Add type hints (#15)

* Add typing info

* Add type hints

* Fix linting issues

* Fix workflow

* Fix workflow

* Fix workflow

* Fix workflow

* Fix workflow

* Fix workflow

* Fix dependencies

* Fix bug

* Fix Python 3.8 compatibility

* Fix typing

* Fix workflow

* Fix workflow

* Fix bug

* Fix bug

* Add more type hints

* [skip ci] Fix bug in `ObjectMeasureAdapter`

The correspondance cache is removed, since correspondances may differ between subsequent calls, now that `ObjectMeasureAdapter` is also used for region-based measures.

* [skip ci] Fix type hints

* Fix bug

* Fix issues

* Fix linting issues

* Add type hints for regional.py

* Add Literal types

* Update type hints

* Add type hints for contour.py

* Add type hints for detection.py

* Add `python>=3.8` requirement

* Fix requirements.txt

* Fix bug

* Add type hints for parallel.py

.flake8

@@ -1,6 +1,6 @@
 [flake8]
 
-extend-ignore = E221,E211,E222,E202,F541,E201
+extend-ignore = E221,E211,E222,E202,F541,E201,E203
 
 per-file-ignores =
     segmetrics/__init__.py:F401

.github/workflows/testsuite.yml

@@ -57,6 +57,31 @@ jobs:
           isort segmetrics --check-only
           isort tests --check-only
 
+  type_checking:
+
+    name: Type checking
+    runs-on: ubuntu-latest
+
+    steps:
+
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install mypy
+
+      - name: Run mypy
+        shell: bash
+        run: |
+          mypy --config-file .mypy.ini --ignore-missing-imports segmetrics
+
   run_testsuite:
 
     name: Tests

.mypy.ini

@@ -0,0 +1,3 @@
+[mypy]
+
+disable_error_code = import-untyped

requirements.txt

@@ -1,4 +1,4 @@
-numpy>=1.18
+numpy>=1.20
 scipy
 scikit-image>=0.18
 scikit-learn

segmetrics/contour.py

@@ -1,23 +1,33 @@
+from typing import (
+    List,
+    Sequence,
+)
+
 import numpy as np
 from scipy import ndimage
 from skimage import morphology as morph
 
 from segmetrics.measure import (
+    CorrespondanceFunction,
     ImageMeasureMixin,
     Measure,
 )
+from segmetrics.typing import (
+    BinaryImage,
+    LabelImage,
+)
 
 
-def _compute_binary_contour(mask, width=1):
+def _compute_binary_contour(mask: BinaryImage, width: int = 1) -> BinaryImage:
     dilation = morph.binary_dilation(mask, morph.disk(width))
     return np.logical_and(dilation, np.logical_not(mask))
 
 
-def _quantile_max(quantile, values):
+def _quantile_max(quantile: float, values: Sequence[float]) -> float:
     if quantile == 1:
         return np.max(values)
     else:
-        values = np.sort(values)
+        values = list(sorted(values))
         return values[int(quantile * (len(values) - 1))]
 
 
@@ -27,7 +37,12 @@ class ContourMeasure(ImageMeasureMixin, Measure):
     binary volumes (images).
     """
 
-    def __init__(self, *args, correspondance_function='min', **kwargs):
+    def __init__(
+        self,
+        *args,
+        correspondance_function: CorrespondanceFunction = 'min',
+        **kwargs,
+    ) -> None:
         super().__init__(
             *args,
             correspondance_function=correspondance_function,
@@ -58,18 +73,18 @@ class Hausdorff(ContourMeasure):
       distance." International Journal of computer vision 24.3 (1997): 251-270.
     """
 
-    def __init__(self, quantile=1, **kwargs):
+    def __init__(self, quantile: float = 1, **kwargs):
         super().__init__(**kwargs)
         assert 0 < quantile <= 1
         self.quantile = quantile
 
-    def set_expected(self, expected):
+    def set_expected(self, expected: LabelImage) -> None:
         self.expected_contour = _compute_binary_contour(expected > 0)
         self.expected_contour_distance_map = ndimage.distance_transform_edt(
             np.logical_not(self.expected_contour)
         )
 
-    def compute(self, actual):
+    def compute(self, actual: LabelImage) -> List[float]:
         actual_contour = _compute_binary_contour(actual > 0)
         if not self.expected_contour.any() or not actual_contour.any():
             return []
@@ -80,13 +95,13 @@ def compute(self, actual):
             )
         ]
 
-    def default_name(self):
+    def default_name(self) -> str:
         if self.quantile == 1:
             return 'HSD'
         else:
             return f'HSD (Q={self.quantile:g})'
 
-    def _quantile_max(self, values):
+    def _quantile_max(self, values: Sequence[float]) -> float:
         return _quantile_max(self.quantile, values)
 
 
@@ -114,17 +129,17 @@ class NSD(ContourMeasure):
     :math:`1`. Lower values correspond to better segmentation performance.
     """
 
-    def set_expected(self, expected):
-        self.expected = (expected > 0)
-        self.expected_contour = _compute_binary_contour(self.expected)
+    def set_expected(self, expected: LabelImage) -> None:
+        self.expected_binary: BinaryImage = (expected > 0)
+        self.expected_contour = _compute_binary_contour(self.expected_binary)
         self.expected_contour_distance_map = ndimage.distance_transform_edt(
             np.logical_not(self.expected_contour)
         )
 
-    def compute(self, actual):
-        actual = (actual > 0)
-        union         = np.logical_or(self.expected, actual)
-        intersection  = np.logical_and(self.expected, actual)
+    def compute(self, actual: LabelImage) -> List[float]:
+        actual_binary: BinaryImage = (actual > 0)
+        union         = np.logical_or(self.expected_binary, actual_binary)
+        intersection  = np.logical_and(self.expected_binary, actual_binary)
         denominator   = self.expected_contour_distance_map[union].sum()
         nominator     = self.expected_contour_distance_map[
             np.logical_and(union, np.logical_not(intersection))

segmetrics/detection.py

@@ -1,22 +1,42 @@
-import numpy as np
+from typing import (
+    Any,
+    Dict,
+    List,
+    Optional,
+    Set,
+)
+
+import numpy as np
 
 from segmetrics.measure import Measure
+from segmetrics.typing import (
+    BinaryImage,
+    LabelImage,
+)
 
 
-def _assign(assignments, key, value):
+def _assign(
+    assignments: Dict,
+    key: Any,
+    value: Any,
+) -> None:
     if key not in assignments:
         assignments[key] = set()
     assignments[key] |= {value}
 
 
-def _compute_seg_by_ref_assignments(seg, ref, include_background=False):
-    seg_by_ref = {}
+def _compute_seg_by_ref_assignments(
+    seg: LabelImage,
+    ref: LabelImage,
+    include_background: bool = False,
+) -> Dict[int, Set[int]]:
+    seg_by_ref: Dict[int, Set[int]] = dict()
 
     if include_background:
         seg_by_ref[0] = set()
 
     for seg_label in range(1, seg.max() + 1):
-        seg_cc = (seg == seg_label)
+        seg_cc: BinaryImage = (seg == seg_label)
 
         if not seg_cc.any():
             continue
@@ -27,7 +47,12 @@ def _compute_seg_by_ref_assignments(seg, ref, include_background=False):
     return seg_by_ref
 
 
-def _compute_ref_by_seg_assignments(seg, ref, *args, **kwargs):
+def _compute_ref_by_seg_assignments(
+    seg: LabelImage,
+    ref: LabelImage,
+    *args,
+    **kwargs,
+) -> Dict[int, Set[int]]:
     return _compute_seg_by_ref_assignments(ref, seg, *args, **kwargs)
 
 
@@ -42,14 +67,14 @@ class FalseSplit(Measure):
       algorithms," in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.
     """
 
-    def compute(self, actual):
+    def compute(self, actual: LabelImage) -> List[float]:
         seg_by_ref = _compute_seg_by_ref_assignments(actual, self.expected)
         return [
             sum(len(seg_by_ref[ref_label]) > 1
                 for ref_label in seg_by_ref.keys() if ref_label > 0)
         ]
 
-    def default_name(self):
+    def default_name(self) -> str:
         return 'Split'
 
 
@@ -64,14 +89,14 @@ class FalseMerge(Measure):
       algorithms," in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.
     """
 
-    def compute(self, actual):
+    def compute(self, actual: LabelImage) -> List[float]:
         ref_by_seg = _compute_ref_by_seg_assignments(actual, self.expected)
         return [
             sum(len(ref_by_seg[seg_label]) > 1
                 for seg_label in ref_by_seg.keys() if seg_label > 0)
         ]
 
-    def default_name(self):
+    def default_name(self) -> str:
         return 'Merge'
 
 
@@ -86,11 +111,11 @@ class FalsePositive(Measure):
       algorithms," in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.
     """
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs) -> None:
         super().__init__(**kwargs)
-        self.result = None
+        self.result: Optional[LabelImage] = None
 
-    def compute(self, actual):
+    def compute(self, actual: LabelImage) -> List[float]:
         seg_by_ref = _compute_seg_by_ref_assignments(
             actual,
             self.expected,
@@ -101,7 +126,7 @@ def compute(self, actual):
             self.result[actual == seg_label] = seg_label
         return [len(seg_by_ref[0])]
 
-    def default_name(self):
+    def default_name(self) -> str:
         return 'Spurious'
 
 
@@ -116,11 +141,11 @@ class FalseNegative(Measure):
       algorithms," in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.
     """
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs) -> None:
         super().__init__(**kwargs)
-        self.result = None
+        self.result: Optional[LabelImage] = None
 
-    def compute(self, actual):
+    def compute(self, actual: LabelImage) -> List[float]:
         ref_by_seg = _compute_ref_by_seg_assignments(
             actual,
             self.expected,
@@ -131,5 +156,5 @@ def compute(self, actual):
             self.result[self.expected == ref_label] = ref_label
         return [len(ref_by_seg[0])]
 
-    def default_name(self):
+    def default_name(self) -> str:
         return 'Missing'