⚡ Faster Filtering in Patch Dataset

tests/test_patch_extraction.py

@@ -83,6 +83,7 @@ def test_get_patch_extractor(source_image, patch_extr_csv):
     )
 
     assert isinstance(points, patchextraction.PointsPatchExtractor)
+    assert len(points) == 1860
 
     sliding_window = patchextraction.get_patch_extractor(
         input_img=input_img,
@@ -424,97 +425,62 @@ def test_filter_coordinates():
     mask = np.zeros([9, 6])
     mask[0:4, 3:8] = 1  # will flag first 2
     mask_reader = VirtualWSIReader(mask)
-
-    # Tests for (original) filter_coordinates method
-    # functionality test
-    flag_list = PatchExtractor.filter_coordinates(
-        mask_reader, bbox_list, resolution=1.0, units="baseline"
-    )
-    assert np.sum(flag_list - np.array([1, 1, 0, 0, 0, 0])) == 0
-
-    # Test for bad mask input
-    with pytest.raises(
-        ValueError, match="`mask_reader` should be wsireader.VirtualWSIReader."
-    ):
-        PatchExtractor.filter_coordinates(
-            mask, bbox_list, resolution=1.0, units="baseline"
-        )
-
-    # Test for bad bbox coordinate list in the input
-    with pytest.raises(ValueError, match=r".*should be ndarray of integer type.*"):
-        PatchExtractor.filter_coordinates(
-            mask_reader, bbox_list.tolist(), resolution=1.0, units="baseline"
-        )
-
-    # Test for incomplete coordinate list
-    with pytest.raises(ValueError, match=r".*`coordinates_list` of shape.*"):
-        PatchExtractor.filter_coordinates(
-            mask_reader, bbox_list[:, :2], resolution=1.0, units="baseline"
-        )
+    slide_shape = [6, 9]  # slide shape (w, h) at requested resolution
 
     ###############################################
-    # Tests for filter_coordinates_fast (new) method
+    # Tests for filter_coordinates (new) method
     _info = mask_reader.info
     _info.mpp = 1.0
     mask_reader._m_info = _info
 
     # functionality test
-    flag_list = PatchExtractor.filter_coordinates_fast(
+    flag_list = PatchExtractor.filter_coordinates(
         mask_reader,
         bbox_list,
-        coordinate_resolution=1.0,
-        coordinate_units="mpp",
-        mask_resolution=1,
+        slide_shape,
     )
     assert np.sum(flag_list - np.array([1, 1, 0, 0, 0, 0])) == 0
-    flag_list = PatchExtractor.filter_coordinates_fast(
-        mask_reader, bbox_list, coordinate_resolution=(1.0, 1.0), coordinate_units="mpp"
-    )
+    flag_list = PatchExtractor.filter_coordinates(mask_reader, bbox_list, slide_shape)
 
     # Test for bad mask input
     with pytest.raises(
         ValueError, match="`mask_reader` should be wsireader.VirtualWSIReader."
     ):
-        PatchExtractor.filter_coordinates_fast(
+        PatchExtractor.filter_coordinates(
             mask,
             bbox_list,
-            coordinate_resolution=1.0,
-            coordinate_units="mpp",
+            slide_shape,
         )
 
     # Test for bad bbox coordinate list in the input
     with pytest.raises(ValueError, match=r".*should be ndarray of integer type.*"):
-        PatchExtractor.filter_coordinates_fast(
+        PatchExtractor.filter_coordinates(
             mask_reader,
             bbox_list.tolist(),
-            coordinate_resolution=1,
-            coordinate_units="mpp",
+            slide_shape,
         )
 
     # Test for incomplete coordinate list
     with pytest.raises(ValueError, match=r".*`coordinates_list` must be of shape.*"):
-        PatchExtractor.filter_coordinates_fast(
+        PatchExtractor.filter_coordinates(
             mask_reader,
             bbox_list[:, :2],
-            coordinate_resolution=1,
-            coordinate_units="mpp",
+            slide_shape,
         )
 
     # Test for put of range min_mask_ratio
     with pytest.raises(ValueError, match="`min_mask_ratio` must be between 0 and 1."):
-        PatchExtractor.filter_coordinates_fast(
+        PatchExtractor.filter_coordinates(
             mask_reader,
             bbox_list,
-            coordinate_resolution=1.0,
-            coordinate_units="mpp",
+            slide_shape,
             min_mask_ratio=-0.5,
         )
     with pytest.raises(ValueError, match="`min_mask_ratio` must be between 0 and 1."):
-        PatchExtractor.filter_coordinates_fast(
+        PatchExtractor.filter_coordinates(
             mask_reader,
             bbox_list,
-            coordinate_resolution=1.0,
-            coordinate_units="mpp",
+            slide_shape,
             min_mask_ratio=1.1,
         )
 
@@ -529,7 +495,7 @@ def test_mask_based_patch_extractor_ndpi(sample_ndpi):
 
     # Generating a test mask to read patches from
     mask_dim = (int(slide_dimensions[0] / 10), int(slide_dimensions[1] / 10))
-    wsi_mask = np.zeros(mask_dim, dtype=np.uint8)
+    wsi_mask = np.zeros(mask_dim[::-1], dtype=np.uint8)  # reverse as dims are (w, h)
     # masking two column to extract patch from
     wsi_mask[:, :2] = 255
 

tiatoolbox/models/dataset/classification.py

@@ -142,13 +142,8 @@ class WSIPatchDataset(dataset_abc.PatchDatasetABC):
         units:
             See (:class:`.WSIReader`) for details.
         preproc_func:
-            Preprocessing function used to transform the input data. If
-            supplied, then torch.Compose will be used on the input
-            preprocs. preprocs is a list of torchvision transforms for
-            preprocessing the image. The transforms will be applied in
-            the order that they are given in the list. For more
-            information, visit the following link:
-            https://pytorch.org/vision/stable/transforms.html.
+            Preprocessing function used to transform the input data. It will
+            be called on each patch before returning it.
 
     """
 
@@ -162,6 +157,8 @@ def __init__(
         resolution=None,
         units=None,
         auto_get_mask=True,
+        min_mask_ratio=0,
+        preproc_func=None,
     ):
         """Create a WSI-level patch dataset.
 
@@ -187,12 +184,22 @@ def __init__(
                 `units`. Expected to be positive and of (height, width).
                 Note, this is not at level 0.
             resolution:
-              Check (:class:`.WSIReader`) for details. When
-              `mode='tile'`, value is fixed to be `resolution=1.0` and
-              `units='baseline'` units: check (:class:`.WSIReader`) for
-              details.
+                Check (:class:`.WSIReader`) for details. When
+                `mode='tile'`, value is fixed to be `resolution=1.0` and
+                `units='baseline'` units: check (:class:`.WSIReader`) for
+                details.
+            units:
+                Units in which `resolution` is defined.
+            auto_get_mask:
+                If `True`, then automatically get simple threshold mask using
+                WSIReader.tissue_mask() function.
+            min_mask_ratio:
+                Only patches with positive area percentage above this value are
+                included. Defaults to 0.
             preproc_func:
-                Preprocessing function used to transform the input data.
+                Preprocessing function used to transform the input data. If
+                supplied, the function will be called on each patch before
+                returning it.
 
         Examples:
             >>> # A user defined preproc func and expected behavior
@@ -233,6 +240,7 @@ def __init__(
         ):
             raise ValueError(f"Invalid `stride_shape` value {stride_shape}.")
 
+        self.preproc_func = preproc_func
         img_path = pathlib.Path(img_path)
         if mode == "wsi":
             self.reader = WSIReader.open(img_path)
@@ -299,8 +307,8 @@ def __init__(
             selected = PatchExtractor.filter_coordinates(
                 mask_reader,  # must be at the same resolution
                 self.inputs,  # must already be at requested resolution
-                resolution=resolution,
-                units=units,
+                wsi_shape=wsi_shape,
+                min_mask_ratio=min_mask_ratio,
             )
             self.inputs = self.inputs[selected]