def test_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
# M defines a translation: dst(1, 1, h, w) = rst(1, 1, h+1, w+1)
M = tensor(
np.array([[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]],
dtype=np.float32).reshape(M_shape))
outp = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(
outp.numpy(), np.array([[[[5.0, 6.0], [9.0, 10.0]]]],
dtype=np.float32))
def _get_transformed_image(self, image, mat3x3):
"""apply perspective transform to the image
note: there is NO need to guarantee the bottom right element equals 1
Args:
image (Tensor): input images (shape: n * 3 * 112 * 112)
mat3x3 (Tensor): perspective matrix (shape: n * 3 * 3)
Returns:
transformed_image (Tensor): perspectively transformed image
"""
s = self.input_size
transformed_image = F.warp_perspective(image, mat3x3, [s, s])
return transformed_image
def forward(self, data, quad):
"""
data: (1, 3, 48, 160)
quad: (1, 4, 2)
"""
N = quad.shape[0]
dst = F.repeat(self.bb_out, N, axis=0).reshape(-1, 4, 2)
I = F.broadcast_to(self.I, quad.shape)
A = F.broadcast_to(self.A, (N, 8, 8))
A[:, 0:4, 0:2] = quad
A[:, 4:8, 5:6] = I[:, :, 0:1]
A[:, 0:4, 6:8] = -quad * dst[:, :, 0:1]
A[:, 4:8, 3:5] = quad
A[:, 0:4, 2:3] = I[:, :, 0:1]
A[:, 4:8, 6:8] = -quad * dst[:, :, 1:2]
B = dst.transpose(0, 2, 1).reshape(-1, 8, 1)
M = F.concat([F.matmul(F.matinv(A), B)[:, :, 0], I[:, 0:1, 0]],
axis=1).reshape(-1, 3, 3)
new_data = F.warp_perspective(data, M, (48, 160)) # (N, 3, 48, 160)
return {"data": new_data}
def forward(self, data, idx, roi):
N, H, W, C = data.shape
xmax = roi[:, 1, 0]
xmin = roi[:, 0, 0]
ymax = roi[:, 1, 1]
ymin = roi[:, 0, 1]
scale = F.maximum((xmax - xmin) / W, (ymax - ymin) / H)
I = F.broadcast_to(self.I, (N, ))
M = F.broadcast_to(self.M, (N, 3, 3))
M[:, 0, 0] = scale
M[:, 0, 2] = xmin
M[:, 1, 1] = scale
M[:, 1, 2] = ymin
M[:, 2, 2] = I
resized = (F.warp_perspective(data,
M, (H, W),
mat_idx=idx,
border_mode="CONSTANT",
format="NHWC").transpose(
0, 3, 1, 2).astype(np.float32))
return resized
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out