def fusion(self, depthmaps, Rs):
"""
Fuse the rendered depth maps.
:param depthmaps: depth maps
:type depthmaps: numpy.ndarray
:param Rs: rotation matrices corresponding to views
:type Rs: [numpy.ndarray]
:return: (T)SDF
:rtype: numpy.ndarray
"""
Ks = self.fusion_intrisics.reshape((1, 3, 3))
Ks = np.repeat(Ks, len(depthmaps), axis=0).astype(np.float32)
Ts = []
for i in range(len(Rs)):
Rs[i] = Rs[i]
Ts.append(np.array([0, 0, 1]))
Ts = np.array(Ts).astype(np.float32)
Rs = np.array(Rs).astype(np.float32)
depthmaps = np.array(depthmaps).astype(np.float32)
views = libfusion.PyViews(depthmaps, Ks, Rs, Ts)
# Note that this is an alias defined as libfusiongpu.tsdf_gpu or libfusioncpu.tsdf_cpu!
return compute_tsdf(views, self.options.resolution,
self.options.resolution, self.options.resolution,
self.voxel_size, self.truncation, False)
def fusion(self, depthmaps, Rs):
"""
Fuse the rendered depth maps.
:param depthmaps: depth maps
:type depthmaps: numpy.ndarray
:param Rs: rotation matrices corresponding to views
:type Rs: [numpy.ndarray]
:return: (T)SDF
:rtype: numpy.ndarray
"""
Ks = self.fusion_intrisics.reshape((1, 3, 3))
Ks = np.repeat(Ks, len(depthmaps), axis=0).astype(np.float32)
Ts = []
for i in range(len(Rs)):
Rs[i] = Rs[i]
Ts.append(np.array([0, 0, 1]))
data_dir = self.options.depth_dir.replace('depth', 'data')
if not os.path.exists(data_dir):
os.makedirs(data_dir)
for i in range(len(Rs)):
R = Rs[i]
T = Ts[i]
extrinsics = np.eye(4)
extrinsics[:3, :3] = R
extrinsics[:3, 3] = T
np.savetxt(os.path.join(data_dir, '{}.extrinsics.txt'.format(i)),
extrinsics)
intrinsics = self.fusion_intrisics
np.savetxt(os.path.join(data_dir, '{}.intrinsics.txt'.format(i)),
intrinsics)
depth = np.copy(depthmaps[i])
depth *= 1000
depth = depth.astype(np.uint16)
cv2.imwrite(
os.path.expanduser(
os.path.join(data_dir, '{}.depth.png'.format(i))), depth)
Ts = np.array(Ts).astype(np.float32)
Rs = np.array(Rs).astype(np.float32)
depthmaps = np.array(depthmaps).astype(np.float32)
views = libfusion.PyViews(depthmaps, Ks, Rs, Ts)
# Note that this is an alias defined as libfusiongpu.tsdf_gpu or libfusioncpu.tsdf_cpu!
return compute_tsdf(views, self.options.resolution,
self.options.resolution, self.options.resolution,
self.voxel_size, self.truncation, False)
def fusion(depthmaps, Rs):
"""
Fuse the rendered depth maps.
:param depthmaps: depth maps
:type depthmaps: numpy.ndarray
:param Rs: rotation matrices corresponding to views
:type Rs: [numpy.ndarray]
:return: (T)SDF
:rtype: numpy.ndarray
"""
Ks = np.array([[
config['watertight_rendering']['focal_length_x'], 0,
config['watertight_rendering']['principal_point_x']
],
[
0, config['watertight_rendering']['focal_length_y'],
config['watertight_rendering']['principal_point_y']
], [0, 0, 1]])
Ks = Ks.reshape((1, 3, 3))
Ks = np.repeat(Ks, len(depthmaps), axis=0).astype(np.float32)
Ts = []
for i in range(len(Rs)):
Rs[i] = Rs[i]
Ts.append(np.array(config['watertight_rendering']['mesh_center']))
Ts = np.array(Ts).astype(np.float32)
Rs = np.array(Rs).astype(np.float32)
depthmaps = np.array(depthmaps).astype(np.float32)
views = libfusion.PyViews(depthmaps, Ks, Rs, Ts)
# Note that this is an alias defined as libfusiongpu.tsdf_gpu or libfusioncpu.tsdf_cpu!
return compute_tsdf(views, config['watertight_fusion']['resolution'],
config['watertight_fusion']['resolution'],
config['watertight_fusion']['resolution'],
config['watertight_fusion']['voxel_size'],
config['watertight_fusion']['truncation'], False)
