def rotate_and_flip(tensor,device,p=0.5):
for i in range(tensor.shape[0]):
random_number=np.random.uniform()
if random_number < p:
center = torch.ones(tensor.shape[1], 2).to(device)
center[:, 0] = tensor.shape[3] / 2 # x
center[:, 1] = tensor.shape[2] / 2 # y
#scale: torch.tensor = torch.ones(1)#*np.random.uniform(0.8,1.2)
angle = torch.tensor([np.random.randint(-90,90,)*np.ones(tensor.shape[1])]).squeeze().to(device).float()
#print(angle.shape)
#print(tensor[i].shape)
#M = kornia.get_rotation_matrix2d(center, angle, scale)#.to(device)
#Mt = torch.ones((tensor.shape[0],2,3))
#Mt[:] = M
#Mt=Mt.to(device)
#tensor[:,j]=kornia.warp_affine(tensor[:,j], Mt, dsize=(tensor.shape[3], tensor.shape[4]))
#print(tensor.dtype)
tensor[i]=kornia.rotate(tensor[i],angle,center)
random_number=np.random.uniform()
if random_number < p:
tensor[i,:]=kornia.hflip(tensor[i,:])
random_number=np.random.uniform()
if random_number < p:
tensor[i,:]=kornia.vflip(tensor[i,:])
return tensor
def test_affine_rotate(self, device):
torch.manual_seed(0)
angle = torch.rand(1, device=device) * 90.0
input = torch.rand(1, 2, 3, 4, device=device)
transform = kornia.Affine(angle=angle).to(device)
actual = transform(input)
expected = kornia.rotate(input, angle)
assert_allclose(actual, expected)
def test_affine_rotate(self, device, dtype):
# TODO: Remove when #666 is implemented
if device.type == 'cuda':
pytest.skip("Currently breaks in CUDA." "See https://github.com/kornia/kornia/issues/666")
torch.manual_seed(0)
angle = torch.rand(1, device=device, dtype=dtype) * 90.0
input = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
transform = kornia.Affine(angle=angle).to(device=device, dtype=dtype)
actual = transform(input)
expected = kornia.rotate(input, angle)
assert_close(actual, expected, atol=1e-4, rtol=1e-4)
def rotate_dgm(data, n_trans=5, random_rotate=False):
if random_rotate:
theta_list = random.sample(list(np.arange(1, 359)), n_trans)
else:
theta_list = np.arange(10, 360, int(360 / n_trans))
data = torch.cat([
data if theta == 0 else dgm.rotate(data,
torch.Tensor([theta]).type_as(data))
for theta in theta_list
],
dim=0)
return data
def grad_rot(input, a, b, c):
rot = rotate(input,
torch.tensor(30., dtype=input.dtype, device=device))
return enhance.equalize_clahe(rot, a, b, c)
def _transform(self, img: torch.Tensor,
param: torch.Tensor) -> torch.Tensor:
return kornia.rotate(img, param)
def Rotate(x, v):
return kornia.rotate(x, v)
def rotate(img: torch.Tensor, mag: torch.Tensor) -> torch.Tensor:
return kornia.rotate(img, mag)
