Activate windows tests

.github/workflows/run_tests.yaml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest]
+        os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ["3.11"]
     steps:
       - name: Checkout
@@ -27,7 +27,7 @@ jobs:
       - name: Setup micromamba
         uses: mamba-org/setup-micromamba@v1
         with:
-          environment-file: ${{ runner.os == 'Windows' && 'environment_cpu_win.yaml' || 'environment.yaml' }}
+          environment-file: "environment.yaml"
           create-args: >-
             python=${{ matrix.python-version }}
 

doc/start_page.md

@@ -18,8 +18,8 @@ Please cite our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2024.
 
 ## Requirements & Installation
 
-SynapseNet was developed and tested on Linux. It is possible to install and use it on Mac or Windows, but we have not extensively tested this.
-Furthermore, SynapseNet requires a GPU for segmentation of 3D volumes.
+SynapseNet was tested on all operating systems (Linux, Mac, Windows).
+SynapseNet requires a GPU or a Macbook with M chipset for the segmentation of 3D volumes.
 
 You need a [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) or [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) installation. Follow the instruction at the respective links if you have installed neither. We assume you have `conda` for the rest of the instructions. After installing it, you can use the `conda` command.
 

environment.yaml

@@ -7,6 +7,7 @@ dependencies:
     - kornia
     - magicgui
     - napari
+    - nifty >= 1.2.2
     - pip
     - pyqt
     - python-elf

test/test_cli.py

@@ -1,5 +1,8 @@
 import os
+import platform
+import sys
 import unittest
+
 from subprocess import run
 from shutil import rmtree
 
@@ -9,8 +12,9 @@
 from synapse_net.sample_data import get_sample_data
 
 
+@unittest.skipIf(platform.system() == "Windows", "CLI does not work on Windows")
 class TestCLI(unittest.TestCase):
-    tmp_dir = "./tmp"
+    tmp_dir = "tmp"
 
     def setUp(self):
         self.data_path = get_sample_data("tem_2d")
@@ -41,15 +45,28 @@ def check_segmentation_result(self):
         # napari.run()
 
     def test_segmentation_cli(self):
-        cmd = ["synapse_net.run_segmentation", "-i", self.data_path, "-o", self.tmp_dir, "-m", "vesicles_2d"]
+        if platform.system() == "Windows":
+            cmd = [
+                sys.executable, "-m",  "synapse_net.run_segmentation",
+                "-i", self.data_path, "-o", self.tmp_dir, "-m", "vesicles_2d"
+            ]
+        else:
+            cmd = ["synapse_net.run_segmentation", "-i", self.data_path, "-o", self.tmp_dir, "-m", "vesicles_2d"]
         run(cmd)
         self.check_segmentation_result()
 
     def test_segmentation_cli_with_scale(self):
-        cmd = [
-            "synapse_net.run_segmentation", "-i", self.data_path, "-o", self.tmp_dir, "-m", "vesicles_2d",
-            "--scale", "0.5"
-        ]
+        if platform.system() == "Windows":
+            cmd = [
+                sys.executable, "-m", "synapse_net.run_segmentation",
+                "-i", self.data_path, "-o", self.tmp_dir, "-m", "vesicles_2d",
+                "--scale", "0.5"
+            ]
+        else:
+            cmd = [
+                "synapse_net.run_segmentation", "-i", self.data_path, "-o", self.tmp_dir, "-m", "vesicles_2d",
+                "--scale", "0.5"
+            ]
         run(cmd)
         self.check_segmentation_result()
 

test/test_inference.py

@@ -0,0 +1,51 @@
+import os
+import unittest
+from functools import partial
+from shutil import rmtree
+
+import imageio.v3 as imageio
+from synapse_net.file_utils import read_mrc
+from synapse_net.sample_data import get_sample_data
+
+
+class TestInference(unittest.TestCase):
+    tmp_dir = "tmp"
+    model_type = "vesicles_2d"
+    tiling = {"tile": {"z": 1, "y": 512, "x": 512}, "halo": {"z": 0, "y": 32, "x": 32}}
+
+    def setUp(self):
+        self.data_path = get_sample_data("tem_2d")
+        os.makedirs(self.tmp_dir, exist_ok=True)
+
+    def tearDown(self):
+        try:
+            rmtree(self.tmp_dir)
+        except OSError:
+            pass
+
+    def test_run_segmentation(self):
+        from synapse_net.inference import run_segmentation, get_model
+
+        image, _ = read_mrc(self.data_path)
+        model = get_model(self.model_type)
+        seg = run_segmentation(image, model, model_type=self.model_type, tiling=self.tiling)
+        self.assertEqual(image.shape, seg.shape)
+
+    def test_segmentation_with_inference_helper(self):
+        from synapse_net.inference import run_segmentation, get_model
+        from synapse_net.inference.util import inference_helper
+
+        model = get_model(self.model_type)
+        segmentation_function = partial(
+            run_segmentation, model=model, model_type=self.model_type, verbose=False, tiling=self.tiling,
+        )
+        inference_helper(self.data_path, self.tmp_dir, segmentation_function, data_ext=".mrc")
+        expected_output_path = os.path.join(self.tmp_dir, "tem_2d_prediction.tif")
+        self.assertTrue(os.path.exists(expected_output_path))
+        seg = imageio.imread(expected_output_path)
+        image, _ = read_mrc(self.data_path)
+        self.assertEqual(image.shape, seg.shape)
+
+
+if __name__ == "__main__":
+    unittest.main()