Store original image downsample in LabeledImageServer

qubalab/images/image_server.py

@@ -294,6 +294,8 @@ def _resize(image: Union[np.ndarray, Image.Image], target_size: tuple[int, int],
                     pilImage = Image.fromarray(image)
                 elif np.issubdtype(image.dtype, np.integer):
                     pilImage = Image.fromarray(image.astype(np.int32), mode='I')
+                elif np.issubdtype(image.dtype, np.bool_):
+                    pilImage = Image.fromarray(image, "1")
                 else:
                     pilImage = Image.fromarray(image.astype(np.float32), mode='F')
                 pilImage = ImageServer._resize(pilImage, target_size=target_size, resample=resample)

qubalab/images/labeled_server.py

@@ -29,6 +29,7 @@ def __init__(
         label_map: dict[Classification, int] = None,
         downsample: float = None,
         multichannel: bool = False,
+        resize_method = PIL.Image.Resampling.NEAREST,
         **kwargs
     ):
         """
@@ -42,10 +43,10 @@ def __init__(
                              integers representing a label (see the label_map parameter). If True, the number of channels will be
                              equal to the highest label value + 1, and the pixel located at (c, y, x) is a boolean indicating if an annotation
                              with label c is present on the pixel located at (x, y)
-        :param resize_method: the resampling method to use when resizing the image for downsampling. Bicubic by default
+        :param resize_method: the resampling method to use when resizing the image for downsampling. Nearest neighbour by default for labeled images.
         :raises ValueError: when a label in label_map is less than or equal to 0
         """
-        super().__init__(**kwargs)
+        super().__init__(resize_method=resize_method, **kwargs)
 
         if label_map is not None and any(label <= 0 for label in label_map.values()):
             raise ValueError('A label in label_map is less than or equal to 0: ' + str(label_map))
@@ -70,25 +71,26 @@ def _build_metadata(self) -> ImageMetadata:
             self._base_image_metadata.path,
             f'{self._base_image_metadata.name} - labels',
             (ImageShape(
-                int(self._base_image_metadata.width / self._downsample),
-                int(self._base_image_metadata.height / self._downsample),
+                int(self._base_image_metadata.width),
+                int(self._base_image_metadata.height),
                 1,
                 max(self._feature_index_to_label.values(), default=0)+1 if self._multichannel else 1,
                 1,
             ),),
             PixelCalibration(
                 PixelLength(
-                    self._base_image_metadata.pixel_calibration.length_x.length * self._downsample,
+                    self._base_image_metadata.pixel_calibration.length_x.length,
                     self._base_image_metadata.pixel_calibration.length_x.unit
                 ),
                 PixelLength(
-                    self._base_image_metadata.pixel_calibration.length_y.length * self._downsample,
+                    self._base_image_metadata.pixel_calibration.length_y.length,
                     self._base_image_metadata.pixel_calibration.length_y.unit
                 ),
                 self._base_image_metadata.pixel_calibration.length_z
             ),
             False,
-            bool if self._multichannel else np.uint32
+            bool if self._multichannel else np.uint32,
+            downsamples = [self._downsample]
         )
 
     def _read_block(self, level: int, region: Region2D) -> np.ndarray:

tests/images/test_labeled_server.py

@@ -37,7 +37,7 @@
 
 def test_image_width_with_downsample():
     downsample = 1.5
-    expected_width = int(sample_metadata.shape.x / downsample)
+    expected_width = sample_metadata.shape.x
     labeled_server = LabeledImageServer(sample_metadata, [], downsample=downsample)
 
     width = labeled_server.metadata.width
@@ -49,7 +49,7 @@ def test_image_width_with_downsample():
 
 def test_image_height_with_downsample():
     downsample = 1.5
-    expected_height = int(sample_metadata.shape.y / downsample)
+    expected_height = sample_metadata.shape.y
     labeled_server = LabeledImageServer(sample_metadata, [], downsample=downsample)
 
     height = labeled_server.metadata.height
@@ -174,7 +174,7 @@ def test_image_n_resolutions():
 
 def test_x_pixel_length_with_downsample():
     downsample = 1.5
-    expected_length_x = sample_metadata.pixel_calibration.length_x.length * downsample
+    expected_length_x = sample_metadata.pixel_calibration.length_x.length
     labeled_server = LabeledImageServer(sample_metadata, [], downsample=downsample)
 
     length_x = labeled_server.metadata.pixel_calibration.length_x.length
@@ -327,7 +327,7 @@ def test_read_points_in_single_channel_image_without_label_map_with_downsample()
     )
     labeled_server = LabeledImageServer(sample_metadata, features, multichannel=False, downsample=downsample)
 
-    image = labeled_server.read_region(1, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
+    image = labeled_server.read_region(downsample, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
 
     np.testing.assert_array_equal(image, expected_image)
 
@@ -359,7 +359,7 @@ def test_read_line_in_single_channel_image_without_label_map_with_downsample():
     )
     labeled_server = LabeledImageServer(sample_metadata, features, multichannel=False, downsample=downsample)
 
-    image = labeled_server.read_region(1, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
+    image = labeled_server.read_region(downsample, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
 
     np.testing.assert_array_equal(image, expected_image)
 
@@ -391,7 +391,7 @@ def test_read_polygon_in_single_channel_image_without_label_map_with_downsample(
     )
     labeled_server = LabeledImageServer(sample_metadata, features, multichannel=False, downsample=downsample)
 
-    image = labeled_server.read_region(1, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
+    image = labeled_server.read_region(downsample, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
 
     np.testing.assert_array_equal(image, expected_image)
 
@@ -409,10 +409,7 @@ def rands():
             (x, y + 1)
         )
 
-    coords = [rands() for i in range(max_objects)]
-
-    n_objects = len(coords)
-    features = [ImageFeature(geojson.Polygon([coords[i]]), Classification("Some classification")) for i in range(n_objects)]
+    features = [ImageFeature(geojson.Polygon([rands()])) for i in range(max_objects)]
     labeled_server = LabeledImageServer(large_metadata, features, multichannel=False, downsample=downsample)
 
     image = labeled_server.read_region(1, Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height))
@@ -434,8 +431,41 @@ def test_single_channel_labeled_image_with_region_request():
 
     np.testing.assert_array_equal(image, expected_image)
 
+
+
+def test_single_channel_labeled_image_with_starting_downsample():
+    features = [ImageFeature(geojson.LineString([(6, 5), (9, 5)]))]
+    # when resizing, we lose the labels with bicubic
+    expected_image = np.array(
+        [[[0, 0, 0, 0, 0],
+          [0, 0, 0, 0, 0],
+          [0, 0, 0, 1, 1]]]
+    )
+    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=False, downsample=1)
+    downsample = 2
+    region = Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height)
+    image = labeled_server.read_region(downsample, region)
+
+    np.testing.assert_array_equal(image, expected_image)
+
+
+def test_single_channel_labeled_image_with_request_downsample():
+    # we downsample 
+    features = [ImageFeature(geojson.LineString([(6, 5), (9, 5)]))]
+    expected_image = np.array(
+        [[[0, 0, 0, 0, 0],
+          [0, 0, 0, 0, 0],
+          [0, 0, 0, 1, 1]]]
+    )
+    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=False, downsample=1)
+    region = Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height)
+    image = labeled_server.read_region(2, region)
+
+    np.testing.assert_array_equal(image, expected_image)
+
+
+
 def test_multi_channel_labeled_image_with_region_request():
-    downsample = 1
     features = [ImageFeature(geojson.LineString([(7, 5), (9, 5)]))]
     expected_image = np.array(
         [[[False, False, False, False, False],
@@ -445,7 +475,66 @@ def test_multi_channel_labeled_image_with_region_request():
           [False, False, False, False, False],
           [False, False, True, True, True]]]
     )
-    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=True, downsample=downsample)
+    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=True, downsample=1)
     region = Region2D(5, 3, labeled_server.metadata.width-5, labeled_server.metadata.height-3)
     image = labeled_server.read_region(1, region)
+
+    np.testing.assert_array_equal(image, expected_image)
+
+
+
+
+def test_multi_channel_labeled_image_with_starting_downsample():
+    # we downsample the feature, then request at the same downsample
+    features = [ImageFeature(geojson.LineString([(6, 5), (9, 5)]))]
+    expected_image = np.array(
+        [[[False, False, False, False, False],
+          [False, False, False, False, False],
+          [False, False, False, False, False]],
+          [[False, False, False, False, False],
+          [False, False, False, False, False],
+          [False, False, False, True, True]]]
+    )
+    downsample = 2
+    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=True, downsample=downsample)
+    region = Region2D(0, 0, sample_metadata.width, sample_metadata.height)
+    image = labeled_server.read_region(2, region)
+
+    np.testing.assert_array_equal(image, expected_image)
+
+def test_multi_channel_labeled_image_with_request_downsample():
+    features = [ImageFeature(geojson.LineString([(6, 5), (9, 5)]))]
+    ## because we resize the image after reading, we lose the small region
+    expected_image = np.array(
+        [[[False, False, False, False, False],
+          [False, False, False, False, False],
+          [False, False, False, False, False]],
+          [[False, False, False, False, False],
+          [False, False, False, False, False],
+          [False, False, False, False, False]]]
+    )
+    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=True, downsample=1)
+    downsample = 2
+    region = Region2D(0, 0, labeled_server.metadata.width, labeled_server.metadata.height)
+    image = labeled_server.read_region(downsample, region)
+
+    np.testing.assert_array_equal(image, expected_image)
+
+
+def test_multi_channel_labeled_image_with_starting_downsample_upsampled():
+    # we downsample the feature, then request at a downsample of 1, so upsampled!
+    # therefore the feature gets much bigger
+    features = [ImageFeature(geojson.LineString([(5, 5), (9, 5)]))]
+    expected_image = np.array(
+        [[[False, False, False, False, False],
+        [False, False, False, False, False],
+        [False, False, False, False, False]],
+
+       [[False, False, False, False, False],
+        [False, False, False, False, False],
+        [False, False,  True,  True,  True]]]
+    )
+    labeled_server = LabeledImageServer(sample_metadata, features, multichannel=True, downsample=2)
+    image = labeled_server.read_region(2)
+
     np.testing.assert_array_equal(image, expected_image)