Merge 071a100 into 036429b

setup.py

@@ -31,7 +31,6 @@ def ascii_bytes_from(path, *paths):
     "Pillow",
     "scikit-image",
     "scipy",
-    "sparse",
     "openslide-wrapper",
     "typing_extensions",
 ]

src/histolab/exceptions.py

@@ -0,0 +1,14 @@
+class LevelError(Exception):
+    """Raised when a requested level is not available"""
+
+    def __init__(self, *args) -> None:
+        if args:
+            self.message = args[0]
+        else:
+            self.message = None
+
+    def __str__(self):
+        if self.message:
+            return self.message
+        else:
+            return ""

src/histolab/slide.py

@@ -30,8 +30,8 @@
 import numpy as np
 import openslide
 import PIL
-import sparse
 
+from .exceptions import LevelError
 from .filters import image_filters as imf
 from .filters import morphological_filters as mof
 from .tile import Tile
@@ -42,7 +42,6 @@
     polygon_to_mask_array,
     region_coordinates,
     regions_from_binary_mask,
-    resize_mask,
     scale_coordinates,
 )
 
@@ -97,20 +96,32 @@ def level_dimensions(self, level: int = 0) -> Tuple[int, int]:
         try:
             return self._wsi.level_dimensions[level]
         except IndexError:
-            raise ValueError(
+            raise LevelError(
                 f"Level {level} not available. Number of available levels: "
                 f"{len(self._wsi.level_dimensions)}"
             )
 
+    @lazyproperty
+    def levels(self) -> List[int]:
+        """Return the slide's available levels
+
+        Returns
+        -------
+        List[int]
+            The levels available
+        """
+        return list(range(len(self._wsi.level_dimensions)))
+
     @lazyproperty
     @lru_cache(maxsize=100)
-    def biggest_tissue_box_mask(self) -> sparse._coo.core.COO:
-        """Return the binary mask of the box containing the max area of tissue.
+    def biggest_tissue_box_mask(self) -> np.ndarray:
+        """Return the thumbnail binary mask of the box containing the max tissue area.
 
         Returns
         -------
-        mask: sparse._coo.core.COO
-            Binary mask of the box containing the max area of tissue.
+        mask: np.ndarray
+            Binary mask of the box containing the max area of tissue. The dimensions are
+            those of the thumbnail.
 
         """
         thumb = self._wsi.get_thumbnail(self._thumbnail_size)
@@ -123,8 +134,7 @@ def biggest_tissue_box_mask(self) -> sparse._coo.core.COO:
         thumb_bbox_mask = polygon_to_mask_array(
             self._thumbnail_size, biggest_region_coordinates
         )
-        thumb_bbox_mask_sparse = sparse.COO(thumb_bbox_mask)
-        return resize_mask(thumb_bbox_mask_sparse, self.dimensions)
+        return thumb_bbox_mask
 
     def extract_tile(self, coords: CoordinatePair, level: int) -> Tile:
         """Extract a tile of the image at the selected level.

src/histolab/tiler.py

@@ -2,7 +2,8 @@
 from typing import Tuple
 
 import numpy as np
-import sparse
+
+from histolab.exceptions import LevelError
 
 from .slide import Slide
 from .tile import Tile
@@ -11,7 +12,6 @@
     lru_cache,
     region_coordinates,
     regions_from_binary_mask,
-    resize_mask,
     scale_coordinates,
 )
 
@@ -28,10 +28,10 @@ class Tiler(Protocol):
     tile_size: int
 
     @lru_cache(maxsize=100)
-    def box_mask(self, slide: Slide) -> sparse._coo.core.COO:
-        """Return binary mask at level 0 of the box to consider for tiles extraction.
+    def box_mask_thumb(self, slide: Slide) -> np.ndarray:
+        """Return binary mask, at thumbnail level, of the box for tiles extraction.
 
-        The mask pixels set to True will be the ones corresponding to the tissue box.
+        The mask pixels set to True correspond to the tissue box.
 
         Parameters
         ----------
@@ -40,38 +40,12 @@ def box_mask(self, slide: Slide) -> sparse._coo.core.COO:
 
         Returns
         -------
-        sparse._coo.core.COO
-            Extraction mask at level 0
+        np.ndarray
+            Extraction mask at thumbnail level
         """
 
         return slide.biggest_tissue_box_mask
 
-    @lru_cache(maxsize=100)
-    def box_mask_lvl(self, slide: Slide) -> sparse._coo.core.COO:
-        """Return binary mask at target level of the box to consider for the extraction.
-
-        The mask pixels set to True will be the ones corresponding to the tissue box.
-
-        Parameters
-        ----------
-        slide : Slide
-            The Slide from which to extract the extraction mask
-
-        Returns
-        -------
-        sparse._coo.core.COO
-            Extraction mask at target level
-        """
-
-        box_mask_wsi = self.box_mask(slide)
-
-        if self.level != 0:
-            return resize_mask(
-                box_mask_wsi, target_dimensions=slide.level_dimensions(self.level),
-            )
-        else:
-            return box_mask_wsi
-
     def _tile_filename(
         self, tile_wsi_coords: CoordinatePair, tiles_counter: int
     ) -> str:
@@ -159,7 +133,7 @@ def level(self) -> int:
     @level.setter
     def level(self, level_: int):
         if level_ < 0:
-            raise ValueError(f"Level cannot be negative ({level_})")
+            raise LevelError(f"Level cannot be negative ({level_})")
         self._valid_level = level_
 
     def extract(self, slide: Slide):
@@ -172,6 +146,12 @@ def extract(self, slide: Slide):
         slide : Slide
             Slide from which to extract the tiles
         """
+        if self.level not in slide.levels:
+            raise LevelError(
+                f"Level {self.level} not available. Number of available levels: "
+                f"{len(slide.levels)}"
+            )
+
         grid_tiles = self._grid_tiles_generator(slide)
 
         tiles_counter = 0
@@ -181,7 +161,7 @@ def extract(self, slide: Slide):
             tile.save(tile_filename)
             print(f"\t Tile {tiles_counter} saved: {tile_filename}")
 
-        print(f"{tiles_counter+1} Grid Tiles have been saved.")
+        print(f"{tiles_counter} Grid Tiles have been saved.")
 
     def _grid_coordinates_from_bbox_coordinates(
         self, bbox_coordinates: CoordinatePair, slide: Slide
@@ -242,16 +222,21 @@ def _grid_coordinates_generator(self, slide: Slide) -> CoordinatePair:
         Iterator[CoordinatePair]
             Iterator of tiles' CoordinatePair
         """
-        box_mask_lvl = self.box_mask_lvl(slide)
+        box_mask_thumb = self.box_mask_thumb(slide)
 
-        regions = regions_from_binary_mask(box_mask_lvl.todense())
+        regions = regions_from_binary_mask(box_mask_thumb)
         for region in regions:  # at the moment there is only one region
-            bbox_coordinates = region_coordinates(region)
+            bbox_coordinates_thumb = region_coordinates(region)
+            bbox_coordinates = scale_coordinates(
+                bbox_coordinates_thumb,
+                box_mask_thumb.shape[::-1],
+                slide.level_dimensions(self.level),
+            )
             yield from self._grid_coordinates_from_bbox_coordinates(
                 bbox_coordinates, slide
             )
 
-    def _grid_tiles_generator(self, slide: Slide) -> (Tile, CoordinatePair):
+    def _grid_tiles_generator(self, slide: Slide) -> Tuple[Tile, CoordinatePair]:
         """Generator of tiles arranged in a grid.
 
         Parameters
@@ -377,7 +362,7 @@ def level(self) -> int:
     @level.setter
     def level(self, level_: int):
         if level_ < 0:
-            raise ValueError(f"Level cannot be negative ({level_})")
+            raise LevelError(f"Level cannot be negative ({level_})")
         self._valid_level = level_
 
     @property
@@ -427,24 +412,32 @@ def _random_tile_coordinates(self, slide: Slide) -> CoordinatePair:
         CoordinatePair
             Random tile Coordinates at level 0
         """
-        box_mask_lvl = self.box_mask_lvl(slide)
+        box_mask_thumb = self.box_mask_thumb(slide)
         tile_w_lvl, tile_h_lvl = self.tile_size
 
-        x_ul_lvl = np.random.choice(sparse.where(box_mask_lvl)[1])
-        y_ul_lvl = np.random.choice(sparse.where(box_mask_lvl)[0])
+        x_ul_lvl = np.random.choice(np.where(box_mask_thumb)[1])
+        y_ul_lvl = np.random.choice(np.where(box_mask_thumb)[0])
+
+        # Scale tile dimensions to thumbnail dimensions
+        tile_w_thumb = (
+            tile_w_lvl * box_mask_thumb.shape[1] / slide.level_dimensions(self.level)[0]
+        )
+        tile_h_thumn = (
+            tile_h_lvl * box_mask_thumb.shape[0] / slide.level_dimensions(self.level)[1]
+        )
 
-        x_br_lvl = x_ul_lvl + tile_w_lvl
-        y_br_lvl = y_ul_lvl + tile_h_lvl
+        x_br_lvl = x_ul_lvl + tile_w_thumb
+        y_br_lvl = y_ul_lvl + tile_h_thumn
 
         tile_wsi_coords = scale_coordinates(
             reference_coords=CoordinatePair(x_ul_lvl, y_ul_lvl, x_br_lvl, y_br_lvl),
-            reference_size=slide.level_dimensions(level=self.level),
-            target_size=slide.level_dimensions(level=0),
+            reference_size=box_mask_thumb.shape[::-1],
+            target_size=slide.dimensions,
         )
 
         return tile_wsi_coords
 
-    def _random_tiles_generator(self, slide: Slide) -> (Tile, CoordinatePair):
+    def _random_tiles_generator(self, slide: Slide) -> Tuple[Tile, CoordinatePair]:
         """Generate Random Tiles within a slide box.
 
         Stops if:

src/histolab/util.py

@@ -24,7 +24,6 @@
 import numpy as np
 import PIL
 import PIL.ImageDraw
-import sparse
 from skimage.measure import label, regionprops
 
 from .types import CoordinatePair, Region
@@ -182,29 +181,6 @@ def region_coordinates(region: Region) -> CoordinatePair:
     return CoordinatePair(x_ul, y_ul, x_br, y_br)
 
 
-def resize_mask(
-    input_mask: sparse._coo.core.COO, target_dimensions: Tuple[int, int]
-) -> sparse._coo.core.COO:
-    """Resize mask to ``target_dimensions``.
-
-    Parameters
-    ----------
-    input_mask : sparse.COO
-        Input mask
-    target_dimensions : List[int, int]
-        Dimensions of the resized mask
-
-    Returns
-    -------
-    sparse._coo.core.COO
-        Resized mask
-    """
-    input_mask_img = PIL.Image.fromarray(input_mask.todense())
-    resized_mask_img = input_mask_img.resize(target_dimensions)
-    resized_mask_arr = np.array(resized_mask_img).astype(bool)
-    return sparse.COO(resized_mask_arr)
-
-
 def apply_mask_image(img: PIL.Image.Image, mask: np.ndarray) -> PIL.Image.Image:
     """Mask image with the provided binary mask.
 

tests/base.py

@@ -1,5 +1,4 @@
 import numpy as np
-import sparse
 
 # ============== IMAGES ==============
 IMAGE1_GRAY = np.array(
@@ -236,6 +235,3 @@
         [True, True, False, False, False, True, True, False, True, False],
     ]
 )
-
-SPARSE_BASE_MASK = sparse.COO(BASE_MASK)
-SPARSE_COMPLEX_MASK = sparse.COO(COMPLEX_MASK)

tests/unit/test_exceptions.py

@@ -0,0 +1,56 @@
+import pytest
+
+from histolab import exceptions as exp
+
+from ..unitutil import ANY, initializer_mock
+
+
+class DescribeExceptions(object):
+    @pytest.mark.parametrize("args", ("error", ["hello", "error"], None))
+    def it_can_construct_from_args_level_error(self, request, args):
+        _init = initializer_mock(request, exp.LevelError)
+
+        if type(args) == list:
+            level_error = exp.LevelError(*args)
+        else:
+            level_error = exp.LevelError(args)
+
+        if type(args) == list:
+            _init.assert_called_once_with(ANY, *args)
+        else:
+            _init.assert_called_once_with(ANY, args)
+        assert isinstance(level_error, exp.LevelError)
+        assert isinstance(level_error, Exception)
+
+    @pytest.mark.parametrize("args", ("error", ["hello", "error"], None))
+    def it_knows_its_message_level_error(self, request, args):
+        if type(args) == list:
+            level_error = exp.LevelError(*args)
+        else:
+            level_error = exp.LevelError(args)
+
+        message = level_error.message
+
+        if args:
+            if type(args) == str:
+                assert type(message) == str
+                assert message == args
+            if type(args) == list:
+                assert type(message) == str
+                assert message == list(args)[0]
+        else:
+            assert message is None
+
+    @pytest.mark.parametrize("args", ("error", ["hello", "error"], None))
+    def it_knows_its_str_level_error(self, request, args):
+        if type(args) == list:
+            level_error = exp.LevelError(*args)
+        else:
+            level_error = exp.LevelError(args)
+
+        s = str(level_error)
+
+        if not args:
+            assert s == ""
+        else:
+            assert s == level_error.message