rm wt

Author: macromeer

src/napari_tmidas/_tests/test_skimage_filters.py

@@ -8,7 +8,6 @@
     resize_mask,
     rolling_ball_background,
     simple_thresholding,
-    white_tophat,
 )
 
 
@@ -89,20 +88,6 @@ def test_simple_thresholding_different_thresholds(self):
 class TestBrightRegionExtraction:
     """Test suite for bright region extraction functions"""
 
-    def test_white_tophat_basic(self):
-        """Test white top-hat transform extracts bright features"""
-        # Create image with bright spot on dark background
-        image = np.zeros((100, 100), dtype=np.uint8)
-        image[40:60, 40:60] = 200  # Bright square
-        image[45:55, 45:55] = 255  # Brighter center
-
-        result = white_tophat(image, footprint_size=15)
-
-        # Result should have bright features extracted
-        assert result.shape == image.shape
-        assert result.max() > 0  # Should have some bright regions
-        assert result.sum() < image.sum()  # Background removed
-
     def test_percentile_threshold_original(self):
         """Test percentile thresholding with original values"""
         # Create image with gradient

src/napari_tmidas/processing_functions/skimage_filters.py

@@ -521,48 +521,6 @@ def convert_to_uint8(image: np.ndarray) -> np.ndarray:
 
 if SKIMAGE_AVAILABLE:
 
-    @BatchProcessingRegistry.register(
-        name="White Top-Hat (Extract Bright Features)",
-        suffix="_tophat",
-        description="Extract bright features from background using morphological white top-hat transform",
-        parameters={
-            "footprint_size": {
-                "type": int,
-                "default": 15,
-                "min": 3,
-                "max": 101,
-                "description": "Size of structuring element (larger removes bigger background features)",
-            }
-        },
-    )
-    def white_tophat(
-        image: np.ndarray, footprint_size: int = 15
-    ) -> np.ndarray:
-        """
-        Apply white top-hat transform to extract bright regions.
-
-        The white top-hat transform extracts bright features that are smaller than
-        the structuring element. It works by subtracting a morphological opening
-        from the original image, effectively removing background and keeping only
-        bright peaks and spots.
-
-        Parameters:
-        -----------
-        image : numpy.ndarray
-            Input image array
-        footprint_size : int
-            Size of the structuring element (disk). Larger values remove broader
-            background variations and keep only the brightest local features.
-
-        Returns:
-        --------
-        numpy.ndarray
-            Image with bright features extracted and background removed
-        """
-        # Create circular structuring element
-        footprint = skimage.morphology.disk(footprint_size)
-        return skimage.morphology.white_tophat(image, footprint=footprint)
-
     @BatchProcessingRegistry.register(
         name="Percentile Threshold (Keep Brightest)",
         suffix="_percentile",