def extrusion_to_matrix(entity):
"""
Converts an extrusion vector to a rotation matrix that denotes the transformation between world coordinate system
and the entity's own coordinate system (described by the extrusion vector).
"""
def arbitrary_x_axis(extrusion_normal):
world_y = Vector((0, 1, 0))
world_z = Vector((0, 0, 1))
if abs(extrusion_normal[0]) < 1 / 64 and abs(extrusion_normal[1]) < 1 / 64:
a_x = world_y.cross(extrusion_normal)
else:
a_x = world_z.cross(extrusion_normal)
a_x.normalize()
return a_x, extrusion_normal.cross(a_x)
az = Vector(entity.extrusion)
ax, ay = arbitrary_x_axis(az)
ax4 = ax.to_4d()
ay4 = ay.to_4d()
az4 = az.to_4d()
ax4[3] = 0
ay4[3] = 0
az4[3] = 0
translation = Vector((0, 0, 0, 1))
if hasattr(entity, "elevation"):
if type(entity.elevation) is tuple:
translation = Vector(entity.elevation).to_4d()
else:
translation = (az * entity.elevation).to_4d()
return Matrix((ax4, ay4, az4, translation)).transposed()
def extrusion_to_matrix(entity):
"""
Converts an extrusion vector to a rotation matrix that denotes the transformation between world coordinate system
and the entity's own coordinate system (described by the extrusion vector).
"""
def arbitrary_x_axis(extrusion_normal):
world_y = Vector((0, 1, 0))
world_z = Vector((0, 0, 1))
if abs(extrusion_normal[0]) < 1 / 64 and abs(
extrusion_normal[1]) < 1 / 64:
a_x = world_y.cross(extrusion_normal)
else:
a_x = world_z.cross(extrusion_normal)
a_x.normalize()
return a_x, extrusion_normal.cross(a_x)
az = Vector(entity.extrusion)
ax, ay = arbitrary_x_axis(az)
ax4 = ax.to_4d()
ay4 = ay.to_4d()
az4 = az.to_4d()
ax4[3] = 0
ay4[3] = 0
az4[3] = 0
translation = Vector((0, 0, 0, 1))
if hasattr(entity, "elevation"):
if type(entity.elevation) is tuple:
translation = Vector(entity.elevation).to_4d()
else:
translation = (az * entity.elevation).to_4d()
return Matrix((ax4, ay4, az4, translation)).transposed()
def point_camera_to(cam, xyz_target, up=(0, 0, 1)):
"""Points camera to target.
Args:
cam (bpy_types.Object): Camera object.
xyz_target (array_like): Target point in world coordinates.
up (array_like, optional): World vector that, when projected,
points up in the image plane.
"""
logger_name = thisfile + '->point_camera_to()'
up = Vector(up)
xyz_target = Vector(xyz_target)
direction = xyz_target - cam.location
# Rotate camera with quaternion so that `track` aligns with `direction`, and
# world +z, when projected, aligns with camera +y (i.e., points up in image plane)
track = '-Z'
rot_quat = direction.to_track_quat(track, 'Y')
cam.rotation_euler = (0, 0, 0)
cam.rotation_euler.rotate(rot_quat)
# Further rotate camera so that world `up`, when projected, points up on image plane
# We know right now world +z, when projected, points up, so we just need to rotate
# the camera around the lookat direction by an angle
cam_mat, _, _ = get_camera_matrix(cam)
up_proj = cam_mat * up.to_4d()
orig_proj = cam_mat * Vector((0, 0, 0)).to_4d()
try:
up_proj = Vector((up_proj[0] / up_proj[2], up_proj[1] / up_proj[2])) - \
Vector((orig_proj[0] / orig_proj[2], orig_proj[1] / orig_proj[2]))
except ZeroDivisionError:
logger.name = logger_name
logger.error(("w in homogeneous coordinates is 0; "
"camera coincides with the point to project? "
"So can't rotate camera to ensure up vector"))
logger.info(
"Camera '%s' pointed to %s, but with no guarantee on up vector",
cam.name, tuple(xyz_target))
return cam
# +------->
# |
# |
# v
up_proj[1] = -up_proj[1]
# ^
# |
# |
# +------->
a = Vector((0, 1)).angle_signed(up_proj) # clockwise is positive
cam.rotation_euler.rotate(Quaternion(direction, a))
logger.name = logger_name
logger.info("Camera '%s' pointed to %s with world %s pointing up",
cam.name, tuple(xyz_target), tuple(up))
return cam
def point_camera_to(cam, xyz_target, up=(0, 0, 1)):
"""
Point camera to target
Args:
cam: Camera object
bpy_types.Object
xyz_target: Target point in world coordinates
Array_like of 3 floats
up: World vector, when projected, points up in the image plane
Array_like of 3 floats
Optional; defaults to (0, 0, 1)
"""
logger.name = thisfile + '->point_camera_to()'
up = Vector(up)
xyz_target = Vector(xyz_target)
direction = xyz_target - cam.location
# Rotate camera with quaternion so that `track` aligns with `direction`, and
# world +z, when projected, aligns with camera +y (i.e., points up in image plane)
track = '-Z'
rot_quat = direction.to_track_quat(track, 'Y')
cam.rotation_euler.rotate(rot_quat)
# Further rotate camera so that world `up`, when projected, points up on image plane
# We know right now world +z, when projected, points up, so we just need to rotate
# the camera around the lookat direction by an angle
cam_mat, _, _ = get_camera_matrix(cam)
up_proj = cam_mat * up.to_4d()
orig_proj = cam_mat * Vector((0, 0, 0)).to_4d()
up_proj = Vector((up_proj[0] / up_proj[2], up_proj[1] / up_proj[2])) - \
Vector((orig_proj[0] / orig_proj[2], orig_proj[1] / orig_proj[2]))
# +------->
# |
# |
# v
up_proj[1] = -up_proj[1]
# ^
# |
# |
# +------->
a = Vector((0, 1)).angle_signed(up_proj) # clockwise is positive
cam.rotation_euler.rotate(Quaternion(direction, a))
logger.info("Camera '%s' pointed to %s with world %s pointing up",
cam.name, tuple(xyz_target), tuple(up))
return cam
def calculateMVBB(points, edges):
if len(points) < 4:
handleDegenerateCases(points, edges)
# shift the points such that the minimum x, y, z values
# in the entire set of points is 0.
points = np.array(points)
#shift = points.min(axis=0)
#points = points - shift
min_volume = float("inf")
n = len(edges)
specs = None
# try every pair of edges (ordering is not important)
for i2 in range(n):
e2 = edges[i2]
u2 = points[e2[0]] - points[e2[1]]
for i1 in range(i2 + 1, n):
e1 = edges[i1]
u1 = points[e1[0]] - points[e1[1]]
if np.dot(u2, u1) != 0:
continue
# transform the two edges into a orthogonal basis
u = normcross(u2, u1)
v = normcross(u, u2)
w = normcross(u, v)
# project all the points on to the basis u v w
forward, backwards = basisChange(u, v, w)
p = points @ forward
volume, mins, maxes = calcVolume(p)
# we are looking for the minimum volume box
if volume <= min_volume:
min_volume = volume
specs = u, v, w, mins, maxes, backwards
if specs is None:
return handleDegenerateCases(points, edges)
u, v, w, mins, maxes, backwards = specs
# get the corner by using our projections, then shift it to move
# it back into the same origin as the original set of points
mins = mins.tolist()[0]
maxes = maxes.tolist()[0]
corner = u * mins[0] + v * mins[1] + w * mins[2]
#corner += shift
# create the sides which are vectors with the magnitude the length
# of that side
l1 = (maxes[0] - mins[0])
l2 = (maxes[1] - mins[1])
l3 = (maxes[2] - mins[2])
#v1 = u * l1
#v2 = v * l2
#v3 = w * l3
#original return corner, v1, v2, v3
v4 = lambda x: Vector([*x, 0])
vc = Vector((corner + (u * l1 + v * l2 + w * l3) / 2).tolist())
rotTransMatrix = Matrix(list(zip(v4(u), v4(v), v4(w), vc.to_4d())))
return rotTransMatrix, Vector([l1 / 2, l2 / 2, l3 / 2, 0]) #matrix
def scan_advanced(scanner_object,
evd_file=None,
evd_last_scan=True,
timestamp=0.0,
world_transformation=Matrix()):
# threshold for comparing projector and camera rays
thresh = 0.01
inv_scan_x = scanner_object.inv_scan_x
inv_scan_y = scanner_object.inv_scan_y
inv_scan_z = scanner_object.inv_scan_z
x_multiplier = -1.0 if inv_scan_x else 1.0
y_multiplier = -1.0 if inv_scan_y else 1.0
z_multiplier = -1.0 if inv_scan_z else 1.0
start_time = time.time()
max_distance = scanner_object.kinect_max_dist
min_distance = scanner_object.kinect_min_dist
add_blender_mesh = scanner_object.add_scan_mesh
add_noisy_blender_mesh = scanner_object.add_noise_scan_mesh
noise_mu = scanner_object.kinect_noise_mu
noise_sigma = scanner_object.kinect_noise_sigma
noise_scale = scanner_object.kinect_noise_scale
noise_smooth = scanner_object.kinect_noise_smooth
res_x = scanner_object.kinect_xres
res_y = scanner_object.kinect_yres
flength = scanner_object.kinect_flength
WINDOW_INLIER_DISTANCE = scanner_object.kinect_inlier_distance
if res_x < 1 or res_y < 1:
raise ValueError("Resolution must be > 0")
pixel_width = max(0.0001, (math.tan(
(parameters["horiz_fov"] / 2.0) * math.pi / 180.0) * flength) /
max(1.0, res_x / 2.0)) #default:0.0078
pixel_height = max(0.0001, (math.tan(
(parameters["vert_fov"] / 2.0) * math.pi / 180.0) * flength) /
max(1.0, res_y / 2.0)) #default:0.0078
print("%f,%f" % (pixel_width, pixel_height))
cx = float(res_x) / 2.0
cy = float(res_y) / 2.0
evd_buffer = []
rays = [0.0] * res_y * res_x * 6
ray_info = [[0.0, 0.0, 0.0]] * res_y * res_x
baseline = Vector([0.075, 0.0,
0.0]) #Kinect has a baseline of 7.5 centimeters
rayidx = 0
ray = Vector([0.0, 0.0, 0.0])
"""Calculate the rays from the projector"""
for y in range(res_y):
for x in range(res_x):
"""Calculate a vector that originates at the principal point
and points to the pixel in the sensor. This vector is then
scaled to the maximum scanning distance
"""
physical_x = float(x - cx) * pixel_width
physical_y = float(y - cy) * pixel_height
physical_z = -float(flength)
#ray = Vector([physical_x, physical_y, physical_z])
ray.xyz = [physical_x, physical_y, physical_z]
ray.normalize()
final_ray = max_distance * ray
rays[rayidx * 6] = final_ray[0]
rays[rayidx * 6 + 1] = final_ray[1]
rays[rayidx * 6 + 2] = final_ray[2]
rays[rayidx * 6 + 3] = baseline.x
rays[rayidx * 6 + 4] = baseline.y
rays[rayidx * 6 + 5] = baseline.z
""" pitch and yaw are added for completeness, normally they are
not provided by a ToF Camera but can be derived
from the pixel position and the camera parameters.
"""
yaw = math.atan(physical_x / flength)
pitch = math.atan(physical_y / flength)
ray_info[rayidx][0] = yaw
ray_info[rayidx][1] = pitch
ray_info[rayidx][2] = timestamp
rayidx += 1
""" Max distance is increased because the kinect is limited by 4m
_normal distance_ to the imaging plane, We don't need shading in the
first pass.
#TODO: the shading requirements might change when transmission
is implemented (the rays might pass through glass)
"""
returns = blensor.scan_interface.scan_rays(rays, 2.0 * max_distance, True,
True, True, True)
camera_rays = []
projector_ray_index = -1 * numpy.ones(len(returns), dtype=numpy.uint32)
kinect_image = numpy.zeros((res_x * res_y, 16))
kinect_image[:, 3:11] = float('NaN')
kinect_image[:, 11] = -1.0
"""Calculate the rays from the camera to the hit points of the projector rays"""
for i in range(len(returns)):
idx = returns[i][-1]
kinect_image[idx, 12:15] = returns[i][5]
if returns[i][0] < max_distance:
camera_rays.extend([
returns[i][1] + baseline.x, returns[i][2] + baseline.y,
returns[i][3] + baseline.z
])
projector_ray_index[i] = idx
camera_returns = blensor.scan_interface.scan_rays(camera_rays,
2 * max_distance, False,
False, False)
evd_storage = evd.evd_file(evd_file,
res_x,
res_y,
max_distance,
output_image=False,
output_noisy=True,
append_frame_counter=False)
all_quantized_disparities = numpy.empty(res_x * res_y)
all_quantized_disparities[:] = INVALID_DISPARITY
disparity_weight = numpy.empty(res_x * res_y)
disparity_weight[:] = INVALID_DISPARITY
all_quantized_disp_mat = all_quantized_disparities.reshape(res_y, res_x)
disp_weight_mat = disparity_weight.reshape(res_y, res_x)
weights = numpy.array([
1.0 / float((1.2 * x)**2 + (1.2 * y)**2) if x != 0 or y != 0 else 1.0
for x in range(-4, 5) for y in range(-4, 5)
]).reshape((9, 9))
"""Build a quantized disparity map"""
for i in range(len(camera_returns)):
idx = camera_returns[i][-1]
projector_idx = projector_ray_index[
idx] # Get the index of the original ray
if (abs(camera_rays[idx * 3] - camera_returns[i][1]) < thresh and
abs(camera_rays[idx * 3 + 1] - camera_returns[i][2]) < thresh
and
abs(camera_rays[idx * 3 + 2] - camera_returns[i][3]) < thresh
and abs(camera_returns[i][3]) <= max_distance
and abs(camera_returns[i][3]) >= min_distance):
"""The ray hit the projected ray, so this is a valid measurement"""
projector_point = get_uv_from_idx(projector_idx, res_x, res_y)
camera_x = get_pixel_from_world(
camera_rays[idx * 3], camera_rays[idx * 3 + 2],
flength / pixel_width) + random.gauss(noise_mu, noise_sigma)
camera_y = get_pixel_from_world(camera_rays[idx * 3 + 1],
camera_rays[idx * 3 + 2],
flength / pixel_width)
""" Kinect calculates the disparity with an accuracy of 1/8 pixel"""
camera_x_quantized = round(camera_x * 8.0) / 8.0
#I don't know if this accurately represents the kinect
camera_y_quantized = round(camera_y * 8.0) / 8.0
disparity_quantized = camera_x_quantized + projector_point[0]
if projector_idx >= 0:
all_quantized_disparities[projector_idx] = disparity_quantized
processed_disparities = numpy.empty(res_x * res_y)
fast_9x9_window(all_quantized_disparities, res_x, res_y,
processed_disparities, noise_smooth, noise_scale)
"""We reuse the vector objects to spare us the object creation every
time
"""
v = Vector([0.0, 0.0, 0.0])
vn = Vector([0.0, 0.0, 0.0])
"""Check if the rays of the camera meet with the rays of the projector and
add them as valid returns if they do"""
image_idx = 0
for i in range(len(camera_returns)):
idx = camera_returns[i][-1]
projector_idx = projector_ray_index[
idx] # Get the index of the original ray
camera_x, camera_y = get_uv_from_idx(projector_idx, res_x, res_y)
if projector_idx >= 0:
disparity_quantized = processed_disparities[projector_idx]
else:
disparity_quantized = INVALID_DISPARITY
if disparity_quantized < INVALID_DISPARITY and disparity_quantized != 0.0:
disparity_quantized = -disparity_quantized
Z_quantized = (flength *
(baseline.x)) / (disparity_quantized * pixel_width)
X_quantized = baseline.x + Z_quantized * camera_x * pixel_width / flength
Y_quantized = baseline.y + Z_quantized * camera_y * pixel_width / flength
Z_quantized = -(Z_quantized + baseline.z)
v.xyz=[x_multiplier*(returns[idx][1]+baseline.x),\
y_multiplier*(returns[idx][2]+baseline.y),\
z_multiplier*(returns[idx][3]+baseline.z)]
vector_length = math.sqrt(v[0]**2 + v[1]**2 + v[2]**2)
vt = (world_transformation * v.to_4d()).xyz
vn.xyz = [
x_multiplier * X_quantized, y_multiplier * Y_quantized,
z_multiplier * Z_quantized
]
vector_length_noise = vn.magnitude
#TODO@mgschwan: prevent object creation here too
v_noise = (world_transformation * vn.to_4d()).xyz
kinect_image[projector_idx] = [
ray_info[projector_idx][2], 0.0, 0.0, -returns[idx][3],
-Z_quantized, vt[0], vt[1], vt[2], v_noise[0], v_noise[1],
v_noise[2], returns[idx][4], returns[idx][5][0],
returns[idx][5][1], returns[idx][5][2], projector_idx
]
image_idx += 1
else:
"""Occlusion"""
#FIXME: Dirty hack to signal we've got an occluded/invalid value
kinect_image[projector_idx] = [
0.0, 0.0, 0.0, 0, numpy.nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
returns[idx][4], returns[idx][5][0], returns[idx][5][1],
returns[idx][5][2], projector_idx
]
pass
for e in kinect_image:
evd_storage.addEntry(timestamp=e[0],
yaw=e[1],
pitch=e[2],
distance=e[3],
distance_noise=e[4],
x=e[5],
y=e[6],
z=e[7],
x_noise=e[8],
y_noise=e[9],
z_noise=e[10],
object_id=e[11],
color=[e[12], e[13], e[14]],
idx=e[15])
if evd_file:
evd_storage.appendEvdFile()
if not evd_storage.isEmpty():
scan_data = numpy.array(evd_storage.buffer)
additional_data = None
if scanner_object.store_data_in_mesh:
additional_data = evd_storage.buffer
if add_blender_mesh:
mesh_utils.add_mesh_from_points_tf(scan_data[:, 5:8],
"Scan",
world_transformation,
buffer=additional_data)
if add_noisy_blender_mesh:
mesh_utils.add_mesh_from_points_tf(scan_data[:, 8:11],
"NoisyScan",
world_transformation,
buffer=additional_data)
bpy.context.scene.update()
end_time = time.time()
scan_time = end_time - start_time
print("Elapsed time: %.3f" % (scan_time))
return True, 0.0, scan_time
def scan_advanced(scanner_object, evd_file=None,
evd_last_scan=True,
timestamp = 0.0,
world_transformation=Matrix()):
# threshold for comparing projector and camera rays
thresh = 0.01
inv_scan_x = scanner_object.inv_scan_x
inv_scan_y = scanner_object.inv_scan_y
inv_scan_z = scanner_object.inv_scan_z
x_multiplier = -1.0 if inv_scan_x else 1.0
y_multiplier = -1.0 if inv_scan_y else 1.0
z_multiplier = -1.0 if inv_scan_z else 1.0
start_time = time.time()
max_distance = scanner_object.kinect_max_dist
min_distance = scanner_object.kinect_min_dist
add_blender_mesh = scanner_object.add_scan_mesh
add_noisy_blender_mesh = scanner_object.add_noise_scan_mesh
noise_mu = scanner_object.kinect_noise_mu
noise_sigma = scanner_object.kinect_noise_sigma
noise_scale = scanner_object.kinect_noise_scale
noise_smooth = scanner_object.kinect_noise_smooth
res_x = scanner_object.kinect_xres
res_y = scanner_object.kinect_yres
flength = scanner_object.kinect_flength
WINDOW_INLIER_DISTANCE = scanner_object.kinect_inlier_distance
if res_x < 1 or res_y < 1:
raise ValueError("Resolution must be > 0")
pixel_width = 0.0078
pixel_height = 0.0078
cx = float(res_x) /2.0
cy = float(res_y) /2.0
evd_buffer = []
rays = [0.0]*res_y*res_x*6
ray_info = [[0.0,0.0,0.0]]*res_y*res_x
baseline = Vector([0.075,0.0,0.0]) #Kinect has a baseline of 7.5 centimeters
rayidx=0
ray = Vector([0.0,0.0,0.0])
"""Calculate the rays from the projector"""
for y in range(res_y):
for x in range(res_x):
"""Calculate a vector that originates at the principal point
and points to the pixel in the sensor. This vector is then
scaled to the maximum scanning distance
"""
physical_x = float(x-cx) * pixel_width
physical_y = float(y-cy) * pixel_height
physical_z = -float(flength)
#ray = Vector([physical_x, physical_y, physical_z])
ray.xyz=[physical_x, physical_y, physical_z]
ray.normalize()
final_ray = max_distance*ray
rays[rayidx*6] = final_ray[0]
rays[rayidx*6+1] = final_ray[1]
rays[rayidx*6+2] = final_ray[2]
rays[rayidx*6+3] = baseline.x
rays[rayidx*6+4] = baseline.y
rays[rayidx*6+5] = baseline.z
""" pitch and yaw are added for completeness, normally they are
not provided by a ToF Camera but can be derived
from the pixel position and the camera parameters.
"""
yaw = math.atan(physical_x/flength)
pitch = math.atan(physical_y/flength)
ray_info[rayidx][0] = yaw
ray_info[rayidx][1] = pitch
ray_info[rayidx][2] = timestamp
rayidx += 1
""" Max distance is increased because the kinect is limited by 4m
_normal distance_ to the imaging plane, We don't need shading in the
first pass.
#TODO: the shading requirements might change when transmission
is implemented (the rays might pass through glass)
"""
returns = blensor.scan_interface.scan_rays(rays, 2.0*max_distance, True,True,True,True)
camera_rays = []
projector_ray_index = -1 * numpy.ones(len(returns), dtype=numpy.uint32)
kinect_image = numpy.zeros((res_x*res_y,16))
kinect_image[:,3:11] = float('NaN')
kinect_image[:,11] = -1.0
"""Calculate the rays from the camera to the hit points of the projector rays"""
for i in range(len(returns)):
idx = returns[i][-1]
kinect_image[idx,12:15] = returns[i][5]
if returns[i][0] < max_distance:
camera_rays.extend([returns[i][1]+baseline.x, returns[i][2]+baseline.y,
returns[i][3]+baseline.z])
projector_ray_index[i] = idx
camera_returns = blensor.scan_interface.scan_rays(camera_rays, 2*max_distance, False,False,False)
verts = []
verts_noise = []
evd_storage = evd.evd_file(evd_file, res_x, res_y, max_distance)
all_quantized_disparities = numpy.empty(res_x*res_y)
all_quantized_disparities[:] = INVALID_DISPARITY
disparity_weight = numpy.empty(res_x*res_y)
disparity_weight[:] = INVALID_DISPARITY
all_quantized_disp_mat = all_quantized_disparities.reshape(res_y,res_x)
disp_weight_mat = disparity_weight.reshape(res_y,res_x)
weights = numpy.array([1.0/float((1.2*x)**2+(1.2*y)**2) if x!=0 or y!=0 else 1.0 for x in range(-4,5) for y in range (-4,5)]).reshape((9,9))
"""Build a quantized disparity map"""
for i in range(len(camera_returns)):
idx = camera_returns[i][-1]
projector_idx = projector_ray_index[idx] # Get the index of the original ray
if (abs(camera_rays[idx*3]-camera_returns[i][1]) < thresh and
abs(camera_rays[idx*3+1]-camera_returns[i][2]) < thresh and
abs(camera_rays[idx*3+2]-camera_returns[i][3]) < thresh and
abs(camera_returns[i][3]) <= max_distance and
abs(camera_returns[i][3]) >= min_distance):
"""The ray hit the projected ray, so this is a valid measurement"""
projector_point = get_uv_from_idx(projector_idx, res_x,res_y)
camera_x = get_pixel_from_world(camera_rays[idx*3],camera_rays[idx*3+2],
flength/pixel_width) + random.gauss(noise_mu, noise_sigma)
camera_y = get_pixel_from_world(camera_rays[idx*3+1],camera_rays[idx*3+2],
flength/pixel_width)
""" Kinect calculates the disparity with an accuracy of 1/8 pixel"""
camera_x_quantized = round(camera_x*8.0)/8.0
#I don't know if this accurately represents the kinect
camera_y_quantized = round(camera_y*8.0)/8.0
disparity_quantized = camera_x_quantized + projector_point[0]
if projector_idx >= 0:
all_quantized_disparities[projector_idx] = disparity_quantized
processed_disparities = numpy.empty(res_x*res_y)
fast_9x9_window(all_quantized_disparities, res_x, res_y, processed_disparities, noise_smooth, noise_scale)
"""We reuse the vector objects to spare us the object creation every
time
"""
v = Vector([0.0,0.0,0.0])
vn = Vector([0.0,0.0,0.0])
"""Check if the rays of the camera meet with the rays of the projector and
add them as valid returns if they do"""
image_idx = 0
for i in range(len(camera_returns)):
idx = camera_returns[i][-1]
projector_idx = projector_ray_index[idx] # Get the index of the original ray
camera_x,camera_y = get_uv_from_idx(projector_idx, res_x,res_y)
if projector_idx >= 0:
disparity_quantized = processed_disparities[projector_idx]
else:
disparity_quantized = INVALID_DISPARITY
if disparity_quantized < INVALID_DISPARITY and disparity_quantized != 0.0:
disparity_quantized = -disparity_quantized
Z_quantized = (flength*(baseline.x))/(disparity_quantized*pixel_width)
X_quantized = baseline.x+Z_quantized*camera_x*pixel_width/flength
Y_quantized = baseline.y+Z_quantized*camera_y*pixel_width/flength
Z_quantized = -(Z_quantized+baseline.z)
v.xyz=[x_multiplier*(returns[idx][1]+baseline.x),\
y_multiplier*(returns[idx][2]+baseline.y),\
z_multiplier*(returns[idx][3]+baseline.z)]
vector_length = math.sqrt(v[0]**2+v[1]**2+v[2]**2)
vt = (world_transformation * v.to_4d()).xyz
verts.append ( vt )
vn.xyz = [x_multiplier*X_quantized,y_multiplier*Y_quantized,z_multiplier*Z_quantized]
vector_length_noise = vn.magnitude
#TODO@mgschwan: prevent object creation here too
v_noise = (world_transformation * vn.to_4d()).xyz
verts_noise.append( v_noise )
kinect_image[projector_idx] = [ray_info[projector_idx][2],
0.0, 0.0, -returns[idx][3], -Z_quantized, vt[0],
vt[1], vt[2], v_noise[0], v_noise[1], v_noise[2],
returns[idx][4], returns[idx][5][0], returns[idx][5][1],
returns[idx][5][2],projector_idx]
image_idx += 1
else:
"""Occlusion"""
pass
for e in kinect_image:
evd_storage.addEntry(timestamp = e[0], yaw = e[1], pitch=e[2],
distance=e[3], distance_noise=e[4], x=e[5], y=e[6], z=e[7],
x_noise=e[8], y_noise=e[9], z_noise=e[10], object_id=e[11],
color=[e[12],e[13],e[14]], idx=e[15])
if evd_file:
evd_storage.appendEvdFile()
if add_blender_mesh:
mesh_utils.add_mesh_from_points_tf(verts, "Scan", world_transformation)
if add_noisy_blender_mesh:
mesh_utils.add_mesh_from_points_tf(verts_noise, "NoisyScan", world_transformation)
bpy.context.scene.update()
end_time = time.time()
scan_time = end_time-start_time
print ("Elapsed time: %.3f"%(scan_time))
return True, 0.0, scan_time
def distance(self, other:Vector):
"""平面とVectorの距離を返す"""
return self.dot(other.to_4d())
def __new__(cls, location=Vector(), normal=ZAXIS, rotation=Quaternion()):
loc = Vector(location)
nor = Vector(normal).normalized()
vector = nor.to_4d()
vector[3] = -nor.dot(loc)
return Vector.__new__(cls, vector)