Update to [email protected]

pyclesperanto/_tier4.py

@@ -0,0 +1,169 @@
+#
+# This code is auto-generated from CLIc 'cle::tier4.hpp' file, do not edit manually.
+#
+
+import importlib
+import warnings
+from typing import Optional
+
+import numpy as np
+
+from ._array import Image
+from ._core import Device
+from ._decorators import plugin_function
+
+clic = importlib.import_module('._pyclesperanto', package='pyclesperanto')
+
+@plugin_function
+def label_bounding_box(
+    input_image: Image,
+    label_id: int,
+    device: Optional[Device] =None
+) -> list:
+    """Determines the bounding box of the specified label from a label image. The
+    positions are returned in  an array of 6 values as follows: minX, minY, minZ,
+    maxX, maxY, maxZ.
+
+    Parameters
+    ----------
+    input_image: Image 
+
+    label_id: int 
+
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    list
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_boundingBox
+    """
+
+    return clic._label_bounding_box(device, input_image, int(label_id))
+
+@plugin_function(category=["in assistant", "combine", "bia-bob-suggestion"])
+def mean_squared_error(
+    input_image0: Image,
+    input_image1: Image,
+    device: Optional[Device] =None
+) -> float:
+    """Determines the mean squared error (MSE) between two images. The MSE will be
+    stored in a new row of ImageJs Results table in the column 'MSE'.
+
+    Parameters
+    ----------
+    input_image0: Image 
+
+    input_image1: Image 
+
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    float
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_meanSquaredError
+    """
+
+    return clic._mean_squared_error(device, input_image0, input_image1)
+
+@plugin_function
+def spots_to_pointlist(
+    input_image: Image,
+    output_image: Optional[Image] =None,
+    device: Optional[Device] =None
+) -> Image:
+    """Transforms a spots image as resulting from maximum/minimum detection in an image
+    where every column contains d pixels (with d = dimensionality of the original
+    image) with the coordinates of the maxima/minima.
+
+    Parameters
+    ----------
+    input_image: Image 
+
+    output_image: Optional[Image] (= None)
+
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    Image
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_spotsToPointList
+    """
+
+    return clic._spots_to_pointlist(device, input_image, output_image)
+
+@plugin_function(category=["label processing", "in assistant", "bia-bob-suggestion"])
+def relabel_sequential(
+    input_image: Image,
+    output_image: Optional[Image] =None,
+    blocksize: int =4096,
+    device: Optional[Device] =None
+) -> Image:
+    """Analyses a label map and if there are gaps in the indexing (e.g. label 5 is not
+    present) all subsequent labels will be relabelled. Thus, afterwards number of
+    labels and maximum label index are equal. This operation is mostly performed on
+    the CPU.
+
+    Parameters
+    ----------
+    input_image: Image 
+
+    output_image: Optional[Image] (= None)
+
+    blocksize: int (= 4096)
+        Renumbering is done in blocks for performance reasons.
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    Image
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_closeIndexGapsInLabelMap
+    """
+
+    return clic._relabel_sequential(device, input_image, output_image, int(blocksize))
+
+@plugin_function(category=["binarize", "in assistant", "bia-bob-suggestion"])
+def threshold_otsu(
+    input_image: Image,
+    output_image: Optional[Image] =None,
+    device: Optional[Device] =None
+) -> Image:
+    """Binarizes an image using Otsu's threshold method [3] implemented in
+    scikit-image[2] using a histogram determined on the GPU to create binary images.
+
+    Parameters
+    ----------
+    input_image: Image 
+
+    output_image: Optional[Image] (= None)
+
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    Image
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_thresholdOtsu
+    [2] https://scikit-image.org/docs/dev/api/skimage.filters.html#skimage.filters.threshold_otsu
+    [3] https://ieeexplore.ieee.org/document/4310076
+    """
+
+    return clic._threshold_otsu(device, input_image, output_image)

pyclesperanto/_tier5.py

@@ -0,0 +1,106 @@
+#
+# This code is auto-generated from CLIc 'cle::tier5.hpp' file, do not edit manually.
+#
+
+import importlib
+import warnings
+from typing import Optional
+
+import numpy as np
+
+from ._array import Image
+from ._core import Device
+from ._decorators import plugin_function
+
+clic = importlib.import_module('._pyclesperanto', package='pyclesperanto')
+
+@plugin_function(category=["combine"])
+def array_equal(
+    input_image0: Image,
+    input_image1: Image,
+    device: Optional[Device] =None
+) -> bool:
+    """Compares if all pixels of two images are identical. If shape of the images or
+    any pixel are different, returns False. True otherwise This function is supposed
+    to work similarly like its counterpart in numpy [1].
+
+    Parameters
+    ----------
+    input_image0: Image 
+
+    input_image1: Image 
+
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    bool
+
+    References
+    ----------
+    [1] https://numpy.org/doc/stable/reference/generated/numpy.array_equal.html
+    """
+
+    return clic._array_equal(device, input_image0, input_image1)
+
+@plugin_function(category=["label processing", "combine labels", "in assistant", "bia-bob-suggestion"])
+def combine_labels(
+    input_image0: Image,
+    input_image1: Image,
+    output_image: Optional[Image] =None,
+    device: Optional[Device] =None
+) -> Image:
+    """Combines two label images by adding labels of a given label image to another.
+    Labels in the second image overwrite labels in the first passed image.
+    Afterwards, labels are relabeled sequentially.
+
+    Parameters
+    ----------
+    input_image0: Image 
+        label image to add labels to.
+    input_image1: Image 
+        label image to add labels from.
+    output_image: Optional[Image] (= None)
+        Output label image.
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    Image
+    """
+
+    return clic._combine_labels(device, input_image0, input_image1, output_image)
+
+@plugin_function(category=["label", "in assistant", "bia-bob-suggestion"])
+def connected_components_labeling(
+    input_image: Image,
+    output_image: Optional[Image] =None,
+    connectivity: str ='box',
+    device: Optional[Device] =None
+) -> Image:
+    """Performs connected components analysis inspecting the box neighborhood of every
+    pixel to a binary image and generates a label map.
+
+    Parameters
+    ----------
+    input_image: Image 
+        Binary image to label.
+    output_image: Optional[Image] (= None)
+        Output label image.
+    connectivity: str (= 'box')
+        Defines pixel neighborhood relationship, "box" or "sphere".
+    device: Optional[Device] (= None)
+        Device to perform the operation on.
+
+    Returns
+    -------
+    Image
+
+    References
+    ----------
+    [1] https://clij.github.io/clij2-docs/reference_connectedComponentsLabelingBox
+    """
+
+    return clic._connected_components_labeling(device, input_image, output_image, str(connectivity))