Merge PR #341 | New export widget

.gitignore

@@ -3,6 +3,7 @@ __pycache__/
 # Conda build
 plantseg.egg-info/
 dist/
+build/
 
 # Dev
 settings.json

docs/snippets/napari/io/widget_export_stacks.py

@@ -5,7 +5,7 @@
 
 from napari_widgets_render import render_widget
 
-from plantseg.viewer_napari.widgets import widget_export_stacks
+from plantseg.viewer_napari.widgets import widget_export_image
 
-html = render_widget(widget_export_stacks)
+html = render_widget(widget_export_image)
 print(html)

plantseg/core/image.py

@@ -121,6 +121,7 @@ class ImageProperties(BaseModel):
     voxel_size: VoxelSize
     image_layout: ImageLayout
     original_voxel_size: VoxelSize
+    original_name: str | None = None
 
     @property
     def dimensionality(self) -> ImageDimensionality:
@@ -274,6 +275,8 @@ def from_napari_layer(cls, layer: Image | Labels) -> "PlantSegImage":
             raise ValueError("Voxel size not found in metadata")
         new_voxel_size = VoxelSize(**metadata["voxel_size"])
 
+        original_name = metadata.get("original_name", None)
+
         # Loading from napari layer, the id needs to be present in the metadata
         # If not present, the layer is corrupted
         if "id" in metadata:
@@ -287,6 +290,7 @@ def from_napari_layer(cls, layer: Image | Labels) -> "PlantSegImage":
             voxel_size=new_voxel_size,
             image_layout=image_layout,
             original_voxel_size=original_voxel_size,
+            original_name=original_name,
         )
 
         if image_type != properties.image_type:
@@ -469,6 +473,10 @@ def voxel_size(self) -> VoxelSize:
     def original_voxel_size(self) -> VoxelSize:
         return self._properties.original_voxel_size
 
+    @property
+    def original_name(self) -> str | None:
+        return self._properties.original_name
+
     @property
     def name(self) -> str:
         return self._properties.name
@@ -575,6 +583,7 @@ def import_image(
         voxel_size=voxel_size,
         image_layout=image_layout,
         original_voxel_size=voxel_size,
+        original_name=image_name,
     )
 
     return PlantSegImage(data=data, properties=image_properties)
@@ -616,46 +625,59 @@ def _image_postprocessing(
 
 def save_image(
     image: PlantSegImage,
-    directory: Path,
-    file_name: str,
-    custom_key: str,
-    scale_to_origin: bool,
-    file_format: str = "tiff",
-    dtype: str = "uint16",
+    export_directory: Path,
+    name_pattern: str,
+    key: str | None = None,
+    scale_to_origin: bool = True,
+    export_format: str = "tiff",
+    data_type: str = "uint16",
 ) -> None:
     """
     Write a PlantSegImage object to disk.
 
     Args:
-        image (PlantSegImage): Image to save
-        directory (Path): Directory to save the image
-        file_name (str): Name of the file
-        custom_key (str): Custom key to save the image (it will be used as suffix in tiff files or as key in h5 or zarr files)
-        scale_to_origin (bool): Scale the image to the original voxel size (if different from the current voxel size)
-        file_format (str): File format to save the image, should be tiff, h5 or zarr
-        dtype (str): Data type to save the image, should be uint8, uint16, float32 or float64
+        image (PlantSegImage): input image to be saved to disk
+        export_directory (Path): output directory path where the image will be saved
+        name_pattern (str): output file name pattern, can contain the {image_name} or {original_name} tokens
+            to be replaced in the final file name.
+        key (str | None): key for the image (used only for h5 and zarr formats).
+        scale_to_origin (bool): scale the voxel size to the original one
+        export_format (str): file format (tiff, h5, zarr)
+        data_type (str): data type to save the image.
     """
-    data, voxel_size = _image_postprocessing(image, scale_to_origin, dtype)
 
-    directory = Path(directory)
+    data, voxel_size = _image_postprocessing(image, scale_to_origin=scale_to_origin, export_dtype=data_type)
+
+    directory = Path(export_directory)
     directory.mkdir(parents=True, exist_ok=True)
 
-    if file_format == "tiff":
-        file_path_name = directory / f"{file_name}_{custom_key}.tiff"
+    name_pattern = name_pattern.replace("{image_name}", image.name)
+
+    if image.original_name is not None:
+        name_pattern = name_pattern.replace("{original_name}", image.original_name)
+
+    if export_format == "tiff":
+        file_path_name = directory / f"{name_pattern}.tiff"
         create_tiff(path=file_path_name, stack=data, voxel_size=voxel_size, layout=image.image_layout.value)
 
-    elif file_format == "zarr":
-        file_path_name = directory / f"{file_name}.zarr"
+    elif export_format == "zarr":
+        if key is None or key == "":
+            raise ValueError("Key is required for zarr format")
+
+        file_path_name = directory / f"{name_pattern}.zarr"
         create_zarr(
             path=file_path_name,
             stack=data,
             voxel_size=voxel_size,
-            key=custom_key,
+            key=key,
         )
 
-    elif file_format == "h5":
-        file_path_name = directory / f"{file_name}.h5"
-        create_h5(path=file_path_name, stack=data, voxel_size=voxel_size, key=custom_key)
+    elif export_format == "h5":
+        if key is None or key == "":
+            raise ValueError("Key is required for h5 format")
+
+        file_path_name = directory / f"{name_pattern}.h5"
+        create_h5(path=file_path_name, stack=data, voxel_size=voxel_size, key=key)
 
     else:
-        raise ValueError(f"File format {file_format} not recognized, should be tiff, h5 or zarr")
+        raise ValueError(f"Export format {export_format} not recognized, should be tiff, h5 or zarr")

plantseg/headless/headless.py

@@ -78,7 +78,7 @@ def parse_input_config(inputs_config: dict):
             list_input_keys[key] = parse_input_path(value)
             has_input_path = True
 
-        elif key.find("output_dir") != -1:
+        elif key.find("export_directory") != -1:
             single_input_keys[key] = output_directory(value)
             has_output_dir = True
 

plantseg/tasks/io_tasks.py

@@ -59,60 +59,46 @@ def import_image_task(
 @task_tracker(
     is_leaf=True,
     list_inputs={
-        "output_directory": TaskUserInput(
+        "export_directory": TaskUserInput(
             allowed_types=['str'],
             description="Output directory path where the image will be saved",
             headless_default=None,
             user_input_required=True,
         ),
-        "output_file_name": TaskUserInput(
-            allowed_types=['str', 'None'],
-            description="Output file name (if None, the image name will be used)",
-            headless_default=None,
-            user_input_required=False,
+        "name_pattern": TaskUserInput(
+            allowed_types=['str'], description="Output file name", headless_default=None, user_input_required=False
         ),
     },
 )
 def export_image_task(
     image: PlantSegImage,
-    output_directory: Path,
-    output_file_name: str | None = None,
-    custom_key_suffix: str | None = None,
+    export_directory: Path,
+    name_pattern: str = "{original_name}_export",
+    key: str | None = None,
     scale_to_origin: bool = True,
-    file_format: str = "tiff",
-    dtype: str = "uint16",
+    export_format: str = "tiff",
+    data_type: str = "uint16",
 ) -> None:
     """
     Task wrapper for saving an PlantSegImage object to disk.
 
     Args:
         image (PlantSegImage): input image to be saved to disk
-        output_directory (Path): output directory path where the image will be saved
-        output_file_name (str | None): output file name (if None, the image name will be used)
-        custom_key_suffix (str | None): custom key for the image. If format is .h5 or .zarr this key will be used
-            to create the dataset. If None, the semantic type will be used (raw, segmentation, predictio).
-            If the image is tiff, the custom key will be added to the file name as a suffix.
-            If None, the custom key will not be added.
-        scale_to_origin (bool): scale to origin
-        file_format (str): file format
-        dtype (str): data type
+        export_directory (Path): output directory path where the image will be saved
+        name_pattern (str): output file name pattern, can contain the {image_name} or {original_name} tokens
+            to be replaced in the final file name.
+        key (str | None): key for the image (used only for h5 and zarr formats).
+        scale_to_origin (bool): scale the voxel size to the original one
+        export_format (str): file format (tiff, h5, zarr)
+        data_type (str): data type to save the image.
     """
-    if output_file_name is None:
-        output_file_name = image.name
-
-    if custom_key_suffix is None:
-        if file_format == "tiff":
-            custom_key_suffix = ""
-        else:
-            custom_key_suffix = image.semantic_type.value
-
     save_image(
         image=image,
-        directory=output_directory,
-        file_name=output_file_name,
-        custom_key=custom_key_suffix,
+        export_directory=export_directory,
+        name_pattern=name_pattern,
+        key=key,
         scale_to_origin=scale_to_origin,
-        file_format=file_format,
-        dtype=dtype,
+        export_format=export_format,
+        data_type=data_type,
     )
     return None

plantseg/viewer_napari/containers.py

@@ -7,7 +7,8 @@
     widget_cropping,
     widget_docs,
     widget_dt_ws,
-    widget_export_stacks,
+    widget_export_headless_workflow,
+    widget_export_image,
     widget_filter_segmentation,
     widget_fix_over_under_segmentation_from_nuclei,
     widget_gaussian_smoothing,
@@ -34,7 +35,8 @@ def get_data_io_tab():
         widgets=[
             widget_docs,
             widget_open_file,
-            widget_export_stacks,
+            widget_export_image,
+            widget_export_headless_workflow,
             widget_show_info,
             widget_infos,
         ],

plantseg/viewer_napari/widgets/__init__.py

@@ -8,7 +8,13 @@
     widget_set_biggest_instance_to_zero,
 )
 from plantseg.viewer_napari.widgets.docs import widget_docs
-from plantseg.viewer_napari.widgets.io import widget_export_stacks, widget_infos, widget_open_file, widget_show_info
+from plantseg.viewer_napari.widgets.io import (
+    widget_export_headless_workflow,
+    widget_export_image,
+    widget_infos,
+    widget_open_file,
+    widget_show_info,
+)
 from plantseg.viewer_napari.widgets.prediction import widget_add_custom_model, widget_unet_prediction
 from plantseg.viewer_napari.widgets.proofreading import (
     widget_add_label_to_corrected,
@@ -31,7 +37,8 @@
     "widget_cropping",
     # IO
     "widget_open_file",
-    "widget_export_stacks",
+    "widget_export_image",
+    "widget_export_headless_workflow",
     "widget_show_info",
     "widget_infos",
     # Main - Prediction

plantseg/viewer_napari/widgets/dataprocessing.py

@@ -1,9 +1,7 @@
-from concurrent.futures import Future
 from enum import Enum
 
 from magicgui import magicgui
 from napari.layers import Image, Labels, Layer, Shapes
-from napari.types import LayerDataTuple
 
 from plantseg.core.image import PlantSegImage
 from plantseg.core.voxelsize import VoxelSize
@@ -49,9 +47,7 @@
         "tooltip": "To allow toggle the update of other widgets in unit tests; invisible to users.",
     },
 )
-def widget_gaussian_smoothing(
-    image: Image, sigma: float = 1.0, update_other_widgets: bool = True
-) -> Future[LayerDataTuple]:
+def widget_gaussian_smoothing(image: Image, sigma: float = 1.0, update_other_widgets: bool = True) -> None:
     """Apply Gaussian smoothing to an image layer."""
 
     ps_image = PlantSegImage.from_napari_layer(image)
@@ -103,7 +99,7 @@ def widget_cropping(
     crop_roi: Shapes | None = None,
     crop_z: tuple[int, int] = (0, 100),
     update_other_widgets: bool = True,
-) -> Future[LayerDataTuple]:
+) -> None:
     if crop_roi is not None:
         assert len(crop_roi.shape_type) == 1, "Only one rectangle should be used for cropping"
         assert crop_roi.shape_type[0] == "rectangle", "Only a rectangle shape should be used for cropping"
@@ -242,7 +238,7 @@ def widget_rescaling(
     reference_shape: tuple[int, int, int] = (1, 1, 1),
     order: int = 0,
     update_other_widgets: bool = True,
-) -> Future[LayerDataTuple]:
+) -> None:
     """Rescale an image or label layer."""
 
     if isinstance(image, Image) or isinstance(image, Labels):
@@ -437,7 +433,7 @@ def _on_rescale_order_changed(order):
 )
 def widget_remove_false_positives_by_foreground(
     segmentation: Labels, foreground: Image, threshold: float = 0.5
-) -> Future[LayerDataTuple]:
+) -> None:
     """Remove false positives from a segmentation layer using a foreground probability layer."""
 
     ps_segmentation = PlantSegImage.from_napari_layer(segmentation)
@@ -487,7 +483,7 @@ def widget_fix_over_under_segmentation_from_nuclei(
     boundary_pmaps: Image | None = None,
     threshold=(33, 66),
     quantile=(0.3, 99.9),
-) -> Future[LayerDataTuple]:
+) -> None:
     """
     Widget interface for correcting over- and under-segmentation of cells based on nuclei segmentation.
 
@@ -537,7 +533,7 @@ def widget_fix_over_under_segmentation_from_nuclei(
 
 
 @magicgui(
-    call_button=f"Relabel Instances",
+    call_button="Relabel Instances",
     segmentation={
         "label": "Segmentation",
         "tooltip": "Segmentation can be any label layer.",
@@ -552,7 +548,7 @@ def widget_fix_over_under_segmentation_from_nuclei(
 def widget_relabel(
     segmentation: Labels,
     background: int | None = None,
-) -> Future[LayerDataTuple]:
+) -> None:
     """Relabel an image layer."""
 
     ps_image = PlantSegImage.from_napari_layer(segmentation)
@@ -605,7 +601,7 @@ def _on_relabel_segmentation_changed(segmentation: Labels):
 def widget_set_biggest_instance_to_zero(
     segmentation: Labels,
     instance_could_be_zero: bool = False,
-) -> Future[LayerDataTuple]:
+) -> None:
     """Set the biggest instance to zero in a label layer."""
 
     ps_image = PlantSegImage.from_napari_layer(segmentation)