remove usage of skimage in morphology

eolearn/geometry/morphology.py

@@ -10,11 +10,10 @@
 
 import itertools as it
 from enum import Enum
-from typing import Callable
+from functools import partial
 
+import cv2
 import numpy as np
-import skimage.filters.rank
-import skimage.morphology
 
 from eolearn.core import EOPatch, EOTask, MapFeatureTask
 from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec
@@ -44,7 +43,7 @@ def __init__(
         parsed_mask_feature = parse_renamed_feature(mask_feature, allowed_feature_types=lambda fty: fty.is_array())
 
         self.mask_type, self.mask_name, self.new_mask_name = parsed_mask_feature
-        self.disk = skimage.morphology.disk(disk_radius)
+        self.disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (disk_radius, disk_radius))
         self.erode_labels = erode_labels
         self.no_data_label = no_data_label
 
@@ -57,7 +56,7 @@ def execute(self, eopatch: EOPatch) -> EOPatch:
         erode_labels = set(erode_labels) - {self.no_data_label}
         other_labels = set(all_labels) - set(erode_labels) - {self.no_data_label}
 
-        eroded_masks = [skimage.morphology.binary_erosion(feature_array == label, self.disk) for label in erode_labels]
+        eroded_masks = [cv2.erode((feature_array == label).astype(np.uint8), self.disk) for label in erode_labels]
         other_masks = [feature_array == label for label in other_labels]
 
         merged_mask = np.logical_or.reduce(eroded_masks + other_masks, axis=0)
@@ -75,20 +74,18 @@ class MorphologicalOperations(Enum):
     CLOSING = "closing"
     DILATION = "dilation"
     EROSION = "erosion"
-    MEDIAN = "median"
 
     @classmethod
-    def get_operation(cls, morph_type: MorphologicalOperations) -> Callable:
+    def get_operation(cls, morph_type: MorphologicalOperations) -> int:
         """Maps morphological operation type to function
 
         :param morph_type: Morphological operation type
         """
         return {
-            cls.OPENING: skimage.morphology.opening,
-            cls.CLOSING: skimage.morphology.closing,
-            cls.DILATION: skimage.morphology.dilation,
-            cls.EROSION: skimage.morphology.erosion,
-            cls.MEDIAN: skimage.filters.rank.median,
+            cls.OPENING: cv2.MORPH_OPEN,
+            cls.CLOSING: cv2.MORPH_CLOSE,
+            cls.DILATION: cv2.MORPH_DILATE,
+            cls.EROSION: cv2.MORPH_ERODE,
         }[morph_type]
 
 
@@ -103,15 +100,7 @@ def get_disk(radius: int) -> np.ndarray:
         :param radius: Radius of disk
         :return: The structuring element where elements of the neighborhood are 1 and 0 otherwise.
         """
-        return skimage.morphology.disk(radius)
-
-    @staticmethod
-    def get_diamond(radius: int) -> np.ndarray:
-        """
-        :param radius: Radius of diamond
-        :return: The structuring element where elements of the neighborhood are 1 and 0 otherwise.
-        """
-        return skimage.morphology.diamond(radius)
+        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (radius, radius))
 
     @staticmethod
     def get_rectangle(width: int, height: int) -> np.ndarray:
@@ -120,15 +109,15 @@ def get_rectangle(width: int, height: int) -> np.ndarray:
         :param height: Height of rectangle
         :return: A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood.
         """
-        return skimage.morphology.rectangle(width, height)
+        return cv2.getStructuringElement(cv2.MORPH_RECT, (height, width))
 
     @staticmethod
     def get_square(width: int) -> np.ndarray:
         """
         :param width: Size of square
         :return: A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood.
         """
-        return skimage.morphology.square(width)
+        return cv2.getStructuringElement(cv2.MORPH_RECT, (width, width))
 
 
 class MorphologicalFilterTask(MapFeatureTask):
@@ -139,7 +128,7 @@ def __init__(
         input_features: FeaturesSpecification,
         output_features: FeaturesSpecification | None = None,
         *,
-        morph_operation: MorphologicalOperations | Callable,
+        morph_operation: MorphologicalOperations,
         struct_elem: np.ndarray | None = None,
     ):
         """
@@ -153,21 +142,19 @@ def __init__(
             output_features = input_features
         super().__init__(input_features, output_features)
 
-        if isinstance(morph_operation, MorphologicalOperations):
-            self.morph_operation = MorphologicalOperations.get_operation(morph_operation)
-        else:
-            self.morph_operation = morph_operation
+        self.morph_operation = MorphologicalOperations.get_operation(morph_operation)
         self.struct_elem = struct_elem
 
     def map_method(self, feature: np.ndarray) -> np.ndarray:
         """Applies the morphological operation to a raster feature."""
         feature = feature.copy()
+        morph_func = partial(cv2.morphologyEx, kernel=self.struct_elem, op=self.morph_operation)
         if feature.ndim == 3:
             for channel in range(feature.shape[2]):
-                feature[..., channel] = self.morph_operation(feature[..., channel], self.struct_elem)
+                feature[..., channel] = morph_func(feature[..., channel])
         elif feature.ndim == 4:
             for time, channel in it.product(range(feature.shape[0]), range(feature.shape[3])):
-                feature[time, ..., channel] = self.morph_operation(feature[time, ..., channel], self.struct_elem)
+                feature[time, ..., channel] = morph_func(feature[time, ..., channel])
         else:
             raise ValueError(f"Invalid number of dimensions: {feature.ndim}")