def test_exclude_labels_on_edges_blobs():
import pyclesperanto_prototype as cle
from skimage.io import imread, imsave
# initialize GPU
cle.select_device("GTX")
print("Used GPU: " + cle.get_device().name)
# load data
root = Path(cle.__file__).parent
filename = str(root / '..' / 'data' / 'blobs.tif')
image = imread(filename)
print("Loaded image size: " + str(image.shape))
# push image to GPU memory
input = cle.push(image)
print("Image size in GPU: " + str(input.shape))
# process the image
blurred = cle.gaussian_blur(image, sigma_x=1, sigma_y=1)
binary = cle.threshold_otsu(blurred)
labeled = cle.connected_components_labeling_box(binary)
wo_edges = cle.exclude_labels_on_edges(labeled)
# The maxmium intensity in a label image corresponds to the number of objects
num_labels = cle.maximum_of_all_pixels(wo_edges)
# print out result
print("Num objects in the image: " + str(num_labels))
assert num_labels == 45
def test_connected_components_labeling_box_blobs():
import pyclesperanto_prototype as cle
from skimage.io import imread, imsave
# initialize GPU
cle.select_device("GTX")
print("Used GPU: " + cle.get_device().name)
# load data
image = imread('https://imagej.nih.gov/ij/images/blobs.gif')
print("Loaded image size: " + str(image.shape))
# push image to GPU memory
input = cle.push(image)
print("Image size in GPU: " + str(input.shape))
# process the image
inverted = cle.subtract_image_from_scalar(image, scalar=255)
blurred = cle.gaussian_blur(inverted, sigma_x=1, sigma_y=1)
binary = cle.threshold_otsu(blurred)
labeled = cle.connected_components_labeling_box(binary)
# The maxmium intensity in a label image corresponds to the number of objects
num_labels = cle.maximum_of_all_pixels(labeled)
# print out result
print("Num objects in the image: " + str(num_labels))
assert num_labels == 63
def test_average_distance_of_n_nearest_other_labels_map():
cle.select_device("gfx")
labels = cle.push(
np.asarray([[1, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0,
2]]))
other_labels = cle.push(
np.asarray([[0, 2, 0, 0, 0, 3], [1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 5,
0]]))
reference = cle.push(
np.asarray([[1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0,
1]]))
average_distance_to_n_nearest_other_labels_map = cle.average_distance_to_n_nearest_other_labels_map(
labels, other_labels, n=1)
a = cle.pull(average_distance_to_n_nearest_other_labels_map)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.01))
def test_gpu_switch():
image = np.random.random((100, 100))
# select NVidia
print(cle.select_device("RTX"))
cle.gaussian_blur(image, sigma_x=1, sigma_y=1)
# select AMD
print(cle.select_device("gfx"))
cle.gaussian_blur(image, sigma_x=1, sigma_y=1)
# select Intel
print(cle.select_device("Intel"))
cle.gaussian_blur(image, sigma_x=1, sigma_y=1)
def main():
import napari
from skimage.io import imread
import pyclesperanto_prototype as cle
from napari_pyclesperanto_assistant._gui._Assistant import Assistant
import sys
if len(sys.argv) > 1:
filename = str(sys.argv[1])
image = imread(filename)
else:
# make some artificial cell image
filename = "undefined.tif"
labels = cle.artificial_tissue_2d(width=512,
height=512,
delta_x=48,
delta_y=32,
random_sigma_x=6,
random_sigma_y=6)
membranes = cle.detect_label_edges(labels)
eroded = cle.maximum_sphere(membranes, radius_x=3, radius_y=3)
blurred = cle.gaussian_blur(eroded, sigma_x=3, sigma_y=3)
image = cle.pull_zyx(blurred)
#image = imread('https://samples.fiji.sc/blobs.png')
#image = imread('C:/structure/data/lund_000500_resampled.tif')
#filename = 'data/Lund_000500_resampled-cropped.tif'
#filename = str(Path(__file__).parent) + '/data/CalibZAPWfixed_000154_max-16.tif'
print("Available GPUs: " + str(cle.available_device_names()))
cle.select_device("rtx")
print("Used GPU: " + str(cle.get_device()))
with napari.gui_qt():
# create a viewer and add some image
viewer = napari.Viewer()
layer = viewer.add_image(image, metadata={'filename': filename})
from napari_pyclesperanto_assistant import napari_plugin
napari_plugin(viewer)
dock_widget = table_to_widget(table)
viewer.window.add_dock_widget(dock_widget, area='right')
def table_to_widget(table: dict) -> QTableWidget:
view = QTableWidget(len(next(iter(table.values()))), len(table))
for i, column in enumerate(table.keys()):
view.setItem(0, i, QTableWidgetItem(column))
for j, value in enumerate(table.get(column)):
view.setItem(j + 1, i, QTableWidgetItem(str(value)))
return view
# -----------------------------------------------------------------------------
from skimage.io import imread
image = imread('https://samples.fiji.sc/blobs.png')
#image = imread('C:/structure/data/lund_000500_resampled.tif')
cle.select_device("RTX")
print(cle.get_device())
with napari.gui_qt():
# create a viewer and add some image
viewer = napari.Viewer()
viewer.add_image(image)
# add the gui to the viewer as a dock widget
dock_widget = viewer.window.add_dock_widget(Gui(viewer), area='right')
import pyclesperanto_prototype as cle
# list names of all available OpenCL-devices
print("Available OpenCL devices:" + str(cle.available_device_names()))
# list CPUs and GPUs separately
gpu_devices = cle.available_device_names(dev_type="gpu")
print("Available GPU OpenCL devices:" + str(gpu_devices))
cpu_devices = cle.available_device_names(dev_type="cpu")
print("Available CPU OpenCL devices:" + str(cpu_devices))
# selecting an Nvidia RTX
cle.select_device("RTX")
print("Using OpenCL device " + cle.get_device().name)
# selecting an Nvidia GTX
cle.select_device("GTX")
print("Using OpenCL device " + cle.get_device().name)
# selecting an Intel UHD GPU
cle.select_device("Intel")
print("Using OpenCL device " + cle.get_device().name)
# selecting an AMD Vega GPU
cle.select_device("904")
print("Using OpenCL device " + cle.get_device().name)
# selecting an AMD Vega GPU
cle.select_device("902")
print("Using OpenCL device " + cle.get_device().name)
import pyclesperanto_prototype as cle
import numpy as np
import time
cle.select_device('RTX')
cle.set_wait_for_kernel_finish(True)
# config
num_iterations = 10
num_tests = 10
# initialize GPU
print("Used GPU: " + cle.get_device().name)
# generate data; 50 MB
image = np.random.random([50, 1024, 1024])
print("Image size: " + str(image.shape))
# push image to GPU memory
flip = cle.push_zyx(image)
flop = cle.create_like(flip)
for j in range(0, num_tests):
start = time.time()
for i in range(0, num_iterations):
cle.maximum_sphere(flip, flop, 3, 3, 0)
cle.minimum_sphere(flop, flip, 3, 3, 0)
end = time.time()
import pyclesperanto_prototype as cle
from skimage.io import imread, imsave
# initialize GPU
cle.select_device("GTX")
print("Used GPU: " + cle.get_device().name)
# load data
image = imread('https://imagej.nih.gov/ij/images/blobs.gif')
print("Loaded image size: " + str(image.shape))
# push image to GPU memory
input = cle.push(image)
print("Image size in GPU: " + str(input.shape))
# process the image
inverted = cle.subtract_image_from_scalar(image, scalar=255)
blurred = cle.gaussian_blur(inverted, sigma_x=1, sigma_y=1)
binary = cle.threshold_otsu(blurred)
labeled = cle.connected_components_labeling_box(binary)
# The maxmium intensity in a label image corresponds to the number of objects
num_labels = cle.maximum_of_all_pixels(labeled)
# print out result
print("Num objects in the image: " + str(num_labels))
# for debugging: print out image
print(labeled)
# Tribolium castaneum morphometry workflow
# See also: https://clij.github.io/clij2-docs/md/tribolium_morphometry/
# Thanks to Pradeep Rajasekhar (Monash University) and Daniela Vorkel (MPI CBG Dresden)
# for code snippets and data :-)
import pyclesperanto_prototype as cle
# we need to select a powerful GPU for this
cle.select_device()
# check which GPU we use
print(cle.get_device().name)
# load data
from skimage.io import imread
image = imread('/home2/kdean/Desktop/lund1051_resampled.tif')
# The dataset is available online:
# https://git.mpi-cbg.de/rhaase/clij2_example_data/blob/master/lund1051_resampled.tif
print(image.shape)
# The dataset shows a Tribolium castaneum embryo, imaged by a custom light sheet microscope,
# at a wavelength of 488nm (Imaging credits: Daniela Vorkel, Myers lab, MPI CBG).
# The data set has be resample to a voxel size of
voxel_size = [1, 1, 1] # microns
# The embryo expresses nuclei-GFP. We will use the dataset to detect nuclei and to generate
# an estimated cell-segmentation.
# Define an interactive user interface using magicgui
from magicgui import magicgui
from napari.layers import Image, Labels