on_edges_z_3d", global_sizes, parameters)
global_sizes = [dimensions[0], 1, dimensions[2]]
execute(__file__, "exclude_labels_on_edges_3d_x.cl",
"exclude_on_edges_y_3d", global_sizes, parameters)
global_sizes = [dimensions[0], dimensions[1], 1]
execute(__file__, "exclude_labels_on_edges_3d_x.cl",
"exclude_on_edges_x_3d", global_sizes, parameters)
label_indices = pull(label_index_map)
count = 1
for i in range(1, num_labels + 1):
if (label_indices[i] > 0):
label_indices[i] = count
count = count + 1
label_index_map = push(np.asarray(label_indices))
print(label_index_map)
replace_intensities(labels_source, label_index_map, labels_destination)
return labels_destination
def exclude_labels_on_edges(label_map_input : Image, label_map_destination : Image = None) -> Image:
"""Removes all labels from a label map which touch the edges of the image
(in X, Y and Z if the image is 3D).
Remaining label elements are renumbered afterwards.
Parameters
----------
label_map_input : Image
label_map_destination : Image
Returns
-------
label_map_destination
Examples
--------
>>> import pyclesperanto_prototype as cle
>>> cle.exclude_labels_on_edges(label_map_input, label_map_destination)
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_excludeLabelsOnEdges
"""
num_labels = int(maximum_of_all_pixels(label_map_input))
label_indices = range(0, num_labels + 1)
label_index_map = push(np.asarray(label_indices))
parameters = {
"src":label_map_input,
"label_index_dst":label_index_map
}
if (len(label_map_input.shape) == 3):
dimensions = [
label_map_input.shape[0],
label_map_input.shape[1],
label_map_input.shape[2]
]
else:
dimensions = [
1,
label_map_input.shape[0],
label_map_input.shape[1]
]
if (len(label_map_input.shape) == 3):
global_sizes = [1, dimensions[1], dimensions[2]]
execute(__file__, "../clij-opencl-kernels/kernels/exclude_labels_on_edges_3d_x.cl", "exclude_on_edges_z_3d", global_sizes, parameters)
global_sizes = [dimensions[0], 1 ,dimensions[2]]
execute(__file__, "../clij-opencl-kernels/kernels/exclude_labels_on_edges_3d_x.cl", "exclude_on_edges_y_3d", global_sizes, parameters)
global_sizes = [dimensions[0], dimensions[1], 1]
execute(__file__, "../clij-opencl-kernels/kernels/exclude_labels_on_edges_3d_x.cl", "exclude_on_edges_x_3d", global_sizes, parameters)
label_indices = pull(label_index_map)
count = 1
for i in range(1, num_labels + 1):
if (label_indices[i] > 0):
label_indices[i] = count
count = count + 1
label_index_map = push(np.asarray(label_indices))
replace_intensities(label_map_input, label_index_map, label_map_destination)
return label_map_destination