Update tooltips

Author: constantinpape

micro_sam/sam_annotator/_tooltips.py

@@ -3,52 +3,52 @@
 
 tooltips = {
   "embedding": {
-    "custom_weights": "Select a path for custom weights.",
+    "custom_weights": "Select custom model weights. For example for a model you have finetuned",
     "device": "Select the computational device to use for processing.",
-    "embeddings_save_path": "Path to save the computed image embeddings.",
-    "halo": """Enter values to activate halo. Only x value for quadratic halo and both for custom.
-    Only active when tiling is active.""",
-    "image": "Select an image to segment objects.",
-    "model": "Select a model to use for segmentation tasks.",
-    "prefer_decoder": "Choose if to use segmentation decoder.",
-    "run_button": "Compute embeddings or load embeddings if embeddings file path is specified.",
-    "settings": "",
-    "tiling": "Enter values to activate tiling. Only x-value for quadratic tiling or both values for custom tiling.",
-    # ... other tooltips for embedding widgets ...
+    "embeddings_save_path": "Select path to save or load the computed image embeddings.",
+    "halo": "Enter overlap values for computing tiled embeddings. Enter only x-value for quadratic size.\n Only active when tiling is used.",
+    "image": "Select the napari image layer.",
+    "model": "Select the segment anything model.",
+    "prefer_decoder": "Choose if the segmentation decoder is used for automatic segmentation. Only if it is available for the selected model..",
+    "run_button": "Compute embeddings or load embeddings if embedding_save_path is specified.",
+    "tiling": "Enter tile size for computing tiled embeddings. Enter only x-value for quadratic size or both for non-quadratic.",
   },
   "segmentnd": {
-    "box_extension": "box_extension",
-    "custom_weights": "Path to the file containing pre-trained weights for the segmentation model.",
-    "iou_threshold": "iou_threshold",
-    "motion_smoothing": "motion_smoothing",
-    "projection_dropdown": "projection dropdown",
-    "settings": "",
-    # ... other tooltips for segmentation widgets ...
+    "box_extension": "Enter factor by which box size is increased when projecting to adjacent slices. Larger factors help if object sizes change between slices.",
+    "iou_threshold": "Enter the minimal overlap between objects in adjacent slices to continue segmentation.",
+    "motion_smoothing": "Enter the motion smoothing factor. It is used to follow objects which have a directed movement, higher values help for objects that are moving fast.",
+    "projection_dropdown": "Choose the projection mode. It determines which prompts are derived from the masks projected to adjacent frames to rerun SAM.",
   },
   "autosegment": {
-    "apply_to_volume": "apply_to_volume",
-    "boundary_distance_thresh": "boundary_distance_thresh",
-    "box_nms_thresh": "box_nms_thresh",
-    "center_distance_thresh": "center_distance_thresh",
-    "gap_closing": "gap_closing",
-    "min_extent": "min_extent",
-    "min_object_size": "min_object_size",
-    "pred_iou_thresh": "pred_iou_thresh",
+    # General settings.
+    "apply_to_volume": "Choose if automatic segmentation is run for the full volume or only the current slice.",
+    "gap_closing": "Enter value for closing gaps across slices for volumetric segmentation. Higher values will reduce artifacts due to missing slices in objects but may lead to wrongly merging objects.",
+    "min_extent": "Enter the minimal number of slices for objects in volumetric segmentation. To filter out small segmentation artifacts.",
+    "min_object_size": "Enter the minimal object size in pixels. This refers to the size per slice for volumetric segmentation.",
     "run_button": "Run automatic segmentation.",
-    "stability_score_thresh": "stability_score_thresh",
-    "with_background": "with_background",
-    # ... tooltips for autosegment widgets ...
-  },
-  "segment": {
-    "run_button": "run_button",
+    "with_background": "Choose if your image has a large background area.",
+    # Settings for AIS.
+    "boundary_distance_thresh": "Enter the boundary distance threshold.",
+    "center_distance_thresh": "Enter the center distance threshold.",
+    # Settings for AMG.
+    "box_nms_thresh": "Enter the non-maximum suppression threshold.",
+    "pred_iou_thresh": "Enter the threshold for filtering objects based on the predicted IOU.",
+    "stability_score_thresh": "Enter the threshold for filtering objects based on the stability score.",
   },
   "prompt_menu": {
-    "labels": "Choose positive prompts to inlcude regions or negative ones to exclude regions.",
+    "labels": "Choose positive prompts to inlcude regions or negative ones to exclude regions. Toggle between the settings by pressing [t].",
   },
   "annotator_tracking": {
-    "track_id": "track_id",
-    "track_state": "track_state",
+    "track_id": "Select the id of the track you are currently annotating.",
+    "track_state": "Select the state of the current annotation. Choose 'division' if the object is dviding in the current frame.",
+  },
+  "image_series_annotator": {
+    "folder": "Select the folder with the images to annotate.",
+    "output_folder": "Select the folder for saving the segmentation results.",
+    "pattern": "Select a pattern for selecting files. E.g. '*.tif' to only select tif files. By default all files in the input folder are selected.",
+    "is_volumetric": "Choose if the data you annotate is volumetric.",
   },
+  # TODO update these once we have finalized the names in the widget.
   "training": {
     "checkpoint": "checkpoint",
     "device": "device",
@@ -62,23 +62,21 @@
     "raw_path": "raw_path",
     "raw_path_val": "raw_path_val",
     "segmentation_decoder": "segmentation_decoder",
-    "setting": "setting"
-  }
+  },
 }
 
 
-def get_tooltip(widget_type, name):
-    """
-    Retrieves the tooltip for a given parameter name within a specific widget type.
+def get_tooltip(widget_type: str, name: str) -> str:
+    """Retrieves the tooltip for a given parameter name within a specific widget type.
 
     Args:
-        widget_type (str): The type of widget (e.g., "embedding", "segmentation").
-        name (str): The name of the parameter to get the tooltip for.
+        widget_type: The type of widget (e.g., "embedding", "segmentation").
+        name: The name of the parameter to get the tooltip for.
 
     Returns:
-        str: The tooltip string, or None if not found.
+        The tooltip string, or None if not found.
     """
     if widget_type in tooltips:
         return tooltips[widget_type].get(name)
     else:
-        return None  # Handle cases where the widget type is not found
+        return None  # Handle cases where the widget type is not found

micro_sam/sam_annotator/_widgets.py

@@ -305,14 +305,19 @@ def clear(viewer: "napari.viewer.Viewer") -> None:
     """Widget for clearing the current annotations.
 
     Args:
-        viewer (napari.viewer.Viewer): _description_
+        viewer: The napari viewer.
     """
     vutil.clear_annotations(viewer)
 
 
 @magic_factory(call_button="Clear Annotations [Shift + C]")
 def clear_volume(viewer: "napari.viewer.Viewer", all_slices: bool = True) -> None:
-    """Widget for clearing the current annotations in 3D."""
+    """Widget for clearing the current annotations in 3D.
+
+    Args:
+        viewer: The napari viewer.
+        all_slices: Choose whether to clear the annotations for all or only the current slice.
+    """
     if all_slices:
         vutil.clear_annotations(viewer)
     else:
@@ -322,7 +327,12 @@ def clear_volume(viewer: "napari.viewer.Viewer", all_slices: bool = True) -> Non
 
 @magic_factory(call_button="Clear Annotations [Shift + C]")
 def clear_track(viewer: "napari.viewer.Viewer", all_frames: bool = True) -> None:
-    """Widget for clearing all tracking annotations and state."""
+    """Widget for clearing all tracking annotations and state.
+
+    Args:
+        viewer: The napari viewer.
+        all_frames: Choose whether to clear the annotations for all or only the current frame.
+    """
     if all_frames:
         _reset_tracking_state(viewer)
         vutil.clear_annotations(viewer)
@@ -468,9 +478,11 @@ def commit(
     """Widget for committing the segmented objects from automatic or interactive segmentation.
 
     Args:
-        viewer (napari.viewer.Viewer): _description_
-        layer (str, optional): _description_. Defaults to "current_object".
-        commit_path (Optional[Path], optional): _description_. Defaults to None.
+        viewer: The napari viewer.
+        layer: Select the layer to commit. Can be either 'current_object' to commit interacitve segmentation results.
+            Or 'auto_segmentation' to commit automatic segmentation results.
+        commit_path: Select a file path where the committed results and prompts will be saved.
+            This feature is still experimental.
     """
     _, seg, mask, bb = _commit_impl(viewer, layer)
 
@@ -497,12 +509,14 @@ def commit_track(
     layer: str = "current_object",
     commit_path: Optional[Path] = None,
 ) -> None:
-    """Widget for committing the segmented objects from interactive tracking.
+    """Widget for committing the objects from interactive tracking.
 
     Args:
-        viewer (napari.viewer.Viewer): _description_
-        layer (str, optional): _description_. Defaults to "current_object".
-        commit_path (Optional[Path], optional): _description_. Defaults to None.
+        viewer: The napari viewer.
+        layer: Select the layer to commit. Can be either 'current_object' to commit interacitve segmentation results.
+            Or 'auto_segmentation' to commit automatic segmentation results.
+        commit_path: Select a file path where the committed results and prompts will be saved.
+            This feature is still experimental.
     """
     # Commit the segmentation layer.
     id_offset, seg, mask, bb = _commit_impl(viewer, layer)
@@ -560,7 +574,7 @@ def settings_widget(
     """Widget to update global micro_sam settings.
 
     Args:
-        cache_directory (Optional[Path], optional): _description_. Defaults to util.get_cache_directory().
+        cache_directory: Select the path for the micro_sam cache directory. `$HOME/.cache/micro_sam`.
     """
     os.environ["MICROSAM_CACHEDIR"] = str(cache_directory)
     print(f"micro-sam cache directory set to: {cache_directory}")
@@ -662,15 +676,11 @@ def _validate_prompts(viewer: "napari.viewer.Viewer") -> bool:
 
 @magic_factory(call_button="Segment Object [S]")
 def segment(viewer: "napari.viewer.Viewer", batched: bool = False) -> None:
-    """_summary_
+    """Segment object(s) for the current prompts.
 
     Args:
-        viewer (napari.viewer.Viewer): _description_
-        batched (bool, optional): _description_. Defaults to False.
-        call_button: run code
-
-    Returns:
-        _type_: _description_
+        viewer: The napari viewer.
+        batched: Choose if you want to segment multiple objects with point prompts.
     """
     if _validate_embeddings(viewer):
         return None
@@ -701,6 +711,11 @@ def segment(viewer: "napari.viewer.Viewer", batched: bool = False) -> None:
 
 @magic_factory(call_button="Segment Slice [S]")
 def segment_slice(viewer: "napari.viewer.Viewer") -> None:
+    """Segment object for to the current prompts.
+
+    Args:
+        viewer: The napari viewer.
+    """
     if _validate_embeddings(viewer):
         return None
     if _validate_prompts(viewer):
@@ -735,13 +750,10 @@ def segment_slice(viewer: "napari.viewer.Viewer") -> None:
 
 @magic_factory(call_button="Segment Frame [S]")
 def segment_frame(viewer: "napari.viewer.Viewer") -> None:
-    """_summary_
+    """Segment object for the current prompts.
 
     Args:
-        viewer (napari.viewer.Viewer): _description_
-
-    Returns:
-        _type_: _description_
+        viewer: The napari viewer.
     """
     if _validate_embeddings(viewer):
         return None

micro_sam/sam_annotator/image_series_annotator.py

@@ -20,6 +20,7 @@
 from .annotator_3d import Annotator3d
 from ._state import AnnotatorState
 from .util import _sync_embedding_widget
+from ._tooltips import get_tooltip
 
 
 def _precompute(
@@ -291,14 +292,16 @@ def _create_options(self):
         self.folder = None
         _, layout = self._add_path_param(
             "folder", self.folder, "directory",
-            title="Input Folder", placeholder="Folder with images ..."
+            title="Input Folder", placeholder="Folder with images ...",
+            tooltip=get_tooltip("image_series_annotator", "folder")
         )
         self.layout().addLayout(layout)
 
         self.output_folder = None
         _, layout = self._add_path_param(
             "output_folder", self.output_folder, "directory",
-            title="Output Folder", placeholder="Folder to save the results ..."
+            title="Output Folder", placeholder="Folder to save the results ...",
+            tooltip=get_tooltip("image_series_annotator", "output_folder")
         )
         self.layout().addLayout(layout)
 
@@ -307,6 +310,7 @@ def _create_options(self):
         model_options = [model for model in model_options if not model.endswith("decoder")]
         _, layout = self._add_choice_param(
             "model_type", self.model_type, model_options, title="Model:",
+            tooltip=get_tooltip("embedding", "model")
         )
         self.layout().addLayout(layout)
 
@@ -316,39 +320,47 @@ def _create_settings(self):
 
         self.pattern = "*"
         _, layout = self._add_string_param(
-            "", self.pattern,
+            "", self.pattern, tooltip=get_tooltip("image_series_annotator", "pattern")
         )
         setting_values.layout().addLayout(layout)
 
         self.is_volumetric = False
-        setting_values.layout().addWidget(self._add_boolean_param("is_volumetric", self.is_volumetric))
+        setting_values.layout().addWidget(self._add_boolean_param(
+            "is_volumetric", self.is_volumetric, tooltip=get_tooltip("image_series_annotator", "is_volumetric")
+        ))
 
         self.device = "auto"
         device_options = ["auto"] + util._available_devices()
-        self.device_dropdown, layout = self._add_choice_param("device", self.device, device_options)
+        self.device_dropdown, layout = self._add_choice_param(
+            "device", self.device, device_options, tooltip=get_tooltip("embedding", "device")
+        )
         setting_values.layout().addLayout(layout)
 
         self.embeddings_save_path = None
         _, layout = self._add_path_param(
-            "embeddings_save_path", self.embeddings_save_path, "directory", title="embeddings save path:"
+            "embeddings_save_path", self.embeddings_save_path, "directory", title="embeddings save path:",
+            tooltip=get_tooltip("embedding", "embeddings_save_path")
         )
         setting_values.layout().addLayout(layout)
 
         self.custom_weights = None  # select_file
         _, layout = self._add_path_param(
-            "custom_weights", self.custom_weights, "file", title="custom weights path:"
+            "custom_weights", self.custom_weights, "file", title="custom weights path:",
+            tooltip=get_tooltip("embedding", "custom_weights")
         )
         setting_values.layout().addLayout(layout)
 
         self.tile_x, self.tile_y = 0, 0
         self.tile_x_param, self.tile_y_param, layout = self._add_shape_param(
-            ("tile_x", "tile_y"), (self.tile_x, self.tile_y), min_val=0, max_val=2048, step=16
+            ("tile_x", "tile_y"), (self.tile_x, self.tile_y), min_val=0, max_val=2048, step=16,
+            tooltip=get_tooltip("embedding", "tiling")
         )
         setting_values.layout().addLayout(layout)
 
         self.halo_x, self.halo_y = 0, 0
         self.halo_x_param, self.halo_y_param, layout = self._add_shape_param(
-            ("halo_x", "halo_y"), (self.halo_x, self.halo_y), min_val=0, max_val=512
+            ("halo_x", "halo_y"), (self.halo_x, self.halo_y), min_val=0, max_val=512,
+            tooltip=get_tooltip("embedding", "halo")
         )
         setting_values.layout().addLayout(layout)