Add utility to draw keypoints

docs/source/utils.rst

@@ -14,5 +14,6 @@ vizualization <sphx_glr_auto_examples_plot_visualization_utils.py>`.
 
     draw_bounding_boxes
     draw_segmentation_masks
+    draw_keypoints
     make_grid
     save_image

test/test_utils.py

@@ -16,6 +16,8 @@
 
 boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
 
+keypoints = torch.tensor([[[10, 10], [5, 5], [2, 2]], [[20, 20], [30, 30], [3, 3]]], dtype=torch.float)
+
 
 def test_make_grid_not_inplace():
     t = torch.rand(5, 3, 10, 10)
@@ -248,5 +250,58 @@ def test_draw_segmentation_masks_errors():
         utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)
 
 
+def test_draw_keypoints_vanilla():
+    # Keypoints is declared on top as global variable
+    keypoints_cp = keypoints.clone()
+
+    img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
+    img_cp = img.clone()
+    result = utils.draw_keypoints(img, keypoints, colors="red", connectivity=((0, 1),))
+    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_keypoint_vanilla.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)
+    assert_equal(result, expected)
+    # Check that keypoints are not modified inplace
+    assert_equal(keypoints, keypoints_cp)
+    # Check that image is not modified in place
+    assert_equal(img, img_cp)
+
+
+@pytest.mark.parametrize("colors", ["red", "#FF00FF", (1, 34, 122)])
+def test_draw_keypoints_colored(colors):
+    # Keypoints is declared on top as global variable
+    keypoints_cp = keypoints.clone()
+
+    img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
+    img_cp = img.clone()
+    result = utils.draw_keypoints(img, keypoints, colors=colors, connectivity=((0, 1),))
+    assert result.size(0) == 3
+    assert_equal(keypoints, keypoints_cp)
+    assert_equal(img, img_cp)
+
+
+def test_draw_keypoints_errors():
+    h, w = 10, 10
+    img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
+
+    with pytest.raises(TypeError, match="The image must be a tensor"):
+        utils.draw_keypoints(image="Not A Tensor Image", keypoints=keypoints)
+    with pytest.raises(ValueError, match="The image dtype must be"):
+        img_bad_dtype = torch.full((3, h, w), 0, dtype=torch.int64)
+        utils.draw_keypoints(image=img_bad_dtype, keypoints=keypoints)
+    with pytest.raises(ValueError, match="Pass individual images, not batches"):
+        batch = torch.randint(0, 256, size=(10, 3, h, w), dtype=torch.uint8)
+        utils.draw_keypoints(image=batch, keypoints=keypoints)
+    with pytest.raises(ValueError, match="Pass an RGB image"):
+        one_channel = torch.randint(0, 256, size=(1, h, w), dtype=torch.uint8)
+        utils.draw_keypoints(image=one_channel, keypoints=keypoints)
+    with pytest.raises(ValueError, match="keypoints must be of shape"):
+        invalid_keypoints = torch.tensor([[10, 10, 10, 10], [5, 6, 7, 8]], dtype=torch.float)
+        utils.draw_keypoints(image=img, keypoints=invalid_keypoints)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

torchvision/utils.py

@@ -7,7 +7,7 @@
 import torch
 from PIL import Image, ImageDraw, ImageFont, ImageColor
 
-__all__ = ["make_grid", "save_image", "draw_bounding_boxes", "draw_segmentation_masks"]
+__all__ = ["make_grid", "save_image", "draw_bounding_boxes", "draw_segmentation_masks", "draw_keypoints"]
 
 
 @torch.no_grad()
@@ -300,6 +300,76 @@ def draw_segmentation_masks(
     return out.to(out_dtype)
 
 
+@torch.no_grad()
+def draw_keypoints(
+    image: torch.Tensor,
+    keypoints: torch.Tensor,
+    connectivity: Optional[Tuple[Tuple[int, int]]] = None,
+    colors: Optional[Union[str, Tuple[int, int, int]]] = None,
+    radius: int = 2,
+    width: int = 3,
+) -> torch.Tensor:
+
+    """
+    Draws Keypoints on given RGB image.
+    The values of the input image should be uint8 between 0 and 255.
+
+    Args:
+        image (Tensor): Tensor of shape (3, H, W) and dtype uint8.
+        keypoints (Tensor): Tensor of shape (num_instances, K, 2) the K keypoints location for each of the N instances,
+            in the format [x, y].
+        connectivity (Tuple[Tuple[int, int]]]): A Tuple of tuple where,
+            each tuple contains pair of keypoints to be connected.
+        colors (str, Tuple): The color can be represented as
+            PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
+        radius (int): Integer denoting radius of keypoint.
+        width (int): Integer denoting width of line connecting keypoints.
+
+    Returns:
+        img (Tensor[C, H, W]): Image Tensor of dtype uint8 with keypoints drawn.
+    """
+
+    if not isinstance(image, torch.Tensor):
+        raise TypeError(f"The image must be a tensor, got {type(image)}")
+    elif image.dtype != torch.uint8:
+        raise ValueError(f"The image dtype must be uint8, 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")
+
+    if keypoints.ndim != 3:
+        raise ValueError("keypoints must be of shape (num_instances, K, 2)")
+
+    ndarr = image.permute(1, 2, 0).numpy()
+    img_to_draw = Image.fromarray(ndarr)
+    draw = ImageDraw.Draw(img_to_draw)
+    img_kpts = keypoints.to(torch.int64).tolist()
+
+    for kpt_id, kpt_inst in enumerate(img_kpts):
+        for inst_id, kpt in enumerate(kpt_inst):
+            x1 = kpt[0] - radius
+            x2 = kpt[0] + radius
+            y1 = kpt[1] - radius
+            y2 = kpt[1] + radius
+            draw.ellipse([x1, y1, x2, y2], fill=colors, outline=None, width=0)
+
+        if connectivity:
+            for connection in connectivity:
+                start_pt_x = kpt_inst[connection[0]][0]
+                start_pt_y = kpt_inst[connection[0]][1]
+
+                end_pt_x = kpt_inst[connection[1]][0]
+                end_pt_y = kpt_inst[connection[1]][1]
+
+                draw.line(
+                    ((start_pt_x, start_pt_y), (end_pt_x, end_pt_y)),
+                    width=width,
+                )
+
+    return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)
+
+
 def _generate_color_palette(num_masks: int):
     palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
     return [tuple((i * palette) % 255) for i in range(num_masks)]