Update tests (#97)

* Fix default mutables in config

* Re-enable CRF optional dependency

* Fix layer set to invisible causing test errors

* Update README.md

* Update credits for example data

examples/README.md

@@ -1,8 +1,8 @@
 # Testing CellSeg3D on demo data
 
 Here is a very small volume (from [IDR project 853](https://idr.openmicroscopy.org/webclient/?show=project-853)) to test on.
+All credits to the original authors of the data.
 You can install, launch `napari`, activate the CellSeg3D plugin app, and drag & drop this volume into the canvas.
-Then, for example, run `inference` with one of our models.
-
-See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) for more details.
+Then, for example, run the `Inference` module with one of our models.
 
+See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) for more details.

napari_cellseg3d/code_plugins/plugin_crop.py

@@ -75,7 +75,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None):
 
         self.create_new_layer = ui.CheckBox("Create new layers")
         self.create_new_layer.setToolTip(
-            'Use this to create a new layer everytime you start cropping, so you can "zoom in" your volume'
+            'Use this to create a new layer every time you start cropping, so you can "zoom in" your volume'
         )
 
         self._viewer.layers.events.inserted.connect(self._check_image_list)
@@ -324,8 +324,9 @@ def _start(self):
 
             if self.crop_second_image:
                 self.image_layer2 = self._add_isotropic_layer(
-                    self.image_layer2, visible=False
+                    self.image_layer2
                 )
+                self.image_layer2.visible = False
         else:
             self.image_layer1.opacity = 0.7
             self.image_layer1.colormap = "inferno"

napari_cellseg3d/config.py

@@ -1,6 +1,6 @@
 """Module to store configuration parameters for napari_cellseg3d."""
 import datetime
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 from typing import List, Optional
 
@@ -182,9 +182,9 @@ class PostProcessConfig:
         instance (InstanceSegConfig): instance segmentation config
     """
 
-    zoom: Zoom = Zoom()
-    thresholding: Thresholding = Thresholding()
-    instance: InstanceSegConfig = InstanceSegConfig()
+    zoom: Zoom = field(default_factory=Zoom)
+    thresholding: Thresholding = field(default_factory=Thresholding)
+    instance: InstanceSegConfig = field(default_factory=InstanceSegConfig)
     artifact_removal: bool = False
     artifact_removal_size: int = 500
 
@@ -265,16 +265,16 @@ class InferenceWorkerConfig:
     """
 
     device: str = "cpu"
-    model_info: ModelInfo = ModelInfo()
-    weights_config: WeightsInfo = WeightsInfo()
+    model_info: ModelInfo = field(default_factory=ModelInfo)
+    weights_config: WeightsInfo = field(default_factory=WeightsInfo)
     results_path: str = str(Path.home() / "cellseg3d" / "inference")
     filetype: str = ".tif"
     keep_on_cpu: bool = False
     compute_stats: bool = False
-    post_process_config: PostProcessConfig = PostProcessConfig()
-    sliding_window_config: SlidingWindowConfig = SlidingWindowConfig()
+    post_process_config: PostProcessConfig = field(default_factory=PostProcessConfig)
+    sliding_window_config: SlidingWindowConfig = field(default_factory=SlidingWindowConfig)
     use_crf: bool = False
-    crf_config: CRFConfig = CRFConfig()
+    crf_config: CRFConfig = field(default_factory=CRFConfig)
 
     images_filepaths: List[str] = None
     layer: napari.layers.Layer = None
@@ -329,10 +329,10 @@ class TrainingWorkerConfig:
     learning_rate: np.float64 = 1e-3
     validation_interval: int = 2
     batch_size: int = 1
-    deterministic_config: DeterministicConfig = DeterministicConfig()
+    deterministic_config: DeterministicConfig = field(default_factory=DeterministicConfig)
     scheduler_factor: float = 0.5
     scheduler_patience: int = 10
-    weights_info: WeightsInfo = WeightsInfo()
+    weights_info: WeightsInfo = field(default_factory=WeightsInfo)
     # data params
     results_path_folder: str = str(Path.home() / "cellseg3d" / "training")
     sampling: bool = False

pyproject.toml

@@ -123,9 +123,9 @@ profile = "black"
 line_length = 79
 
 [project.optional-dependencies]
-#crf = [
-#   "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
-#]
+crf = [
+   "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
+]
 pyqt5 = [
     "pyqt5",
 ]
@@ -164,7 +164,7 @@ test = [
     "coverage",
     "tox",
     "twine",
-#   "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
+    "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
     "onnx",
     "onnxruntime",
 ]