def test_clip_area_0_degree(self):
for _ in range(50):
num_boxes = 100
boxes_5d = torch.zeros(num_boxes, 5)
boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500)
boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500)
# Convert from (x_ctr, y_ctr, w, h, 0) to (x1, y1, x2, y2)
boxes_4d = torch.zeros(num_boxes, 4)
boxes_4d[:, 0] = boxes_5d[:, 0] - boxes_5d[:, 2] / 2.0
boxes_4d[:, 1] = boxes_5d[:, 1] - boxes_5d[:, 3] / 2.0
boxes_4d[:, 2] = boxes_5d[:, 0] + boxes_5d[:, 2] / 2.0
boxes_4d[:, 3] = boxes_5d[:, 1] + boxes_5d[:, 3] / 2.0
image_size = (500, 600)
test_boxes_4d = Boxes(boxes_4d)
test_boxes_5d = RotatedBoxes(boxes_5d)
# Before clip
areas_4d = test_boxes_4d.area()
areas_5d = test_boxes_5d.area()
self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5))
# After clip
test_boxes_4d.clip(image_size)
test_boxes_5d.clip(image_size)
areas_4d = test_boxes_4d.area()
areas_5d = test_boxes_5d.area()
self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5))
def test_clip_area_arbitrary_angle(self):
num_boxes = 100
boxes_5d = torch.zeros(num_boxes, 5)
boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500)
boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500)
boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800)
clip_angle_threshold = random.uniform(0, 180)
image_size = (500, 600)
test_boxes_5d = RotatedBoxes(boxes_5d)
# Before clip
areas_before = test_boxes_5d.area()
# After clip
test_boxes_5d.clip(image_size, clip_angle_threshold)
areas_diff = test_boxes_5d.area() - areas_before
# the areas should only decrease after clipping
self.assertTrue(torch.all(areas_diff <= 0))
# whenever the box is clipped (thus the area shrinks),
# the angle for the box must be within the clip_angle_threshold
# Note that the clip function will normalize the angle range
# to be within (-180, 180]
self.assertTrue(
torch.all(torch.abs(boxes_5d[:, 4][torch.where(areas_diff < 0)]) < clip_angle_threshold)
)
def func(x):
boxes = RotatedBoxes(x)
test = boxes.to(torch.device("cpu")).tensor
return boxes.area(), test