Added utility to draw segmentation masks

docs/source/utils.rst

@@ -7,4 +7,6 @@ torchvision.utils
 
 .. autofunction:: save_image
 
-.. autofunction:: draw_bounding_boxes
+.. autofunction:: draw_bounding_boxes
+
+.. autofunction:: draw_segmentation_masks

test/test_utils.py

@@ -9,6 +9,30 @@
 import torchvision.transforms.functional as F
 from PIL import Image
 
+masks = torch.tensor([
+    [
+        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
+        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
+        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
+        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
+        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799]
+    ],
+    [
+        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
+        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
+        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
+        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
+        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541]
+    ],
+    [
+        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
+        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
+        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
+        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
+        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
+    ]
+], dtype=torch.float)
+
 
 class Tester(unittest.TestCase):
 
@@ -96,6 +120,35 @@ def test_draw_boxes(self):
         expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
         self.assertTrue(torch.equal(result, expected))
 
+    def test_draw_segmentation_masks_colors(self):
+        img = torch.full((3, 5, 5), 255, dtype=torch.uint8)
+        colors = ["#FF00FF", (0, 255, 0), "red"]
+        result = utils.draw_segmentation_masks(img, masks, colors=colors)
+
+        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
+                            "fakedata", "draw_segm_masks_colors_util.png")
+
+        if not os.path.exists(path):
+            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
+            res.save(path)
+
+        expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
+        self.assertTrue(torch.equal(result, expected))
+
+    def test_draw_segmentation_masks_no_colors(self):
+        img = torch.full((3, 20, 20), 255, dtype=torch.uint8)
+        result = utils.draw_segmentation_masks(img, masks, colors=None)
+
+        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
+                            "fakedata", "draw_segm_masks_no_colors_util.png")
+
+        if not os.path.exists(path):
+            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
+            res.save(path)
+
+        expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
+        self.assertTrue(torch.equal(result, expected))
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/utils.py

@@ -6,7 +6,7 @@
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont, ImageColor
 
-__all__ = ["make_grid", "save_image", "draw_bounding_boxes"]
+__all__ = ["make_grid", "save_image", "draw_bounding_boxes", "draw_segmentation_masks"]
 
 
 @torch.no_grad()
@@ -153,7 +153,7 @@ def draw_bounding_boxes(
     If filled, Resulting Tensor should be saved as PNG image.
 
     Args:
-        image (Tensor): Tensor of shape (C x H x W)
+        image (Tensor): Tensor of shape (C x H x W) and dtype uint8.
         boxes (Tensor): Tensor of size (N, 4) containing bounding boxes in (xmin, ymin, xmax, ymax) format. Note that
             the boxes are absolute coordinates with respect to the image. In other words: `0 <= xmin < xmax < W` and
             `0 <= ymin < ymax < H`.
@@ -210,3 +210,61 @@ def draw_bounding_boxes(
             draw.text((bbox[0], bbox[1]), labels[i], fill=color, font=txt_font)
 
     return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1)
+
+
+@torch.no_grad()
+def draw_segmentation_masks(
+    image: torch.Tensor,
+    masks: torch.Tensor,
+    alpha: float = 0.2,
+    colors: Optional[List[Union[str, Tuple[int, int, int]]]] = None,
+) -> torch.Tensor:
+
+    """
+    Draws segmentation masks on given RGB image.
+    The values of the input image should be uint8 between 0 and 255.
+
+    Args:
+        image (Tensor): Tensor of shape (3 x H x W) and dtype uint8.
+        masks (Tensor): Tensor of shape (num_masks, H, W). Each containing probability of predicted class.
+        alpha (float): Float number between 0 and 1 denoting factor of transpaerency of masks.
+        colors (List[Union[str, Tuple[int, int, int]]]): List containing the colors of masks. The colors can
+            be represented as `str` or `Tuple[int, int, int]`.
+    """
+
+    if not isinstance(image, torch.Tensor):
+        raise TypeError(f"Tensor expected, got {type(image)}")
+    elif image.dtype != torch.uint8:
+        raise ValueError(f"Tensor uint8 expected, got {image.dtype}")
+    elif image.dim() != 3:
+        raise ValueError("Pass individual images, not batches")
+    elif image.size()[0] != 3:
+        raise ValueError("Pass an RGB image. Other Image formats are not supported")
+
+    num_masks = masks.size()[0]
+    masks = masks.argmax(0)
+
+    if colors is None:
+        palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
+        colors_t = torch.as_tensor([i for i in range(num_masks)])[:, None] * palette
+        color_arr = (colors_t % 255).numpy().astype("uint8")
+    else:
+        color_list = []
+        for color in colors:
+            if isinstance(color, str):
+                # This will automatically raise Error if rgb cannot be parsed.
+                fill_color = ImageColor.getrgb(color)
+                color_list.append(fill_color)
+            elif isinstance(color, tuple):
+                color_list.append(color)
+
+        color_arr = np.array(color_list).astype("uint8")
+
+    _, h, w = image.size()
+    img_to_draw = Image.fromarray(masks.byte().cpu().numpy()).resize((w, h))
+    img_to_draw.putpalette(color_arr)
+
+    img_to_draw = torch.from_numpy(np.array(img_to_draw.convert('RGB')))
+    img_to_draw = img_to_draw.permute((2, 0, 1))
+
+    return (image.float() * alpha + img_to_draw.float() * (1.0 - alpha)).to(dtype=torch.uint8)