Merge pull request #279 from clEsperanto/update-clic-0.14.1

Update to [email protected]

pyclesperanto/_tier1.py

@@ -1838,7 +1838,7 @@ def mean_sphere(
     return clic._mean_sphere(device, input_image, output_image, int(radius_x), int(radius_y), int(radius_z))
 
 @plugin_function(categories=["filter", "denoise", "in assistant"])
-def mean(
+def mean_filter(
     input_image: Image,
     output_image: Optional[Image] =None,
     radius_x: int =1,
@@ -1876,7 +1876,7 @@ def mean(
     ----------
     [1] https://clij.github.io/clij2-docs/reference_mean3DSphere
     """
-    return clic._mean(device, input_image, output_image, int(radius_x), int(radius_y), int(radius_z), str(connectivity))
+    return clic._mean_filter(device, input_image, output_image, int(radius_x), int(radius_y), int(radius_z), str(connectivity))
 
 @plugin_function(categories=["projection"])
 def mean_x_projection(
@@ -4257,7 +4257,7 @@ def variance_sphere(
     return clic._variance_sphere(device, input_image, output_image, int(radius_x), int(radius_y), int(radius_z))
 
 @plugin_function(categories=["filter", "edge detection", "in assistant"])
-def variance(
+def variance_filter(
     input_image: Image,
     output_image: Optional[Image] =None,
     radius_x: int =1,
@@ -4296,7 +4296,7 @@ def variance(
     [1] https://clij.github.io/clij2-docs/reference_varianceBox
     [2] https://clij.github.io/clij2-docs/reference_varianceSphere
     """
-    return clic._variance(device, input_image, output_image, int(radius_x), int(radius_y), int(radius_z), str(connectivity))
+    return clic._variance_filter(device, input_image, output_image, int(radius_x), int(radius_y), int(radius_z), str(connectivity))
 
 @plugin_function
 def write_values_to_positions(

pyclesperanto/_tier4.py

@@ -163,6 +163,38 @@ def threshold_otsu(
     """
     return clic._threshold_otsu(device, input_image, output_image)
 
+@plugin_function(categories=["label measurement", "map", "in assistant", "combine"])
+def mean_intensity_map(
+    input_image: Image,
+    labels: Image,
+    output_image: Optional[Image] =None,
+    device: Optional[Device] =None
+) -> Image:
+    """Takes an image and a corresponding label map, determines the mean   intensity
+    per label and replaces every label with the that number. This results in a
+    parametric image expressing mean object intensity.
+
+    Parameters
+    ----------
+    input_image: Image 
+        intensity image
+    labels: Image 
+        label image
+    output_image: Optional[Image] (= None)
+        Parametric image computed
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    Image
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_meanIntensityMap
+    """
+    return clic._mean_intensity_map(device, input_image, labels, output_image)
+
 @plugin_function(categories=["label measurement", "map", "in assistant"])
 def pixel_count_map(
     input_image: Image,

pyclesperanto/_version.py

@@ -1,3 +1,3 @@
-VERSION = "0.14.0"
-CLIC_VERSION = "0.14.0"
+VERSION = "0.14.1"
+CLIC_VERSION = "0.14.1"
 COMMON_ALIAS = "cle"

src/wrapper/tier1_.cpp

@@ -238,7 +238,7 @@ m.def("_absolute", &cle::tier1::absolute_func, "Call cle::tier1::absolute_func f
     py::return_value_policy::take_ownership,
     py::arg("device"), py::arg("src"), py::arg("dst"), py::arg("radius_x"), py::arg("radius_y"), py::arg("radius_z"));
 
-    m.def("_mean", &cle::tier1::mean_func, "Call cle::tier1::mean_func from C++ CLIc.",
+    m.def("_mean_filter", &cle::tier1::mean_filter_func, "Call cle::tier1::mean_filter_func from C++ CLIc.",
     py::return_value_policy::take_ownership,
     py::arg("device"), py::arg("src"), py::arg("dst"), py::arg("radius_x"), py::arg("radius_y"), py::arg("radius_z"), py::arg("connectivity"));
 
@@ -546,7 +546,7 @@ m.def("_absolute", &cle::tier1::absolute_func, "Call cle::tier1::absolute_func f
     py::return_value_policy::take_ownership,
     py::arg("device"), py::arg("src"), py::arg("dst"), py::arg("radius_x"), py::arg("radius_y"), py::arg("radius_z"));
 
-    m.def("_variance", &cle::tier1::variance_func, "Call cle::tier1::variance_func from C++ CLIc.",
+    m.def("_variance_filter", &cle::tier1::variance_filter_func, "Call cle::tier1::variance_filter_func from C++ CLIc.",
     py::return_value_policy::take_ownership,
     py::arg("device"), py::arg("src"), py::arg("dst"), py::arg("radius_x"), py::arg("radius_y"), py::arg("radius_z"), py::arg("connectivity"));
 

src/wrapper/tier4_.cpp

@@ -26,6 +26,10 @@ m.def("_label_bounding_box", &cle::tier4::label_bounding_box_func, "Call cle::ti
     py::return_value_policy::take_ownership,
     py::arg("device"), py::arg("src"), py::arg("dst"));
 
+    m.def("_mean_intensity_map", &cle::tier4::mean_intensity_map_func, "Call cle::tier4::mean_intensity_map_func from C++ CLIc.",
+    py::return_value_policy::take_ownership,
+    py::arg("device"), py::arg("src"), py::arg("labels"), py::arg("dst"));
+
     m.def("_pixel_count_map", &cle::tier4::pixel_count_map_func, "Call cle::tier4::pixel_count_map_func from C++ CLIc.",
     py::return_value_policy::take_ownership,
     py::arg("device"), py::arg("src"), py::arg("dst"));

tests/test_mean_box.py

@@ -31,7 +31,7 @@ def test_mean_box():
     )
 
     result = cle.create(test1)
-    cle.mean(test1, result, 1, 1, 0)
+    cle.mean_filter(test1, result, 1, 1, 0)
 
     a = cle.pull(result)
     b = cle.pull(reference)

tests/test_mean_sphere.py

@@ -31,7 +31,7 @@ def test_mean_sphere():
     )
 
     result = cle.create(test1, dtype=float)
-    cle.mean(test1, result, 1, 1, 0, "sphere")
+    cle.mean_filter(test1, result, 1, 1, 0, "sphere")
 
     a = cle.pull(result)
     b = cle.pull(reference)

tests/test_variance_box.py

@@ -31,7 +31,7 @@ def test_variance_box():
     )
 
     result = cle.create(test1, dtype=float)
-    cle.variance(test1, result, 1, 1, 0)
+    cle.variance_filter(test1, result, 1, 1, 0)
 
     a = cle.pull(result)
     b = cle.pull(reference)

tests/test_variance_sphere.py

@@ -31,7 +31,7 @@ def test_variance_sphere():
     )
 
     result = cle.create(test1, dtype=float)
-    cle.variance(test1, result, 1, 1, 0, connectivity="sphere")
+    cle.variance_filter(test1, result, 1, 1, 0, connectivity="sphere")
 
     a = cle.pull(result)
     b = cle.pull(reference)