return val - np.floor(val / period + offset) * period
if __name__ == "__main__":
params = Parameters()
pillar_net = build_point_pillar_graph(params)
pillar_net.load_weights(os.path.join(MODEL_ROOT, "model.h5"))
pillar_net.summary()
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/custom_prediction_multiprocessing"
save_viz_path = os.path.join(
"/home/tan/tjtanaa/PointPillars/visualization",
MODEL_ROOT.split('/')[-1])
# Initialize and setup output directory.
Converter = PointvizConverter(save_viz_path)
gt_database_dir = os.path.join(DATA_ROOT, "gt_database")
# validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size,root_dir = DATA_ROOT,
# npoints=20000, split='train', classes=list(params.classes_map.keys()),
# random_select=True, gt_database_dir=None, aug_hard_ratio=0.7)
validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size,
root_dir=DATA_ROOT,
npoints=20000,
split='train_val_test',
random_select=False,
classes=list(
params.classes_map.keys()))
# validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size, root_dir=DATA_ROOT,
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",
from point_viz.converter import PointvizConverter
if __name__ == "__main__":
# Path to the kitti dataset
dataset_path = '/media/data3/tjtanaa/kitti_dataset'
database_path = os.path.join(dataset_path, "gt_database")
# gt_database_dir = os.path.join(database_path, "train_gt_database_level_Car.pkl")
aug_dataset = PCKittiSingleStagePointwiseDataset(root_dir=dataset_path, split='train',
npoints =16384,
classes =['Car'], random_select =True,
gt_database_dir=database_path, aug_hard_ratio=0.7)
print("============ Grab Aug Sample =====================")
save_viz_path = "/home/tan/tjtanaa/det3d/demos/pc_kitti_single_stage_pointwise_dataset"
# Initialize and setup output directory.
Converter = PointvizConverter(save_viz_path)
for i in range(20):
sample_info = aug_dataset.get_rpn_sample(i)
# sample_info['pts_input'] = pts_input
# sample_info['pts_rect'] = aug_pts_rect
# sample_info['pts_features'] = ret_pts_features
# sample_info['rpn_cls_label'] = rpn_cls_label
# sample_info['rpn_reg_label'] = rpn_reg_label
# sample_info['gt_boxes3d'] = aug_gt_boxes3d
# Pass data and create html files.
sample_info['pts_rect'][:,1] *= -1 # mirror the y axis
coors = sample_info['pts_rect']
bbox_params = np.stack([sample_info['gt_boxes3d'][:,5], sample_info['gt_boxes3d'][:,3], sample_info['gt_boxes3d'][:,4],
sample_info['gt_boxes3d'][:,0], -(sample_info['gt_boxes3d'][:,1] - sample_info['gt_boxes3d'][:,3] / 2) ,
sample_info['gt_boxes3d'][:,2],
sample_info['gt_boxes3d'][:,6]], axis=1)
# parser = argparse.ArgumentParser(description='Analayse the kitti dataset dimensions:\
# mode: 0 := generate the numpy file ')
# parser.add_argument('integers', metavar='N', type=int, nargs='+',
# help='an integer for the accumulator')
# parser.add_argument('--sum', dest='accumulate', action='store_const',
# const=sum, default=max,
# help='sum the integers (default: find the max)')
# args = parser.parse_args()
if __name__ == "__main__":
print("Current Directory: ", currentdir)
print("Grand Grand Parent Directory: ", grandgrandparentdir)
save_viz_path = os.path.join(currentdir, 'visualization/modelnet40')
Converter = PointvizConverter(save_viz_path)
# modelnet_40_dataset = ModelNet40(npoint=10000,
# phase='test',
# batch_size=16,
# normal=False,
# augmentation=False,
# gauss_drop=False,
# rotate_setting=[0., 0., np.pi],
# scale_setting=[0.1, 0.1, 0.1],
# normalization='0~1',
# abs=True,
# home="/media/data3/tjtanaa/ModelNet40_Tony/ModelNet40_10k")
# aug_config = {"rotate_mode": "g",
# "rotate_range": np.pi,
# "scale_mode": "g",
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
def limit_period(val, offset=0.5, period=np.pi):
return val - np.floor(val / period + offset) * period
if __name__ == "__main__":
params = Parameters()
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/custom_prediction_multiprocessing"
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/input_coordinate_analysis_point_pillar_v2_gt_only"
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/input_coordinate_analysis_point_pillar_v2_labels_only"
save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/pedestrian_input_coordinate_analysis_point_pillar_v2_gt_and_labels"
# Initialize and setup output directory.
Converter = PointvizConverter(save_viz_path)
gt_database_dir = os.path.join(DATA_ROOT, "gt_database")
validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size, root_dir=DATA_ROOT,
npoints=16384, split='train_val_test',random_select=False, classes=list(params.classes_map.keys()))
for sample_id in validation_gen.sample_id_list:
print(sample_id)
# for batch_idx in range(0,20):
# [pillars, voxels], [occupancy_, position_, size_, angle_, heading_, classification_], [pts_input, gt_boxes3d, sample] = validation_gen[batch_idx]
# set_boxes, confidences = [], []
# loop_range = occupancy_.shape[0] if len(occupancy_.shape) == 4 else 1
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]
if __name__ == "__main__":
print("Current Directory: ", currentdir)
# pc_template_path
pc_template_path = os.path.join(currentdir, args.pc_template_path)
pc_bin_list = [
filename for filename in os.listdir(pc_template_path)
if '.bin' in filename
]
print("List of raw point clouds: ", pc_bin_list)
if args.step == 0:
save_viz_path = os.path.join(
currentdir, 'visualization/modelnet40_offset_sample/')
Converter = PointvizConverter(save_viz_path)
for idx, angle in enumerate(orientations):
angle = -angle # to return it to zero
R = np.array([[np.cos(angle), -np.sin(angle), 0],
[np.sin(angle), np.cos(angle), 0],
[0, 0, 1]]) # rotation around z axis
print(angle)
coors = np.load(os.path.join(
pc_template_path, pc_bin_list[idx]))[:, :3] # ignore the rgb
print(coors.shape)
coors = transform(coors[:, [2, 0, 1]], R)
bbox_params = [[0.0001, 0.0001, 0.0001, 10, 10, 10, 0]]
# pts_coors = coors[i]
# # pts_coors[:,1] *= -1