from point_viz.converter import PointvizConverter
DATA_ROOT = "/media/data3/tjtanaa/kitti_dataset/KITTI/object/training"
# MODEL_ROOT = "./logs_Car_Pedestrian_Original_2"
MODEL_ROOT = "./logs"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if __name__ == "__main__":
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/custom_prediction_multiprocessing"
save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/prediction"
# Initialize and setup output directory.
Converter = PointvizConverter(save_viz_path)
params = Parameters()
pillar_net = build_point_pillar_graph(params)
pillar_net.load_weights(os.path.join(MODEL_ROOT, "model.h5"))
# pillar_net.summary()
data_reader = KittiDataReader()
lidar_files = sorted(glob(os.path.join(DATA_ROOT, "velodyne",
"*.bin")))[:100]
print(len(lidar_files))
print()
label_files = sorted(glob(os.path.join(DATA_ROOT, "label_2",
"*.txt")))[:100]
calibration_files = sorted(glob(os.path.join(DATA_ROOT, "calib",
def __getitem__(self, batch_id: int):
file_ids = np.arange(batch_id * self.batch_size, self.batch_size * (batch_id + 1))
# print("inside getitem")
pillars = []
voxels = []
occupancy = []
position = []
size = []
angle = []
heading = []
classification = []
pts_input = []
gt_boxes3d = []
save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/original_processor"
# Initialize and setup output directory.
Converter = PointvizConverter(save_viz_path)
for i in file_ids:
lidar = self.data_reader.read_lidar(self.lidar_files[i])
Converter.compile("transform_sample_{}".format(i), coors=lidar[:,:3], intensity=lidar[:,3])
# For each file, dividing the space into a x-y grid to create pillars
# Voxels are the pillar ids
pillars_, voxels_ = self.make_point_pillars(lidar)
# print(pillars_.shape, voxels_.shape)
pillars.append(pillars_)
voxels.append(voxels_)
if self.label_files is not None:
label = self.data_reader.read_label(self.label_files[i])
R, t = self.data_reader.read_calibration(self.calibration_files[i])
# Labels are transformed into the lidar coordinate bounding boxes
# Label has 7 values, centroid, dimensions and yaw value.
label_transformed = self.transform_labels_into_lidar_coordinates(label, R, t)
# These definitions can be found in point_pillars.cpp file
# We are splitting a 10 dim vector that contains this information.
occupancy_, position_, size_, angle_, heading_, classification_ = self.make_ground_truth(
label_transformed)
occupancy.append(occupancy_)
position.append(position_)
size.append(size_)
angle.append(angle_)
heading.append(heading_)
classification.append(classification_)
pts_input.append(lidar)
gt_boxes3d.append(label_transformed)
pillars = np.concatenate(pillars, axis=0)
voxels = np.concatenate(voxels, axis=0)
if self.label_files is not None:
occupancy = np.array(occupancy)
position = np.array(position)
size = np.array(size)
angle = np.array(angle)
heading = np.array(heading)
classification = np.array(classification)
return [pillars, voxels], [occupancy, position, size, angle, heading, classification], [pts_input, gt_boxes3d]
else:
return [pillars, voxels]