def __rotate(self, axis, deg, local):
if local:
row = axis.tolist().index(1)
localBasis = self.orientation.matrix33[row, :]
rot = Quaternion.from_axis_rotation(localBasis,
np.deg2rad(deg),
dtype=np.float32)
else:
rot = Quaternion.from_axis_rotation(axis,
np.deg2rad(deg),
dtype=np.float32)
self.orientation *= rot
def roll(self, value):
"""Rotate using the forward direction
"""
rotation = Quaternion.from_axis_rotation(self._forward,
value * self._speed)
self._up = normalize(quaternion.apply_to_vector(rotation, self._up))
return self
def yaw(self, value):
"""Rotate using up direction
"""
rotation = Quaternion.from_axis_rotation(self._up, value * self._speed)
self._forward = normalize(
quaternion.apply_to_vector(rotation, self._forward))
return self
def mouseMoveEvent(self, event):
pos = event.pos()
# compute point on sphere under pointer
(w, h) = self.viewport
t = (2*self.old_pos.x() - w) / float(w)
u = -(2*self.old_pos.y() - h) / float(h)
# compute inverse of view transform ignoring rotation
m = Matrix44.from_translation(Vector3([0, 0, -self.zoom])) * self.projTransform
m = matrix44.inverse(m)
rayOri = m * Vector3([t, u, -1])
rayEnd = m * Vector3([t, u, 1])
rayDir = rayEnd - rayOri
self.picked = intersectRayUnitSphere(rayOri, rayDir)
# rotate on left-drag
if event.buttons() & QtCore.Qt.LeftButton > 0:
# the rotation vector is the displacement vector rotated by 90 degrees
dx = pos.x() - self.old_pos.x()
dy = pos.y() - self.old_pos.y()
if dx == 0 and dy == 0:
return
v = Vector3([dy, dx, 0])
# update the current orientation
self.layers.multiplyOrientation(Quaternion.from_axis_rotation(
-v.normalised,
-v.length * 0.002,
))
elif event.buttons() & QtCore.Qt.RightButton > 0:
dz = pos.y() - self.old_pos.y()
self.zoom = max(0, self.zoom + dz / 100.0)
self.old_pos = pos
self.update()
def convert_rvec_to_quaternion(self, rvec):
'''Convert rvec (which is log quaternion) to quaternion'''
theta = np.sqrt(rvec[0] * rvec[0] + rvec[1] * rvec[1] + rvec[2] * rvec[2]) # in radians
raxis = [rvec[0] / theta, rvec[1] / theta, rvec[2] / theta]
# pyrr's Quaternion (order is XYZW), https://pyrr.readthedocs.io/en/latest/oo_api_quaternion.html
return Quaternion.from_axis_rotation(raxis, theta)
def orient(self, x, y):
""" Orient the current camera with the current x, y
The function will use two quaternions for computing the final rotation. By using
the current up and side vectors of the camera. The angle used for the rotations
are x and y passed by parameters. The function will normalize the new axis vectors
for the camera by the current rotation.
Remark: there is no translation like in Orbit functionality.
"""
side = normalize(np.cross(self._up, self._forward))
rotationYaw = Quaternion.from_axis_rotation(
self._up, -x * self._speed * self._sensitivity)
rotationPitch = Quaternion.from_axis_rotation(
side, y * self._speed * self._sensitivity)
rotation = rotationYaw * rotationPitch
self._forward = normalize(
quaternion.apply_to_vector(rotation, self._forward))
return self
def pitch(self, value):
""" Rotate using cross product between up and forward vectors (side).
"""
side = normalize(np.cross(self._up, self._forward))
rotation = Quaternion.from_axis_rotation(side, value * self._speed)
self._forward = normalize(
quaternion.apply_to_vector(rotation, self._forward))
self._up = normalize(np.cross(self._forward, side))
return self
def computeForwardKinematics(self):
s_pos, s_rot, joint_axis = self.prev.computeForwardKinematics();
p_pos, p_rot = s_pos[-1], s_rot[-1];
c_rot = p_rot * Quaternion.from_axis_rotation(self.rot_ax, self.start_angle + self.angle);
c_pos = p_pos + c_rot * self.disp;
ja = p_rot * Vector3(self.rot_ax);
return (s_pos + [c_pos]), (s_rot + [c_rot]), (joint_axis + [ja]);
def computeForwardKinematics(self):
s_pos, s_rot, joint_axis = self.prev.computeForwardKinematics()
p_pos, p_rot = s_pos[-1], s_rot[-1]
c_rot = p_rot * Quaternion.from_axis_rotation(
self.rot_ax, self.start_angle + self.angle)
c_pos = p_pos + c_rot * self.disp
ja = p_rot * Vector3(self.rot_ax)
return (s_pos + [c_pos]), (s_rot + [c_rot]), (joint_axis + [ja])
def mochae(self, timedelta: float) -> None:
"""Do moving, but look slowly."""
comp = self.dir ^ self.movdir # Comparison reference, for wisity checking.
rent = arctan2(self.ure | comp, self.movdir | self.dir) # Absolute angle of movdir?
if abs(rent) <= abs(self.rotspe * timedelta):
self.dir = self.movdir.copy() # Snap to alignment, don't risk rounding errors.
else:
r = Quaternion.from_axis_rotation(comp if comp.length else self.ure, self.rotspe * timedelta)
self.dir = r * self.dir # move over a little.
if self.stat == self.states.forw:
tent, rest = self.movdir * self.latspe * timedelta, self.orgp + self.movdir*2 - self.pos
if tent.length > rest.length: # Snap to grid.
self.pos = ((self.pos + rest) * 2).round() / 2
self.stat = self.states.stop
else:
self.pos += tent
def solve_pnp(self, cuboid2d_points, pnp_algorithm = None):
"""
Detects the rotation and traslation
of a cuboid object from its vertexes'
2D location in the image
"""
# Fallback to default PNP algorithm base on OpenCV version
if pnp_algorithm is None:
if CuboidPNPSolver.cv2majorversion == 2:
pnp_algorithm = cv2.CV_ITERATIVE
elif CuboidPNPSolver.cv2majorversion == 3:
pnp_algorithm = cv2.SOLVEPNP_ITERATIVE
elif CuboidPNPSolver.cv2majorversion == 4:
pnp_algorithm = cv2.SOLVEPNP_ITERATIVE
else:
raise Exception("Invalid OpenCV version")
# Alternative algorithms:
# pnp_algorithm = SOLVE_PNP_P3P
# pnp_algorithm = SOLVE_PNP_EPNP
location = None
quaternion = None
projected_points = cuboid2d_points
cuboid3d_points = np.array(self._cuboid3d.get_vertices())
obj_2d_points = []
obj_3d_points = []
for i in range(CuboidVertexType.TotalVertexCount):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if (check_point_2d is None):
continue
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
obj_2d_points = np.array(obj_2d_points, dtype=float)
obj_3d_points = np.array(obj_3d_points, dtype=float)
valid_point_count = len(obj_2d_points)
# Can only do PNP if we have more than 3 valid points
is_points_valid = valid_point_count >= 4
if is_points_valid:
ret, rvec, tvec = cv2.solvePnP(
obj_3d_points,
obj_2d_points,
self._camera_intrinsic_matrix,
self._dist_coeffs,
flags=pnp_algorithm
)
if ret:
location = list(x[0] for x in tvec)
quaternion = self.convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(cuboid3d_points, rvec, tvec, self._camera_intrinsic_matrix, self._dist_coeffs)
projected_points = np.squeeze(projected_points)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
if z < 0:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
return location, quaternion, projected_points
def solve_pnp(self, cuboid2d_points, pnp_algorithm=None):
"""
Detects the rotation and traslation
of a cuboid object from its vertexes'
2D location in the image
"""
location = None
quaternion = None
projected_points = cuboid2d_points
cuboid3d_points = np.array(self._cuboid3d.get_vertices())
obj_2d_points = []
obj_3d_points = []
for i in range(CuboidVertexType.TotalVertexCount):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if (check_point_2d is None):
continue
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
obj_2d_points = np.array(obj_2d_points, dtype=float)
obj_3d_points = np.array(obj_3d_points, dtype=float)
valid_point_count = len(obj_2d_points)
# print(valid_point_count, "valid points found" )
# Set PNP algorithm based on OpenCV version and number of valid points
is_points_valid = False
if pnp_algorithm is None:
if CuboidPNPSolver.cv2majorversion == 2:
is_points_valid = True
pnp_algorithm = cv2.CV_ITERATIVE
elif CuboidPNPSolver.cv2majorversion > 2:
if valid_point_count >= 6:
is_points_valid = True
pnp_algorithm = cv2.SOLVEPNP_ITERATIVE
elif valid_point_count >= 4:
is_points_valid = True
pnp_algorithm = cv2.SOLVEPNP_P3P
# This algorithm requires EXACTLY four points, so we truncate our
# data
obj_3d_points = obj_3d_points[:4]
obj_2d_points = obj_2d_points[:4]
# Alternative algorithms:
# pnp_algorithm = SOLVE_PNP_EPNP
else:
assert False, "DOPE will not work with versions of OpenCV earlier than 2.0"
if is_points_valid:
try:
ret, rvec, tvec = cv2.solvePnP(obj_3d_points,
obj_2d_points,
self._camera_intrinsic_matrix,
self._dist_coeffs,
flags=pnp_algorithm)
except:
# solvePnP will assert if there are insufficient points for the
# algorithm
print("cv2.solvePnP failed with an error")
ret = False
if ret:
location = list(x[0] for x in tvec)
quaternion = self.convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(
cuboid3d_points, rvec, tvec, self._camera_intrinsic_matrix,
self._dist_coeffs)
projected_points = np.squeeze(projected_points)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
if z < 0:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(
location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
return location, quaternion, projected_points
def _eval(class_name, path_to_data_dir, path_to_checkpoint, img_prefix):
# load pre-trained model
model = get_model(trunk='vgg19')
model = model.cuda()
use_vgg(model, './model', 'vgg19')
print("=> Load pre-trained model from {}".format(path_to_checkpoint))
model.load_state_dict(torch.load(path_to_checkpoint))
model.eval()
# parameter of object size for pnp solver
print("=> Load {} object size".format(class_name))
path_to_object_seetings = os.path.join(path_to_data_dir,
'_object_settings.json')
if not os.path.exists(path_to_object_seetings):
raise FileNotFoundError(path_to_object_seetings)
object_list = json.load(open(path_to_object_seetings))['exported_objects']
object_size = None
for obj in object_list:
if obj['class'].find(class_name) != -1:
object_size = obj['cuboid_dimensions']
if not object_size:
raise ValueError("Object size is none")
_cuboid3d = Cuboid3d(object_size)
cuboid3d_points = np.array(_cuboid3d.get_vertices())
# parameter of camera for pnp solver
path_to_camera_seetings = os.path.join(path_to_data_dir,
'_camera_settings.json')
if not os.path.exists(path_to_camera_seetings):
raise FileNotFoundError(path_to_camera_seetings)
intrinsic_settings = json.load(open(
path_to_camera_seetings))['camera_settings'][0]['intrinsic_settings']
matrix_camera = np.zeros((3, 3))
matrix_camera[0, 0] = intrinsic_settings['fx']
matrix_camera[1, 1] = intrinsic_settings['fy']
matrix_camera[0, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[1, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[2, 2] = 1
try:
dist_coeffs = np.array(
json.load(open(path_to_camera_seetings))['camera_settings'][0]
["distortion_coefficients"])
except KeyError:
dist_coeffs = np.zeros((4, 1))
# dataloader
val_dataset = Dataset(path_to_data=path_to_data_dir,
class_name=class_name,
split='val',
img_prefix=img_prefix)
val_dataloader = DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
drop_last=False)
correct = 0
wrong = 0
# set threshold (cm)
threshold = 3.0
for batch_index, (images, _, _, location_targets,
ratio) in tqdm(enumerate(val_dataloader)):
images = images.cuda()
output, _ = model(images)
line, vertex = output[0], output[1]
line, vertex = line.squeeze(), vertex.squeeze()
objects, peaks = find_objects(vertex, line)
location_predictions = []
if len(objects) > 0:
for object in objects:
cuboid2d_points = object[1] + [
(object[0][0] * 8, object[0][1] * 8)
]
cuboid3d_points = np.array(cuboid3d_points)
location = None
quaternion = None
obj_2d_points = []
obj_3d_points = []
for i in range(8):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if (check_point_2d is None):
continue
elif check_point_2d[0] < 0 or check_point_2d[
1] < 0 or check_point_2d[
0] >= Config.crop_size / Config.stride or check_point_2d[
1] >= Config.crop_size / Config.stride:
continue
else:
check_point_2d = (check_point_2d[0] * Config.stride *
ratio, check_point_2d[1] *
Config.stride * ratio)
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
projected_points = object[1]
vertexes = projected_points.copy()
centroid = tuple([
int(point * Config.stride * ratio) for point in object[0]
])
obj_2d_points = np.array(obj_2d_points, dtype=np.float32)
obj_3d_points = np.array(obj_3d_points, dtype=np.float32)
valid_point_count = len(obj_2d_points)
if valid_point_count >= 4:
ret, rvec, tvec = cv2.solvePnP(
obj_3d_points,
obj_2d_points,
matrix_camera,
dist_coeffs,
flags=cv2.SOLVEPNP_ITERATIVE)
if ret:
location = list(x[0] for x in tvec)
quaternion = convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(
cuboid3d_points, rvec, tvec, matrix_camera,
dist_coeffs)
projected_points = np.squeeze(projected_points)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
if z < 0:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(
location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
vertexes = [tuple(p) for p in projected_points]
location_predictions.append(location)
location_predictions = np.array(location_predictions)
if len(location_targets) == 0:
wrong += len(location_predictions)
else:
location_targets = location_targets.cpu().data.numpy()[0]
for location_target in location_targets:
distances = [
np.sqrt(
np.sum(
np.square(location_target -
location_prediction / 10.0)))
for location_prediction in location_predictions
]
if len(distances) == 0:
pass
wrong += 1
elif min(distances) > threshold:
wrong += 1
else:
correct += 1
print('Object: {} Accuracy: {}%'.format(
class_name, correct / (wrong + correct) * 100.0))
def main(args):
output_dir = args.output_dir
if output_dir:
os.makedirs(output_dir, exist_ok=True)
path_to_data_dir = args.path_to_data_dir
if not os.path.exists(path_to_data_dir):
raise FileNotFoundError(path_to_data_dir)
path_to_checkpoint = args.checkpoint
if not os.path.exists(path_to_checkpoint):
raise FileNotFoundError(path_to_data_dir)
class_name = args.class_name
fps = args.fps
img_prefix = args.img_prefix
# load pre-trained model
model = get_model(trunk='vgg19')
model = model.cuda()
use_vgg(model, './model', 'vgg19')
print("=> Load pre-trained model from {}".format(path_to_checkpoint))
model.load_state_dict(torch.load(path_to_checkpoint))
model.eval()
# parameter of object size for pnp solver
print("=> Load {} object size".format(class_name))
path_to_object_seetings = os.path.join(path_to_data_dir,
'_object_settings.json')
if not os.path.exists(path_to_object_seetings):
raise FileNotFoundError(path_to_object_seetings)
object_list = json.load(open(path_to_object_seetings))['exported_objects']
object_size = None
for obj in object_list:
if obj['class'].find(class_name) != -1:
object_size = obj['cuboid_dimensions']
if not object_size:
raise ValueError("Object size is none")
_cuboid3d = Cuboid3d(object_size)
cuboid3d_points = np.array(_cuboid3d.get_vertices())
# parameter of camera for pnp solver
path_to_camera_seetings = os.path.join(path_to_data_dir,
'_camera_settings.json')
if not os.path.exists(path_to_camera_seetings):
raise FileNotFoundError(path_to_camera_seetings)
intrinsic_settings = json.load(open(
path_to_camera_seetings))['camera_settings'][0]['intrinsic_settings']
matrix_camera = np.zeros((3, 3))
matrix_camera[0, 0] = intrinsic_settings['fx']
matrix_camera[1, 1] = intrinsic_settings['fy']
matrix_camera[0, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[1, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[2, 2] = 1
try:
dist_coeffs = np.array(
json.load(open(path_to_camera_seetings))['camera_settings'][0]
["distortion_coefficients"])
except KeyError:
dist_coeffs = np.zeros((4, 1))
path_to_sequences = sorted(
glob.glob(os.path.join(path_to_data_dir, '*.{}'.format(img_prefix))))
for img_path in path_to_sequences:
original_img = crop(cv2.imread(img_path))
ratio = max(original_img.shape[:2]) / Config.crop_size
img = cv2.resize(original_img, (Config.crop_size, Config.crop_size))
img = preprocess(img).float()
img = torch.unsqueeze(img, 0)
out, _ = model(img.cuda())
line, vertex = out[0].squeeze(), out[1].squeeze()
objects, peaks = find_objects(vertex, line)
original_img = cv2.putText(original_img,
"Class name: {}".format(class_name),
(50, 50), cv2.FONT_HERSHEY_COMPLEX, 1,
(255, 255, 255), 2)
if len(objects) > 0:
for object in objects:
cuboid2d_points = object[1] + [
(object[0][0] * 8, object[0][1] * 8)
]
cuboid3d_points = np.array(cuboid3d_points)
location = None
quaternion = None
obj_2d_points = []
obj_3d_points = []
for i in range(8):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if check_point_2d is None:
continue
elif check_point_2d[0] < 0 or check_point_2d[
1] < 0 or check_point_2d[
0] >= Config.crop_size / Config.stride or check_point_2d[
1] >= Config.crop_size / Config.stride:
continue
else:
check_point_2d = (check_point_2d[0] * Config.stride *
ratio, check_point_2d[1] *
Config.stride * ratio)
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
centroid = tuple([
int(point * Config.stride * ratio) for point in object[0]
])
original_img = cv2.circle(original_img, centroid, 5, -1)
obj_2d_points = np.array(obj_2d_points, dtype=float)
obj_3d_points = np.array(obj_3d_points, dtype=float)
valid_point_count = len(obj_2d_points)
if valid_point_count >= 5:
ret, rvec, tvec = cv2.solvePnP(
obj_3d_points,
obj_2d_points,
matrix_camera,
dist_coeffs,
flags=cv2.SOLVEPNP_ITERATIVE)
if ret:
location = list(x[0] for x in tvec)
quaternion = convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(
cuboid3d_points, rvec, tvec, matrix_camera,
dist_coeffs)
projected_points = np.squeeze(projected_points)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
original_img = cv2.putText(
original_img,
"Location Prediction: x: {:.2f} y: {:.2f} z: {:.2f}"
.format(x / 10, y / 10, z / 10), (50, 150),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
print(
"Location Prediction: x: {:.2f} y: {:.2f} z: {:.2f}"
.format(x / 10, y / 10, z / 10))
if z < 0:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(
location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
vertexes = [tuple(p) for p in projected_points]
plot(original_img, vertexes)
if args.save:
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, img_path.split('/')[-1])
print('=> Save {}'.format(output_path))
cv2.imwrite(output_path, original_img)
if args.plot:
original_img = cv2.resize(original_img, (600, 600))
cv2.imshow('prediction', original_img)
cv2.waitKey(int(1000 / fps))
def solve_pnp(self,
cuboid2d_points,
pnp_algorithm=None,
fail_if_projected_diff_exceeds=50,
fail_if_projected_value_exceeds=1e5):
"""
Detects the rotation and traslation
of a cuboid object from its vertexes'
2D location in the image
Inputs:
- cuboid2d_points: list of XY tuples
...
Outputs:
- location in 3D
- pose in 3D (as quaternion)
- projected points: np.ndarray of np.ndarrays
"""
# Fallback to default PNP algorithm base on OpenCV version
if pnp_algorithm is None:
if CuboidPNPSolver.cv2majorversion == 2:
pnp_algorithm = cv2.CV_ITERATIVE
elif CuboidPNPSolver.cv2majorversion == 3:
pnp_algorithm = cv2.SOLVEPNP_ITERATIVE
# Alternative algorithms:
# pnp_algorithm = SOLVE_PNP_P3P
# pnp_algorithm = SOLVE_PNP_EPNP
else:
pnp_algorithm = cv2.SOLVEPNP_EPNP
location = None
quaternion = None
projected_points = cuboid2d_points
cuboid3d_points = np.array(self._cuboid3d.get_vertices())
obj_2d_points = []
obj_3d_points = []
for i in range(CuboidVertexType.TotalVertexCount):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if (check_point_2d is None):
continue
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
obj_2d_points = np.array(obj_2d_points, dtype=float)
obj_3d_points = np.array(obj_3d_points, dtype=float)
valid_point_count = len(obj_2d_points)
# Can only do PNP if we have more than 3 valid points
is_points_valid = valid_point_count >= self.min_required_points
if is_points_valid:
ret, rvec, tvec = cv2.solvePnP(obj_3d_points,
obj_2d_points,
self._camera_intrinsic_matrix,
self._dist_coeffs,
flags=pnp_algorithm)
if ret:
location = list(x[0] for x in tvec)
quaternion = self.convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(
cuboid3d_points, rvec, tvec, self._camera_intrinsic_matrix,
self._dist_coeffs)
projected_points = np.squeeze(projected_points)
success = self.__check_pnp_result(
cuboid2d_points, projected_points,
fail_if_projected_diff_exceeds,
fail_if_projected_value_exceeds)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
if z < 0 or not success:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(
location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
location = None
quaternion = None
# print("PNP solution is behind the camera (Z < 0) => flip the location and rotation")
# else:
# print("solvePNP found good results - location: {} - rotation: {} !!!".format(location, quaternion))
# else:
# print('Error: solvePnP return false ****************************************')
# else:
# print("Need at least 4 valid points in order to run PNP. Currently: {}".format(valid_point_count))
return location, quaternion, projected_points
def pitya(self, pit: float, ya: float) -> None:
"""Sets the pitch and yaw simultaneously, saves a second round of vector normalisation?"""
self._pitch = clip(pit, -pi * 0.4999, pi * 0.4999)
self._yaw = ya % (2 * pi)
self.dir = (Quaternion.from_axis_rotation(self.ure, self._yaw) *
Quaternion.from_axis_rotation(self.xre, self._pitch) * self.zre)
def yaw(self, value: float) -> None:
"""Sets the yref rotation."""
self._yaw = value % (2 * pi)
self.dir = (Quaternion.from_axis_rotation(self.ure, self._yaw) *
Quaternion.from_axis_rotation(self.xre, self._pitch) * self.zre)
def pitch(self, value: float) -> None:
"""Sets the myx rotation."""
self._pitch = clip(value, -pi * 0.4999, pi * 0.4999)
self.dir = (Quaternion.from_axis_rotation(self.ure, self._yaw) *
Quaternion.from_axis_rotation(self.xre, self._pitch) * self.zre)