Update for latest version BigStitcher, add fusion (BDV-playground)

src/imcflibs/imagej/misc.py

@@ -1,5 +1,6 @@
 """Miscellaneous ImageJ related functions, mostly convenience wrappers."""
 
+import csv
 import sys
 import time
 import smtplib
@@ -417,3 +418,33 @@
     threshold_value = int(round(threshold_value.get()))
 
     return threshold_value
+
+
+def write_results(out_file, content):
+    """Write the results to a csv file.
+
+    Parameters
+    ----------
+    out_file : str
+        Path to the output file.
+    content : list of OrderedDict
+        List of dictionaries representing the results.
+
+    """
+
+    # Check if the output file exists
+    if not os.path.exists(out_file):
+        # If the file does not exist, create it and write the header
+        with open(out_file, "wb") as f:
+            dict_writer = csv.DictWriter(
+                f, content[0].keys(), delimiter=";"
+            )
+            dict_writer.writeheader()
+            dict_writer.writerows(content)
+    else:
+        # If the file exists, append the results
+        with open(out_file, "ab") as f:
+            dict_writer = csv.DictWriter(
+                f, content[0].keys(), delimiter=";"
+            )
+            dict_writer.writerows(content)

src/imcflibs/imagej/objects3d.py

@@ -5,9 +5,13 @@
 [mcib3d]: https://mcib3d.frama.io/3d-suite-imagej/
 """
 
+from de.mpicbg.scf.imgtools.image.create.image import ImageCreationUtilities
+from de.mpicbg.scf.imgtools.image.create.labelmap import WatershedLabeling
 from ij import IJ
 from mcib3d.geom import Objects3DPopulation
 from mcib3d.image3d import ImageHandler, ImageLabeller
+from mcib3d.image3d.processing import MaximaFinder
+from net.imglib2.img import ImagePlusAdapter
 
 
 def population3d_to_imgplus(imp, population):
@@ -145,3 +149,93 @@
 
     # Return the new population with the filtered objects
     return Objects3DPopulation(objects_within_intensity)
+def maxima_finder_3D(imageplus, min_threshold=0, noise=100, rxy=1.5, rz=1.5):
+    """
+    Find local maxima in a 3D image.
+
+    This function identifies local maxima in a 3D image using a specified minimum threshold and noise level.
+    The radii for the maxima detection can be set independently for the x/y and z dimensions.
+
+    Parameters
+    ----------
+    imageplus : ij.ImagePlus
+        The input 3D image in which to find local maxima.
+    min_threshold : int, optional
+        The minimum intensity threshold for maxima detection. Default is 0.
+    noise : int, optional
+        The noise tolerance level for maxima detection. Default is 100.
+    rxy : float, optional
+        The radius for maxima detection in the x and y dimensions. Default is 1.5.
+    rz : float, optional
+        The radius for maxima detection in the z dimension. Default is 1.5.
+
+    Returns
+    -------
+    ij.ImagePlus
+        An ImagePlus object containing the detected maxima as peaks.
+    """
+    # Wrap the input ImagePlus into an ImageHandler
+    img = ImageHandler.wrap(imageplus)
+
+    # Duplicate the image and apply a threshold cut-off
+    thresholded = img.duplicate()
+    thresholded.thresholdCut(min_threshold, False, True)
+
+    # Initialize the MaximaFinder with the thresholded image and noise level
+    maxima_finder = MaximaFinder(thresholded, noise)
+
+    # Set the radii for maxima detection in x/y and z dimensions
+    maxima_finder.setRadii(rxy, rz)
+
+    # Retrieve the image peaks as an ImageHandler
+    img_peaks = maxima_finder.getImagePeaks()
+
+    # Convert the ImageHandler peaks to an ImagePlus
+    imp_peaks = img_peaks.getImagePlus()
+
+    # Set the calibration of the peaks image to match the input image
+    imp_peaks.setCalibration(imageplus.getCalibration())
+
+    # Set the title of the peaks image
+    imp_peaks.setTitle("Peaks")
+
+    return imp_peaks
+
+
+def seeded_watershed(imp_binary, imp_peaks, threshold=10):
+    """
+    Perform a seeded watershed segmentation on a binary image using seed points.
+
+    This function applies a watershed segmentation to a binary image using seed points provided in another image.
+    An optional threshold can be specified to control the segmentation process.
+
+    Parameters
+    ----------
+    imp_binary : ij.ImagePlus
+        The binary image to segment.
+    imp_peaks : ij.ImagePlus
+        The image containing the seed points for the watershed segmentation.
+    threshold : float, optional
+        The threshold value to use for the segmentation. Default is 10.
+
+    Returns
+    -------
+    ij.ImagePlus
+        The segmented image with labels.
+    """
+
+    img = ImagePlusAdapter.convertFloat(imp_binary)
+    img_seed = ImagePlusAdapter.convertFloat(imp_peaks).copy()
+
+    if threshold:
+        watersheded_result = WatershedLabeling.watershed(img, img_seed, threshold)
+    else:
+        watersheded_result = WatershedLabeling.watershed(img, img_seed)
+
+    return ImageCreationUtilities.convertImgToImagePlus(
+        watersheded_result,
+        "Label image",
+        "",
+        imp_binary.getDimensions(),
+        imp_binary.getCalibration(),
+    )

tests/bdv/test_define_dataset_auto.py

@@ -1,10 +1,14 @@
+"""Tests for the automatic dataset definition functionality in the BDV module."""
+
 import logging
 
 from imcflibs import pathtools
 from imcflibs.imagej import bdv
 
 
-def set_default_values(project_filename, file_path):
+def set_default_values(
+    project_filename, file_path, series_type="Tiles"
+):
     """Set the default values for dataset definitions.
 
     Parameters
@@ -13,9 +17,11 @@ def set_default_values(project_filename, file_path):
         Name of the project
     file_path : pathlib.Path
         Path to a temporary folder
+    series_type : str, optional
+        Type of Bioformats series (default is "Tiles")
 
     Returns
-    ----------
+    -------
     str
         Start of the options for dataset definitions.
     """
@@ -32,15 +38,17 @@ def set_default_values(project_filename, file_path):
         + file_info["path"]
         + "] "
         + "exclude=10 "
+        + "bioformats_series_are?="
+        + series_type
+        + " "
         + "move_tiles_to_grid_(per_angle)?=[Do not move Tiles to Grid (use Metadata if available)] "
     )
 
     return options
 
 
 def test_define_dataset_auto_tile(tmp_path, caplog):
-    """
-    Test automatic dataset definition method for tile series.
+    """Test automatic dataset definition method for tile series.
 
     Parameters
     ----------
@@ -69,18 +77,22 @@ def test_define_dataset_auto_tile(tmp_path, caplog):
     bf_series_type = "Tiles"
 
     # Define the ImageJ command
-    cmd = "Define dataset ..."
+    cmd = "Define Multi-View Dataset"
 
     # Set the default values for dataset definitions
     options = set_default_values(project_filename, file_path)
 
     # Construct the options for dataset definitions
     options = (
         options
-        + "how_to_load_images=["
+        + "how_to_store_input_images=["
         + "Re-save as multiresolution HDF5"
         + "] "
-        + "dataset_save_path=["
+        + "load_raw_data_virtually "
+        + "metadata_save_path=["
+        + result_folder
+        + "] "
+        + "image_data_save_path=["
         + result_folder
         + "] "
         + "check_stack_sizes "
@@ -94,14 +106,15 @@ def test_define_dataset_auto_tile(tmp_path, caplog):
     final_call = "IJ.run(cmd=[%s], params=[%s])" % (cmd, options)
 
     # Define the dataset using the "Auto-Loader" option
-    bdv.define_dataset_auto(project_filename, file_path, bf_series_type)
+    bdv.define_dataset_auto(
+        project_filename, file_info["path"], bf_series_type
+    )
     # Check if the final call is in the log
     assert final_call == caplog.messages[0]
 
 
 def test_define_dataset_auto_angle(tmp_path, caplog):
-    """
-    Test automatic dataset definition method for angle series.
+    """Test automatic dataset definition method for angle series.
 
     Parameters
     ----------
@@ -133,15 +146,21 @@ def test_define_dataset_auto_angle(tmp_path, caplog):
     cmd = "Define Multi-View Dataset"
 
     # Set the default values for dataset definitions
-    options = set_default_values(project_filename, file_path)
+    options = set_default_values(
+        project_filename, file_path, bf_series_type
+    )
 
     # Construct the options for dataset definitions
     options = (
         options
-        + "how_to_load_images=["
+        + "how_to_store_input_images=["
         + "Re-save as multiresolution HDF5"
         + "] "
-        + "dataset_save_path=["
+        + "load_raw_data_virtually "
+        + "metadata_save_path=["
+        + result_folder
+        + "] "
+        + "image_data_save_path=["
         + result_folder
         + "] "
         + "check_stack_sizes "
@@ -156,6 +175,8 @@ def test_define_dataset_auto_angle(tmp_path, caplog):
     final_call = "IJ.run(cmd=[%s], params=[%s])" % (cmd, options)
 
     # Define the dataset using the "Auto-Loader" option
-    bdv.define_dataset_auto(project_filename, file_path, bf_series_type)
+    bdv.define_dataset_auto(
+        project_filename, file_info["path"], bf_series_type
+    )
     # Check if the final call is in the log
     assert final_call == caplog.messages[0]

tests/bdv/test_definitionoptions.py

@@ -1,13 +1,16 @@
+"""Tests for the imcflibs.imagej.bdv.DefinitionOptions class."""
+
 import pytest
 
 from imcflibs.imagej.bdv import DefinitionOptions
 
+
 def test_defaults():
     """Test the default options by calling all formatters on a "raw" objects."""
     acitt_options = (
         "multiple_angles=[NO (one angle)] "
         "multiple_channels=[YES (all channels in one file)] "
-        "multiple_illuminations=[NO (one illumination direction)] "
+        "multiple_illuminations_directions=[NO (one illumination direction)] "
         "multiple_tiles=[YES (one file per tile)] "
         "multiple_timepoints=[NO (one time-point)] "
     )
@@ -16,6 +19,7 @@ def test_defaults():
 
     assert def_opts.fmt_acitt_options() == acitt_options
 
+
 def test__definition_option():
     """Test an example with wrong setting for definition option."""
 
@@ -24,15 +28,19 @@ def test__definition_option():
     def_opts = DefinitionOptions()
     with pytest.raises(ValueError) as excinfo:
         def_opts.set_angle_definition(test_value)
-    assert str(excinfo.value) == "Value must be one of single, multi_multi or multi_single"
+    assert (
+        str(excinfo.value)
+        == "Value must be one of single, multi_multi. Support for multi_single is not available for angles and illuminations"
+    )
+
 
 def test__multiple_timepoints_files():
     """Test an example setting how to treat multiple time-points."""
 
     acitt_options = (
         "multiple_angles=[NO (one angle)] "
         "multiple_channels=[YES (all channels in one file)] "
-        "multiple_illuminations=[NO (one illumination direction)] "
+        "multiple_illuminations_directions=[NO (one illumination direction)] "
         "multiple_tiles=[YES (one file per tile)] "
         "multiple_timepoints=[YES (one file per time-point)] "
     )
@@ -42,13 +50,14 @@ def test__multiple_timepoints_files():
 
     assert def_opts.fmt_acitt_options() == acitt_options
 
+
 def test__multiple_channels_files_multiple_timepoints():
     """Test an example setting how to treat multiple channels and multiple time-points."""
 
     acitt_options = (
         "multiple_angles=[NO (one angle)] "
         "multiple_channels=[YES (one file per channel)] "
-        "multiple_illuminations=[NO (one illumination direction)] "
+        "multiple_illuminations_directions=[NO (one illumination direction)] "
         "multiple_tiles=[YES (one file per tile)] "
         "multiple_timepoints=[YES (all time-points in one file)] "
     )
@@ -59,14 +68,14 @@ def test__multiple_channels_files_multiple_timepoints():
 
     assert def_opts.fmt_acitt_options() == acitt_options
 
+
 def test_single_tile_multiple_angles_files():
-    """Test an example setting how to treat single tile and multiple angle
-    files"""
+    """Test an example on with one tile and multiple angle files."""
 
     acitt_options = (
         "multiple_angles=[YES (one file per angle)] "
         "multiple_channels=[YES (all channels in one file)] "
-        "multiple_illuminations=[NO (one illumination direction)] "
+        "multiple_illuminations_directions=[NO (one illumination direction)] "
         "multiple_tiles=[NO (one tile)] "
         "multiple_timepoints=[NO (one time-point)] "
     )

tests/bdv/test_processingoptions.py

@@ -1,3 +1,5 @@
+"""Tests for the ProcessingOptions class from the imcflibs.imagej.bdv module."""
+
 from imcflibs.imagej.bdv import ProcessingOptions
 
 
@@ -18,7 +20,10 @@ def test_defaults():
         "how_to_treat_tiles=compare "
         "how_to_treat_timepoints=[treat individually] "
     )
-    use_acitt = "channels=[Average Channels] " "illuminations=[Average Illuminations] "
+    use_acitt = (
+        "channels=[Average Channels] "
+        "illuminations=[Average Illuminations] "
+    )
 
     proc_opts = ProcessingOptions()
 
@@ -47,7 +52,10 @@ def test__treat_tc_ti__ref_c1():
         "how_to_treat_tiles=compare "
         "how_to_treat_timepoints=[treat individually] "
     )
-    use_acitt = "channels=[use Channel 1] " "illuminations=[Average Illuminations] "
+    use_acitt = (
+        "channels=[use Channel 1] "
+        "illuminations=[Average Illuminations] "
+    )
 
     proc_opts = ProcessingOptions()
     proc_opts.treat_tiles("compare")

tests/bdv/test_processingoptions_example3.py

@@ -1,3 +1,4 @@
+"""Tests for ProcessingOptions class with multiple reference channels configuration."""
 
 from imcflibs.imagej.bdv import ProcessingOptions