def test_get_boxes_shape(self, device, dtype):
box = Boxes(torch.tensor([[[1.0, 1.0], [3.0, 2.0], [1.0, 2.0], [3.0, 1.0]]], device=device, dtype=dtype))
t_boxes = torch.tensor(
[[[1.0, 1.0], [3.0, 1.0], [1.0, 2.0], [3.0, 2.0]], [[5.0, 4.0], [2.0, 2.0], [5.0, 2.0], [2.0, 4.0]]],
device=device,
dtype=dtype,
) # (2, 4, 2)
boxes = Boxes(t_boxes)
boxes_batch = Boxes(t_boxes[None]) # (1, 2, 4, 2)
# Single box
h, w = box.get_boxes_shape()
assert (h.item(), w.item()) == (2, 3)
# Boxes
h, w = boxes.get_boxes_shape()
assert h.ndim == 1 and w.ndim == 1
assert len(h) == 2 and len(w) == 2
assert (h == torch.as_tensor([2.0, 3.0], device=device)).all() and (
w == torch.as_tensor([3.0, 4.0], device=device)
).all()
# Box batch
h, w = boxes_batch.get_boxes_shape()
assert h.ndim == 2 and w.ndim == 2
assert h.shape == (1, 2) and w.shape == (1, 2)
assert (h == torch.as_tensor([[2.0, 3.0]], device=device)).all() and (
w == torch.as_tensor([[3.0, 4.0]], device=device)
).all()
def test_smoke(self, device, dtype):
def _create_tensor_box():
# Sample two points of the rectangle
points = torch.rand(1, 4, device=device, dtype=dtype)
# Fill according missing points
tensor_boxes = torch.zeros(1, 4, 2, device=device, dtype=dtype)
tensor_boxes[0, 0] = points[0][:2]
tensor_boxes[0, 1, 0] = points[0][2]
tensor_boxes[0, 1, 1] = points[0][1]
tensor_boxes[0, 2] = points[0][2:]
tensor_boxes[0, 3, 0] = points[0][0]
tensor_boxes[0, 3, 1] = points[0][3]
return tensor_boxes
# Validate
assert Boxes(_create_tensor_box()) # Validate 1 box
# 2 boxes without batching (N, 4, 2) where N=2
two_boxes = torch.cat([_create_tensor_box(), _create_tensor_box()])
assert Boxes(two_boxes)
# 2 boxes in batch (B, 1, 4, 2) where B=2
batched_bbox = torch.stack([_create_tensor_box(), _create_tensor_box()])
assert Boxes(batched_bbox)
def test_boxes_to_tensor(self, shape: Tuple[int], device, dtype):
# quadrilateral with randomized vertices to reflect possible transforms.
box = Boxes(torch.as_tensor([[[2, 2], [2, 3], [1, 3], [1, 2]]], device=device, dtype=dtype).view(*shape, 2))
expected_box_xyxy = torch.as_tensor([[1, 2, 3, 4]], device=device, dtype=dtype).view(*shape)
expected_box_xyxy_plus = torch.as_tensor([[1, 2, 2, 3]], device=device, dtype=dtype).view(*shape)
expected_box_xywh = torch.as_tensor([[1, 2, 2, 2]], device=device, dtype=dtype).view(*shape)
expected_vertices = torch.as_tensor([[[1, 2], [3, 2], [3, 4], [1, 4]]], device=device, dtype=dtype).view(
*shape, 2
)
expected_vertices_plus = torch.as_tensor([[[1, 2], [2, 2], [2, 3], [1, 3]]], device=device, dtype=dtype).view(
*shape, 2
)
boxes_xyxy = box.to_tensor(mode='xyxy')
boxes_xyxy_plus = box.to_tensor(mode='xyxy_plus')
boxes_xywh = box.to_tensor(mode='xywh')
boxes_vertices = box.to_tensor(mode='vertices')
boxes_vertices_plus = box.to_tensor(mode='vertices_plus')
assert boxes_xyxy.shape == expected_box_xyxy.shape # type: ignore
assert_allclose(boxes_xyxy, expected_box_xyxy)
assert boxes_xyxy_plus.shape == expected_box_xyxy_plus.shape # type: ignore
assert_allclose(boxes_xyxy_plus, expected_box_xyxy_plus)
assert boxes_xywh.shape == expected_box_xywh.shape # type: ignore
assert_allclose(boxes_xywh, expected_box_xywh)
assert boxes_vertices.shape == expected_vertices.shape # type: ignore
assert_allclose(boxes_vertices, expected_vertices)
assert boxes_vertices_plus.shape == expected_vertices_plus.shape # type: ignore
assert_allclose(boxes_vertices_plus, expected_vertices_plus)
def test_get_boxes_shape_batch(self, device, dtype):
t_box1 = torch.tensor([[[1.0, 1.0], [3.0, 2.0], [3.0, 1.0], [1.0, 2.0]]], device=device, dtype=dtype)
t_box2 = torch.tensor([[[5.0, 2.0], [2.0, 2.0], [5.0, 4.0], [2.0, 4.0]]], device=device, dtype=dtype)
batched_boxes = Boxes(torch.stack([t_box1, t_box2]))
h, w = batched_boxes.get_boxes_shape()
assert h.ndim == 2 and w.ndim == 2
assert h.shape == (2, 1) and w.shape == (2, 1)
assert (h == torch.as_tensor([[2], [3]], device=device)).all() and (
w == torch.as_tensor([[3], [4]], device=device)
).all()
def _wrapper_transform_boxes(quadrilaterals, M):
boxes = Boxes(quadrilaterals)
boxes = boxes.transform_boxes(M)
return boxes.data
def apply_boxes_method(tensor: torch.Tensor, method: str, **kwargs):
boxes = Boxes(tensor)
result = getattr(boxes, method)(**kwargs)
return result.data if isinstance(result, Boxes) else result
def test_boxes_to_mask(self, device, dtype):
t_box1 = torch.tensor(
[[[1.0, 1.0], [3.0, 1.0], [3.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype
) # (1, 4, 2)
t_box2 = torch.tensor(
[[[2.0, 2.0], [4.0, 2.0], [4.0, 5.0], [2.0, 4.0]]], device=device, dtype=dtype
) # (1, 4, 2)
box1, box2 = Boxes(t_box1), Boxes(t_box2)
two_boxes = Boxes(torch.cat([t_box1, t_box2])) # (2, 4, 2)
batched_boxes = Boxes(torch.stack([t_box1, t_box2])) # (2, 1, 4, 2)
height, width = 7, 5
expected_mask1 = torch.tensor(
[
[
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
]
],
device=device,
dtype=dtype,
)
expected_mask2 = torch.tensor(
[
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 0],
]
],
device=device,
dtype=dtype,
)
expected_two_masks = torch.cat([expected_mask1, expected_mask2])
expected_batched_masks = torch.stack([expected_mask1, expected_mask2])
mask1 = box1.to_mask(height, width)
mask2 = box2.to_mask(height, width)
two_masks = two_boxes.to_mask(height, width)
batched_masks = batched_boxes.to_mask(height, width)
assert mask1.shape == expected_mask1.shape
assert_allclose(mask1, expected_mask1)
assert mask2.shape == expected_mask2.shape
assert_allclose(mask2, expected_mask2)
assert two_masks.shape == expected_two_masks.shape
assert_allclose(two_masks, expected_two_masks)
assert batched_masks.shape == expected_batched_masks.shape
assert_allclose(batched_masks, expected_batched_masks)