Set masks to zero where masks overlap

test/test_utils.py

@@ -210,14 +210,11 @@ def test_draw_segmentation_masks(colors, alpha, device):
     num_masks, h, w = 2, 100, 100
     dtype = torch.uint8
     img = torch.randint(0, 256, size=(3, h, w), dtype=dtype, device=device)
-    masks = torch.randint(0, 2, (num_masks, h, w), dtype=torch.bool, device=device)
+    masks = torch.zeros((num_masks, h, w), dtype=torch.bool, device=device)
+    masks[0, 10:20, 10:20] = True
+    masks[1, 15:25, 15:25] = True
 
-    # For testing we enforce that there's no overlap between the masks. The
-    # current behaviour is that the last mask's color will take priority when
-    # masks overlap, but this makes testing slightly harder, so we don't really
-    # care
     overlap = masks[0] & masks[1]
-    masks[:, overlap] = False
 
     out = utils.draw_segmentation_masks(img, masks, colors=colors, alpha=alpha)
     assert out.dtype == dtype
@@ -239,12 +236,15 @@ def test_draw_segmentation_masks(colors, alpha, device):
         color = torch.tensor(color, dtype=dtype, device=device)
 
         if alpha == 1:
-            assert (out[:, mask] == color[:, None]).all()
+            assert (out[:, mask & ~overlap] == color[:, None]).all()
         elif alpha == 0:
-            assert (out[:, mask] == img[:, mask]).all()
+            assert (out[:, mask & ~overlap] == img[:, mask & ~overlap]).all()
 
-        interpolated_color = (img[:, mask] * (1 - alpha) + color[:, None] * alpha).to(dtype)
-        torch.testing.assert_close(out[:, mask], interpolated_color, rtol=0.0, atol=1.0)
+        interpolated_color = (img[:, mask & ~overlap] * (1 - alpha) + color[:, None] * alpha).to(dtype)
+        torch.testing.assert_close(out[:, mask & ~overlap], interpolated_color, rtol=0.0, atol=1.0)
+
+    interpolated_overlap = (img[:, overlap] * (1 - alpha)).to(dtype)
+    torch.testing.assert_close(out[:, overlap], interpolated_overlap, rtol=0.0, atol=1.0)
 
 
 def test_draw_segmentation_masks_dtypes():

torchvision/utils.py

@@ -299,6 +299,7 @@ def draw_segmentation_masks(
         raise ValueError("The image and the masks must have the same height and width")
 
     num_masks = masks.size()[0]
+    overlapping_masks = masks.sum(dim=0) > 1
 
     if num_masks == 0:
         warnings.warn("masks doesn't contain any mask. No mask was drawn")
@@ -315,6 +316,8 @@ def draw_segmentation_masks(
     for mask, color in zip(masks, colors):
         img_to_draw[:, mask] = color[:, None]
 
+    img_to_draw[:, overlapping_masks] = 0
+
     out = image * (1 - alpha) + img_to_draw * alpha
     # Note: at this point, out is a float tensor in [0, 1] or [0, 255] depending on original_dtype
     return out.to(original_dtype)