inference tooltips + readme tweaks

Author: C-Achard

README.md

@@ -1,11 +1,11 @@
 # napari-cellseg3d
 
-[![License](https://img.shields.io/pypi/l/napari-cellseg3d.svg?color=green)](https://github.com/C_Achard/napari-cellseg-annotator/raw/main/LICENSE)
-[![PyPI](https://img.shields.io/pypi/v/napari-cellseg3d.svg?color=green)](https://pypi.org/project/napari-cellseg-annotator)
-[![Python Version](https://img.shields.io/pypi/pyversions/napari-cellseg3d.svg?color=green)](https://python.org)
-[![tests](https://github.com/C_Achard/napari-cellseg3d/workflows/tests/badge.svg)](https://github.com/C_Achard/napari-cellseg-annotator/actions)
-[![codecov](https://codecov.io/gh/C_Achard/napari-cellseg3d/branch/main/graph/badge.svg)](https://codecov.io/gh/C_Achard/napari-cellseg-annotator)
-[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://napari-hub.org/plugins/cellseg-annotator-test)
+[![License](https://img.shields.io/pypi/l/napari-cellseg-annotator.svg?color=green)](https://github.com/AdaptiveMotorControlLab/CellSeg3d/raw/main/LICENSE)
+[![PyPI](https://img.shields.io/pypi/v/napari-cellseg-annotator.svg?color=green)](https://pypi.org/project/napari-cellseg-annotator)
+[![Python Version](https://img.shields.io/pypi/pyversions/napari-cellseg-annotator.svg?color=green)](https://python.org)
+[![tests](https://github.com/AdaptiveMotorControlLab/CellSeg3d/workflows/tests/badge.svg)](https://github.com/AdaptiveMotorControlLab/CellSeg3d/actions)
+[![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d/branch/main/graph/badge.svg)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d)
+[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg-annotator)](https://napari-hub.org/plugins/cellseg-annotator-test)
 
 napari plugin providing cell segmentation tools : training, inference, review...
 

napari_cellseg3d/plugin_model_inference.py

@@ -118,9 +118,6 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
         ######################
         # TODO : better way ?
         self.segres_size = ui.make_n_spinboxes(min=1, max=1024, default=128)
-        self.segres_size.setToolTip(
-            "Image size on which the SegResNet has been trained (default : 128)"
-        )
         self.model_choice.currentIndexChanged.connect(
             self.toggle_display_segres_size
         )
@@ -234,6 +231,36 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
         self.btn_model_path.setVisible(False)
         self.lbl_model_path.setVisible(False)
 
+        ##################
+        ##################
+        #tooltips
+        self.view_checkbox.setToolTip("Show results in the napari viewer")
+        self.display_number_choice.setToolTip("Choose how many results to display once the work is done.\n"
+                                              "Maximum is 10 for clarity")
+        self.show_original_checkbox.setToolTip("Displays the image used for inference in the viewer")
+        self.segres_size.setToolTip(
+            "Image size on which the SegResNet has been trained (default : 128)"
+        )
+        self.aniso_checkbox.setToolTip("If you have anisotropic data, you can scale data using your resolution in microns")
+        [w.setToolTip("Resolution in microns") for w in self.aniso_box_widgets]
+        thresh_desc = "Thresholding : all values in the image below the chosen probability threshold will be set to 0, and all others to 1."
+        self.thresholding_checkbox.setToolTip(thresh_desc)
+        self.thresholding_count.setToolTip(thresh_desc)
+        self.window_infer_box.setToolTip("Sliding window inference runs the model on parts of the image"
+                                         "\nrather than the whole image, to reduce memory requirements."
+                                         "\nUse this if you have large images.")
+        self.window_size_choice.setToolTip("Size of the window to run inference with (in pixels)")
+        self.keep_data_on_cpu_box.setToolTip("If enabled, data will be kept on the RAM rather than the VRAM.\nCan avoid out of memory issues with CUDA")
+        self.instance_box.setToolTip("Instance segmentation will convert instance (0/1) labels to labels that attempt to assign an unique ID to each cell.")
+        self.instance_method_choice.setToolTip("Choose which method to use for instance segmentation"
+                                    "\nConnected components : all separated objects will be assigned an unique ID. Robust but will not work correctly with adjacent/touching objects\n"
+                                    "Watershed : assigns objects ID based on the probability gradient surrounding an object. Requires the model to surround objects in a gradient; can possibly correctly separate unique but touching/adjacent objects.")
+        self.instance_prob_thresh.setToolTip("All objects below this probability will be ignored (set to 0)")
+        self.instance_small_object_thresh.setToolTip("Will remove all objects smaller (in volume) than the specified number of pixels")
+        self.save_stats_to_csv_box.setToolTip("Will save several statistics for each object to a csv in the results folder. Stats include : volume, centroid coordinates, sphericity")
+        ##################
+        ##################
+
         self.build()
 
     def check_ready(self):