Document grid search util

Author: C-Achard

docs/source/guides/utils_module_guide.rst

@@ -11,6 +11,21 @@ See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.
 
 You may specify the results directory for saving; afterwards you can run each action on a folder or on the currently selected layer.
 
+Default Paths for Saving Results
+________________________________
+
+Each utility saves results to a default directory under the user's home directory. The default paths are as follows:
+
+* Artifact Removal: ``~/cellseg3d/artifact_removed``
+* Fragmentation: ``~/cellseg3d/fragmented``
+* Anisotropy Correction: ``~/cellseg3d/anisotropy``
+* Small Object Removal: ``~/cellseg3d/small_removed``
+* Semantic Label Conversion: ``~/cellseg3d/semantic_labels``
+* Instance Label Conversion: ``~/cellseg3d/instance_labels``
+* Thresholding: ``~/cellseg3d/threshold``
+* Statistics: ``~/cellseg3d/stats``
+* Threshold Grid Search: ``~/cellseg3d/threshold_grid_search``
+
 Available actions
 __________________
 
@@ -89,6 +104,18 @@ Global metrics :
 | Clears labels that are larger than a given threshold.
 | This is useful for removing artifacts that are larger than the objects of interest.
 
+11. Find best threshold
+-----------------------
+| Finds the best threshold for separating objects from the background.
+| Requires a prediction from a model and GT labels as input.
+
+.. caution::
+    If the input prediction is not from the plugin, it will be remapped to the 0-1 range.
+
+| The threshold is found by maximizing the Dice coefficient between the thresolded prediction and the binarized GT labels.
+
+| The value for the best threshold will be displayed, and the prediction will be thresholded and saved with this value.
+
 Source code
 ___________
 

napari_cellseg3d/code_plugins/plugin_convert.py

@@ -857,6 +857,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None):
             parent=parent,
         )
         self.do_binarize = False
+        self.do_remap = False
         self.result_text = ""
         self.values = {}
 
@@ -926,6 +927,8 @@ def _check_ready(self):
             or len(np.unique(label_data)) != 2
         ):
             self.do_binarize = True
+        if image_data.min() < 0 or image_data.max() > 1:
+            self.do_remap = True
         return True
 
     def _get_dice_graph(self):
@@ -945,6 +948,11 @@ def _start(self):
         if self.do_binarize:
             logger.info("Labels values are not binary, binarizing")
             label_data = to_semantic(label_data)
+        if self.do_remap:
+            logger.info(
+                "Prediction values are not from a model. Remapping between 0 and 1"
+            )
+            pred_data = utils.remap_image(pred_data, new_max=1)
         # find best threshold
         search_space = np.arange(0, 1, 0.05)
         for i in search_space: