def draw_uv_map_side(side, model_root, method='s', bins=256):
""" Draw UV Map onto the RGB image of the Side class.
Args:
side: a Side class
model_root: str, will be used to retrieve original 3d point cloud model
using "/<model_root>/011_banana/google_512k/textured.obj"
method: 's' or 'c'
bins: int, default 256
Returns: (h, w, 3) ndarray
"""
_img = side.img
for i, class_name in enumerate(side.class_list):
model_path = f"{model_root}/{class_name}/google_512k/textured.obj"
pts = datalib.load_obj(model_path)
segid = fat_name2segid[class_name]
store_dict = dict(depth_sorted_ind=None)
pts_ind = side.transform_point_cloud(
pts, ind=i, store_dict=store_dict).astype(int)
pts_sorted = pts[store_dict['depth_sorted_ind']]
_img = draw_uv_map_image(pts_sorted, pts_ind, _img,
mask_config=(side.seg, segid),
method=method, bins=bins)
return _img
def generator(n_pc):
idx = 0
while (idx < n_pc):
# for idx in range(len(pc_names)):
if idx == len(pc_names):
idx = 0
pc_filename = pc_names[idx]
pc_filepath = join(root_dir, pc_filename)
points = load_obj(pc_filepath)
idx += 1
yield points
def visualize_object(self,
ind,
model_root,
show_pivots=True,
show_cuboids=True,
show_projected_pcd=True,
color_array=(255, 0, 255)):
"""
Args:
ind: index into self.objects
model_root: /<model_root>/<model_name>/google_512k/textured.obj'
example <model_name> like '037_scissors'
show_pivots: bool, show transformed pivots axis
show_cuboids: bool, show projected cuboids
show_projected_pcd: bool, draw colored projected point cloud
onto the object
color_array: tuple, default pink
Returns: annotated ndarray (h, w, 3)
"""
pivots = self.canonical_pivots[ind]
cuboid = self.canonical_cuboids[ind]
# Get model point cloud
# The '006_mustart_bottle' is UPPER CASE 16K, others' are 16k?
model_name = self.objects[0]['class'].replace('_16k', '').replace('_16K', '')
model_pcd = datalib.load_obj(
osp.join(model_root, model_name, 'google_512k/textured.obj'))
# Visualize
_img = self.img.copy()
if show_projected_pcd:
store_dict = dict(depth_sorted_ind=None)
pcd_2d = self.transform_point_cloud(model_pcd, ind, store_dict=store_dict)
pcd_ind = np.floor(pcd_2d).astype(int)
y_ind = np.clip(pcd_ind[:, 1], 0, self.cap_height-1)
x_ind = np.clip(pcd_ind[:, 0], 0, self.cap_width-1)
_img[y_ind, x_ind, :] = color_array
if show_cuboids:
cuboid_2d = self.transform_point_cloud(cuboid, ind)
_img = visualize.draw_proj_cuboid_image(_img, cuboid_2d)
if show_pivots:
pivots_2d = self.transform_point_cloud(pivots, ind)
_img = visualize.draw_pivots_image(_img, pivots_2d)
return _img
return False
# Set directory paths
dir_path = os.path.dirname(os.path.realpath(__file__))
data_path = os.path.join(dir_path, 'data')
car_path = os.path.join(data_path, 'autonomous-car')
# Reference location on Earth (Hoover Tower)
lat0 = 37.4276
lon0 = -122.1670
h0 = 0
p_ref_ECEF = utils.lla2ecef(np.array([lat0, lon0, h0]))
# Load data
sensors = data_utils.load_obj(car_path + '/sim/sensor_data')
data_gnss = sensors["gnss"]
data_compass = sensors["compass"]
car_params = vehicle_sim.get_parameters()
traj = data_utils.load_obj(car_path + '/sim/traj_data')
################################################################
# Least Squares
################################################################
LS = ls.runLeastSquares(data_gnss["t"],
data_gnss["sat_pos"],
data_gnss["pr"],
p_ref_ECEF=p_ref_ECEF)
data_utils.save_obj(LS, car_path + '/filtering/leastsquares')
################################################################
def __getitem__(self, idx):
pc_filename = self.pc_names[idx]
pc_filepath = join(self.root_dir, pc_filename)
sample = load_obj(pc_filepath)
return sample, 0