Update pyproject.toml; minimal conda for m1 (#38)

* Update pyproject.toml

- make itk and pyqt5 "optional dependency, as for m1 they cause issues!

* conda file for m1

- then run: `conda install -c anaconda pyqt` in the env

* Added "Save location" tooltip

* Packaging fixes

- Remove dask and itk requirements
- Comment duplicate sections in setup.cfg

* Reduced duplicates in setup .cfg

* Clean up pyproject

* Update requirements.txt

* Update README.md

* Update README.md

* Latest pre-commit hooks

* Run full suite of pre-commit checks

---------

Co-authored-by: C-Achard <[email protected]>

Author: MMathisLab

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
     rev: 5.12.0
     hooks:
       - id: isort
-        args: ["--profile", "black", --line-length=72]
+        args: ["--profile", "black", --line-length=79]
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
     rev: 'v0.0.262'

README.md

@@ -29,27 +29,41 @@ A napari plugin for 3D cell segmentation: training, inference, and data review.
 
 ## Installation
 
-Note : we recommend using conda to create a new environment for the plugin.
+**Note** : we recommend using conda to create a new environment for the plugin.
+**M1 Mac users, please see the [M1 install section](#m1-mac-users)**
 
     conda create --name napari-cellseg3d python=3.8
     conda activate napari-cellseg3d
 
 You can install `napari-cellseg3d` via [pip]:
 
-    pip install napari-cellseg3d
+    pip install napari-cellseg3d[all]
 
 OR directly via [napari-hub]:
 
 - Install napari from pip with `pip install "napari[all]"`,
 then from the “Plugins” menu within the napari application, select “Install/Uninstall Package(s)...”
 - Copy `napari-cellseg3d` and paste it where it says “Install by name/url…”
 - Click “Install”
+- Restart napari
+
+### M1 Mac users
+
+Create a new conda env using the provided conda/napari_cellseg3d_m1.yml file :
+
+        conda env create -f conda/napari_cellseg3d_m1.yml
+        conda activate napari_cellseg3d_m1
+
+Then install PyQt5 from conda separately :
+
+        conda install -c anaconda PyQt5
+
 
 ## Documentation
 
 Available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
 
-You can also generate docs by running ``make html`` in the docs folder.
+You can also generate docs by running ``make html`` in the docs/ folder.
 
 ## Usage
 

conda/napari_cellseg3d_m1.yml

@@ -0,0 +1,26 @@
+name: napari_cellseg3d_m1
+channels:
+  - anaconda
+  - conda-forge
+  - defaults
+dependencies:
+    - python=3.8
+    - pip
+    - pip:
+        - numpy
+        - napari>=0.4.14
+        - QtPy
+        - opencv-python>=4.5.5
+        - scikit-image>=0.19.2
+        - matplotlib>=3.4.1
+        - tifffile>=2022.2.9
+        - imageio-ffmpeg>=0.4.5
+        - torch>=1.11
+        - monai[nibabel,einops]>=0.9.0
+        - tqdm
+        - nibabel
+        - scikit-image
+        - pillow
+        - tqdm
+        - matplotlib
+        - vispy>=0.9.6

napari_cellseg3d/code_models/model_workers.py

@@ -457,6 +457,7 @@ def model_output(
         #         self.config.model_info.get_model().get_output(model, inputs)
         #     )
 
+
         def model_output(inputs):
             return post_process_transforms(
                 self.config.model_info.get_model().get_output(model, inputs)

napari_cellseg3d/code_plugins/plugin_base.py

@@ -118,6 +118,7 @@ def show_menu(_, event):
 
     def _build_io_panel(self):
         self.io_panel = ui.GroupedWidget("Data")
+        self.save_label = ui.make_label("Save location :", parent=self)
 
         # self.io_panel.setToolTip("IO Panel")
 
@@ -130,6 +131,7 @@ def _build_io_panel(self):
                 self.filetype_choice,
                 self.image_filewidget,
                 self.labels_filewidget,
+                self.save_label,
                 self.results_filewidget,
             ],
         )

napari_cellseg3d/code_plugins/plugin_crop.py

@@ -440,8 +440,8 @@ def _add_crop_sliders(
         for i in range(len(crop_sizes)):
             if crop_sizes[i] > im1_stack.shape[i]:
                 crop_sizes[i] = im1_stack.shape[i]
-                warnings.warn(
-                    f"WARNING : Crop dimension in axis {i} was too large at {crop_sizes[i]}, it was set to {im1_stack.shape[i]}"
+                logger.warning(
+                    f"Crop dimension in axis {i} was too large at {crop_sizes[i]}, it was set to {im1_stack.shape[i]}"
                 )
 
         cropx, cropy, cropz = crop_sizes

napari_cellseg3d/dev_scripts/convert.py

@@ -2,7 +2,7 @@
 import os
 
 import numpy as np
-from dask_image.imread import imread
+from tifffile import imread
 from tifffile import imwrite
 
 # input_seg_path = "C:/Users/Cyril/Desktop/Proj_bachelor/code/pytorch-test3dunet/cropped_visual/train/lab"
@@ -19,8 +19,8 @@
     filenames.append(os.path.basename(filename))
     # print(os.path.basename(filename))
 for file in paths:
-    img = imread(file)
-    image = img.compute()
+    image = imread(file)
+    # image = img.compute()
 
     image[image >= 1] = 1
     image = image.astype(np.uint16)

napari_cellseg3d/dev_scripts/view_brain.py

@@ -1,5 +1,5 @@
 import napari
-from dask_image.imread import imread
+from tifffile import imread
 
 y = imread("/Users/maximevidal/Documents/3drawdata/wholebrain.tif")
 

napari_cellseg3d/dev_scripts/view_sample.py

@@ -1,5 +1,5 @@
 import napari
-from dask_image.imread import imread
+from tifffile import imread
 
 # Visual
 x = imread(

napari_cellseg3d/utils.py

@@ -4,7 +4,6 @@
 from pathlib import Path
 
 import numpy as np
-from dask_image.imread import imread as dask_imread
 from pandas import DataFrame
 from pandas import Series
 from skimage import io
@@ -301,7 +300,7 @@ def check_csv(project_path, ext):
         filename_pattern_original = Path(project_path) / Path(
             f"dataset/Original_size/Original/*{ext}"
         )
-        images_original = dask_imread(filename_pattern_original)
+        images_original = tfl_imread(filename_pattern_original)
         z, y, x = images_original.shape
         record = Series(
             [
@@ -426,7 +425,9 @@ def get_time_filepath():
     return "{:%H_%M_%S}".format(datetime.now())
 
 
-def load_images(dir_or_path, filetype="", as_folder: bool = False):
+def load_images(
+    dir_or_path, filetype="", as_folder: bool = False
+):  # TODO(cyril):refactor w/o as_folder
     """Loads the images in ``directory``, with different behaviour depending on ``filetype`` and ``as_folder``
 
     * If ``as_folder`` is **False**, will load the path as a single 3D **.tif** image.
@@ -446,7 +447,7 @@ def load_images(dir_or_path, filetype="", as_folder: bool = False):
         as_folder (bool): Whether to load a folder of images as stack or a single 3D image
 
     Returns:
-        dask.array.Array: dask array with loaded images
+        np.array: array with loaded images
     """
 
     if not as_folder:
@@ -459,7 +460,12 @@ def load_images(dir_or_path, filetype="", as_folder: bool = False):
         raise ValueError("If loading as a folder, filetype must be specified")
 
     if as_folder:
-        images_original = dask_imread(filename_pattern_original)
+        try:
+            images_original = tfl_imread(filename_pattern_original)
+        except ValueError:
+            LOGGER.error(
+                "Loading a stack this way is no longer supported. Use napari to load a stack."
+            )
     else:
         images_original = tfl_imread(
             filename_pattern_original