def parse_transform(node):
ret = torch.eye(4)
for child in node:
if child.tag == 'matrix':
value = torch.from_numpy(\
np.reshape(\
# support both ',' and ' ' seperator
np.fromstring(child.attrib['value'], dtype=np.float32, sep=',' if ',' in child.attrib['value'] else ' '),
(4, 4)))
ret = value @ ret
elif child.tag == 'translate':
x = float(child.attrib['x'])
y = float(child.attrib['y'])
z = float(child.attrib['z'])
value = transform.gen_translate_matrix(torch.tensor([x, y, z]))
ret = value @ ret
elif child.tag == 'scale':
# single scale value
if 'value' in child.attrib:
x = y = z = float(child.attrib['value'])
else:
x = float(child.attrib['x'])
y = float(child.attrib['y'])
z = float(child.attrib['z'])
value = transform.gen_scale_matrix(torch.tensor([x, y, z]))
ret = value @ ret
return ret
def parse_transform(node, param_dict):
ret = torch.eye(4)
for child in node:
if child.tag == 'matrix':
value = torch.from_numpy(\
np.reshape(\
# support both ',' and ' ' seperator
np.fromstring(child.attrib['value'], dtype=np.float32, sep=',' if ',' in child.attrib['value'] else ' '),
(4, 4)))
ret = value @ ret
elif child.tag == 'translate':
x = float(check_default(child.attrib['x'], param_dict))
y = float(check_default(child.attrib['y'], param_dict))
z = float(check_default(child.attrib['z'], param_dict))
value = transform.gen_translate_matrix(torch.tensor([x, y, z]))
ret = value @ ret
elif child.tag == 'scale':
# single scale value
if 'value' in child.attrib:
x = y = z = float(child.attrib['value'])
else:
x = float(check_default(child.attrib['x'], param_dict))
y = float(check_default(child.attrib['y'], param_dict))
z = float(check_default(child.attrib['z'], param_dict))
value = transform.gen_scale_matrix(torch.tensor([x, y, z]))
ret = value @ ret
elif child.tag == 'rotate':
x = float(check_default(child.attrib['x'], param_dict)) if 'x' in child.attrib else 0.0
y = float(check_default(child.attrib['y'], param_dict)) if 'y' in child.attrib else 0.0
z = float(check_default(child.attrib['z'], param_dict)) if 'z' in child.attrib else 0.0
angle = transform.radians(float(check_default(child.attrib['angle'], param_dict)))
axis = np.array([x, y, z])
axis = axis / np.linalg.norm(axis)
cos_theta = math.cos(angle)
sin_theta = math.sin(angle)
mat = torch.zeros(4, 4)
mat[0, 0] = axis[0] * axis[0] + (1.0 - axis[0] * axis[0]) * cos_theta
mat[0, 1] = axis[0] * axis[1] * (1.0 - cos_theta) - axis[2] * sin_theta
mat[0, 2] = axis[0] * axis[2] * (1.0 - cos_theta) + axis[1] * sin_theta
mat[1, 0] = axis[0] * axis[1] * (1.0 - cos_theta) + axis[2] * sin_theta
mat[1, 1] = axis[1] * axis[1] + (1.0 - axis[1] * axis[1]) * cos_theta
mat[1, 2] = axis[1] * axis[2] * (1.0 - cos_theta) - axis[0] * sin_theta
mat[2, 0] = axis[0] * axis[2] * (1.0 - cos_theta) - axis[1] * sin_theta
mat[2, 1] = axis[1] * axis[2] * (1.0 - cos_theta) + axis[0] * sin_theta
mat[2, 2] = axis[2] * axis[2] + (1.0 - axis[2] * axis[2]) * cos_theta
mat[3, 3] = 1.0
ret = mat @ ret
return ret
def parse_transform(node):
ret = torch.eye(4)
for child in node:
if child.tag == 'matrix':
value = torch.from_numpy(\
np.reshape(\
np.fromstring(child.attrib['value'], dtype=np.float32, sep=' '),
(4, 4)))
ret = value @ ret
elif child.tag == 'translate':
x = float(child.attrib['x'])
y = float(child.attrib['y'])
z = float(child.attrib['z'])
value = transform.gen_translate_matrix(torch.tensor([x, y, z]))
ret = value @ ret
elif child.tag == 'scale':
x = float(child.attrib['x'])
y = float(child.attrib['y'])
z = float(child.attrib['z'])
value = transform.gen_scale_matrix(torch.tensor([x, y, z]))
ret = value @ ret
return ret