def test_transpose_xy():
test1 = cle.push(np.asarray([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]))
reference = cle.push(np.asarray([[[0, 1], [4, 5]], [[2, 3], [6, 7]]]))
result = cle.create(test1)
cle.transpose_xy(test1, result)
a = cle.pull(result)
b = cle.pull(reference)
print(a)
assert (np.array_equal(a, b))
def test_transpose_xy_3d_generate_output():
test1 = cle.push(np.asarray([[[1, 2, 6], [3, 4, 5]]]))
result = cle.transpose_xy(test1)
a = cle.pull(result)
assert (np.min(a) == 1)
assert (np.max(a) == 6)
assert (np.mean(a) == 3.5)
def _push_and_format(self, picture):
"""
Pushs a given camera picture to GPU memory and brings it in the right shape/dimension
Parameters
----------
picture : numpy array
camera picture
Returns
-------
cle.Image
"""
result = cle.flip(cle.transpose_xy(cle.transpose_xz(
cle.push(picture))),
flip_x=True,
flip_y=False,
flip_z=False)
return result
def test_statistics_of_background_and_labelled_pixels_gpu_compare_to_clij2():
from skimage.io import imread
blobs = cle.push(imread('data/mini_blobs.tif'))
labels = cle.push(
imread('data/mini_blobs_otsu_labels_excluded_edges_imagej.tif'))
regionprops = cle.statistics_of_background_and_labelled_pixels(
blobs, labels)
table = cle.pull(cle.transpose_xy(cle.push_regionprops(regionprops)))
print(table)
import numpy as np
# np.savetxt('../data/mini_blobs_measurements_pyclesperanto.csv', table,delimiter=',')
clij_reference_table = np.loadtxt('data/mini_blobs_measurements_clij.csv',
delimiter=',',
skiprows=1)
# chop off first column as ImageJ saved an additional counter column
clij_reference_table = clij_reference_table[:, 1:]
print(clij_reference_table)
columns_to_test = [
cle.STATISTICS_ENTRY.IDENTIFIER.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_X.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_Y.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_Z.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_END_X.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_END_Y.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_END_Z.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_WIDTH.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_HEIGHT.value,
cle.STATISTICS_ENTRY.BOUNDING_BOX_DEPTH.value,
cle.STATISTICS_ENTRY.MINIMUM_INTENSITY.value,
cle.STATISTICS_ENTRY.MAXIMUM_INTENSITY.value,
cle.STATISTICS_ENTRY.MEAN_INTENSITY.value,
cle.STATISTICS_ENTRY.SUM_INTENSITY.value,
cle.STATISTICS_ENTRY.STANDARD_DEVIATION_INTENSITY.value,
cle.STATISTICS_ENTRY.PIXEL_COUNT.value,
cle.STATISTICS_ENTRY.MASS_CENTER_X.value,
cle.STATISTICS_ENTRY.MASS_CENTER_Y.value,
cle.STATISTICS_ENTRY.MASS_CENTER_Z.value,
cle.STATISTICS_ENTRY.CENTROID_X.value,
cle.STATISTICS_ENTRY.CENTROID_Y.value,
cle.STATISTICS_ENTRY.CENTROID_Z.value,
cle.STATISTICS_ENTRY.SUM_DISTANCE_TO_CENTROID.value,
cle.STATISTICS_ENTRY.MEAN_DISTANCE_TO_CENTROID.value,
cle.STATISTICS_ENTRY.MAX_DISTANCE_TO_CENTROID.value,
cle.STATISTICS_ENTRY.MAX_MEAN_DISTANCE_TO_CENTROID_RATIO.value,
cle.STATISTICS_ENTRY.SUM_INTENSITY_TIMES_X.value,
cle.STATISTICS_ENTRY.SUM_INTENSITY_TIMES_Y.value,
cle.STATISTICS_ENTRY.SUM_INTENSITY_TIMES_Z.value,
cle.STATISTICS_ENTRY.SUM_X.value,
cle.STATISTICS_ENTRY.SUM_Y.value,
cle.STATISTICS_ENTRY.SUM_Z.value,
cle.STATISTICS_ENTRY.SUM_DISTANCE_TO_MASS_CENTER.value,
cle.STATISTICS_ENTRY.MEAN_DISTANCE_TO_MASS_CENTER.value,
cle.STATISTICS_ENTRY.MAX_DISTANCE_TO_MASS_CENTER.value,
cle.STATISTICS_ENTRY.MAX_MEAN_DISTANCE_TO_MASS_CENTER_RATIO.value,
]
for column_index in columns_to_test:
reference_values = clij_reference_table[:, column_index]
values = table[:, column_index]
print("testing STATISTICS_ENTRY" +
str(cle.STATISTICS_ENTRY(column_index)))
print(reference_values)
print(values)
assert np.allclose(reference_values, values, 0.001)
