Fix _parallelogram_to_bounding_boxes

test/test_transforms_v2.py

@@ -7016,6 +7016,29 @@ def test_parallelogram_to_bounding_boxes(input_size, device):
     actual = _parallelogram_to_bounding_boxes(parallelogram)
     torch.testing.assert_close(actual, expected)
 
+    # Test the transformation of a simple parallelogram.
+    #              1
+    #    1-2      /   2
+    #   / /  ->  /   /
+    # 4-3       4   /
+    #              3
+    #
+    #          1
+    # 1-2       \ 2
+    #   \ \  ->  \  \
+    #    4-3       4 \
+    #                 3
+    parallelogram = torch.tensor(
+        [[0, 4, 3, 1, 5, 1, 2, 4], [0, 1, 2, 1, 5, 4, 3, 4]],
+        dtype=torch.float32,
+    )
+    expected = torch.tensor(
+        [[0, 4, 4, 0, 5, 1, 1, 5], [0, 1, 1, 0, 5, 4, 4, 5]],
+        dtype=torch.float32,
+    )
+    actual = _parallelogram_to_bounding_boxes(parallelogram)
+    torch.testing.assert_close(actual, expected)
+
 
 @pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, tv_tensors.Image))
 @pytest.mark.parametrize("data_augmentation", ("hflip", "lsj", "multiscale", "ssd", "ssdlite"))

torchvision/transforms/v2/functional/_geometry.py

@@ -451,54 +451,45 @@ def _parallelogram_to_bounding_boxes(parallelogram: torch.Tensor) -> torch.Tenso
         torch.Tensor: Tensor of same shape as input containing the rectangle coordinates.
                      The output maintains the same dtype as the input.
     """
+    original_shape = parallelogram.shape
     dtype = parallelogram.dtype
     acceptable_dtypes = [torch.float32, torch.float64]
     need_cast = dtype not in acceptable_dtypes
     if need_cast:
         # Up-case to avoid overflow for square operations
         parallelogram = parallelogram.to(torch.float32)
-    out_boxes = parallelogram.clone()
-
-    # Calculate parallelogram diagonal vectors
-    dx13 = parallelogram[..., 4] - parallelogram[..., 0]
-    dy13 = parallelogram[..., 5] - parallelogram[..., 1]
-    dx42 = parallelogram[..., 2] - parallelogram[..., 6]
-    dy42 = parallelogram[..., 3] - parallelogram[..., 7]
-    dx12 = parallelogram[..., 2] - parallelogram[..., 0]
-    dy12 = parallelogram[..., 1] - parallelogram[..., 3]
-    diag13 = torch.sqrt(dx13**2 + dy13**2)
-    diag24 = torch.sqrt(dx42**2 + dy42**2)
-    mask = diag13 > diag24
-
-    # Calculate rotation angle in radians
-    r_rad = torch.atan2(dy12, dx12)
-    cos, sin = torch.cos(r_rad), torch.sin(r_rad)
-
-    # Calculate width using the angle between diagonal and rotation
-    w = torch.where(
-        mask,
-        diag13 * torch.abs(torch.sin(torch.atan2(dx13, dy13) - r_rad)),
-        diag24 * torch.abs(torch.sin(torch.atan2(dx42, dy42) - r_rad)),
-    )
 
-    delta_x = w * cos
-    delta_y = w * sin
-    # Update coordinates to form a rectangle
-    # Keeping the points (x1, y1) and (x3, y3) unchanged.
-    out_boxes[..., 2] = torch.where(mask, parallelogram[..., 0] + delta_x, parallelogram[..., 2])
-    out_boxes[..., 3] = torch.where(mask, parallelogram[..., 1] - delta_y, parallelogram[..., 3])
-    out_boxes[..., 6] = torch.where(mask, parallelogram[..., 4] - delta_x, parallelogram[..., 6])
-    out_boxes[..., 7] = torch.where(mask, parallelogram[..., 5] + delta_y, parallelogram[..., 7])
-
-    # Keeping the points (x2, y2) and (x4, y4) unchanged.
-    out_boxes[..., 0] = torch.where(~mask, parallelogram[..., 2] - delta_x, parallelogram[..., 0])
-    out_boxes[..., 1] = torch.where(~mask, parallelogram[..., 3] + delta_y, parallelogram[..., 1])
-    out_boxes[..., 4] = torch.where(~mask, parallelogram[..., 6] + delta_x, parallelogram[..., 4])
-    out_boxes[..., 5] = torch.where(~mask, parallelogram[..., 7] - delta_y, parallelogram[..., 5])
+    x1, y1, x2, y2, x3, y3, x4, y4 = parallelogram.unbind(-1)
+    cx = (x1 + x3) / 2
+    cy = (y1 + y3) / 2
+
+    # Calculate width, height, and rotation angle of the parallelogram
+    wp = torch.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+    hp = torch.sqrt((x4 - x1) ** 2 + (y4 - y1) ** 2)
+    r12 = torch.atan2(y1 - y2, x2 - x1)
+    r14 = torch.atan2(y1 - y4, x4 - x1)
+    r_rad = r12 - r14
+    sign = torch.where(r_rad > torch.pi / 2, -1, 1)
+    cos, sin = r_rad.cos(), r_rad.sin()
+
+    # Calculate width, height, and rotation angle of the rectangle
+    w = torch.where(wp < hp, wp * sin, wp + hp * cos * sign)
+    h = torch.where(wp > hp, hp * sin, hp + wp * cos * sign)
+    r_rad = torch.where(hp > wp, r14 + torch.pi / 2, r12)
+    cos, sin = r_rad.cos(), r_rad.sin()
+
+    x1 = cx - w / 2 * cos - h / 2 * sin
+    y1 = cy - h / 2 * cos + w / 2 * sin
+    x2 = cx + w / 2 * cos - h / 2 * sin
+    y2 = cy - h / 2 * cos - w / 2 * sin
+    x3 = cx + w / 2 * cos + h / 2 * sin
+    y3 = cy + h / 2 * cos - w / 2 * sin
+    x4 = cx - w / 2 * cos + h / 2 * sin
+    y4 = cy + h / 2 * cos + w / 2 * sin
+    out_boxes = torch.stack((x1, y1, x2, y2, x3, y3, x4, y4), dim=-1).reshape(original_shape)
 
     if need_cast:
         out_boxes = out_boxes.to(dtype)
-
     return out_boxes