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run.py
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"""This file implement an inference pipeline for Point-GNN on KITTI dataset"""
import os
import time
import argparse
import multiprocessing
from functools import partial
import numpy as np
import tensorflow as tf
import open3d
import cv2
from tqdm import tqdm
from dataset.kitti_dataset import KittiDataset, Points
from dataset.painted_kitti_dataset import PaintedKittiDataset, PaintedPoints
from models.graph_gen import get_graph_generate_fn
from models.models import get_model
from models.box_encoding import get_box_decoding_fn, get_box_encoding_fn, \
get_encoding_len
from models import preprocess
from models import nms
from util.config_util import load_config, load_train_config
from util.summary_util import write_summary_scale
parser = argparse.ArgumentParser(description='Point-GNN inference on KITTI')
parser.add_argument('checkpoint_path', type=str,
help='Path to checkpoint')
parser.add_argument('-l', '--level', type=int, default=0,
help='Visualization level, 0 to disable,'+
'1 to nonblocking visualization, 2 to block.'+
'Default=0')
parser.add_argument('--test', dest='test', action='store_true',
default=False, help='Enable test model')
parser.add_argument('--no-box-merge', dest='use_box_merge',
action='store_false', default='True',
help='Disable box merge.')
parser.add_argument('--no-box-score', dest='use_box_score',
action='store_false', default='True',
help='Disable box score.')
parser.add_argument('--dataset_root_dir', type=str, default='../dataset/kitti/',
help='Path to KITTI dataset. Default="../dataset/kitti/"')
parser.add_argument('--dataset_split_file', type=str,
default='',
help='Path to KITTI dataset split file.'
'Default="DATASET_ROOT_DIR/3DOP_splits/val.txt"')
parser.add_argument('--output_dir', type=str,
default='',
help='Path to save the detection results'
'Default="CHECKPOINT_PATH/eval/"')
args = parser.parse_args()
VISUALIZATION_LEVEL = args.level
IS_TEST = args.test
USE_BOX_MERGE = args.use_box_merge
USE_BOX_SCORE = args.use_box_score
DATASET_DIR = args.dataset_root_dir
if args.dataset_split_file == '':
DATASET_SPLIT_FILE = os.path.join(DATASET_DIR, './3DOP_splits/val.txt')
else:
DATASET_SPLIT_FILE = args.dataset_split_file
if args.output_dir == '':
OUTPUT_DIR = os.path.join(args.checkpoint_path, './eval/')
else:
OUTPUT_DIR = args.output_dir
CHECKPOINT_PATH = args.checkpoint_path
CONFIG_PATH = os.path.join(CHECKPOINT_PATH, 'config')
assert os.path.isfile(CONFIG_PATH), 'No config file found in %s'
config = load_config(CONFIG_PATH)
# setup dataset ===============================================================
if IS_TEST:
raise ValueError('running test not supported, fix code')
dataset = KittiDataset(
os.path.join(DATASET_DIR, 'image/testing/image_2'),
os.path.join(DATASET_DIR, 'velodyne/testing/velodyne/'),
os.path.join(DATASET_DIR, 'calib/testing/calib/'),
'',
num_classes=config['num_classes'],
is_training=False)
else:
# dataset = KittiDataset(
# os.path.join(DATASET_DIR, 'image/training/image_2'),
# os.path.join(DATASET_DIR, 'velodyne/training/velodyne/'),
# os.path.join(DATASET_DIR, 'calib/training/calib/'),
# os.path.join(DATASET_DIR, 'labels/training/label_2'),
# DATASET_SPLIT_FILE,
# num_classes=config['num_classes'])
dataset = PaintedKittiDataset(
os.path.join(DATASET_DIR, 'image/training/image_2'),
os.path.join(DATASET_DIR, 'velodyne/training/painted_lidar/'),
os.path.join(DATASET_DIR, 'calib/training/calib/'),
os.path.join(DATASET_DIR, 'labels/training/label_2'),
DATASET_SPLIT_FILE,
num_classes=config['num_classes'])
NUM_TEST_SAMPLE = dataset.num_files
NUM_CLASSES = dataset.num_classes
# occlusion score =============================================================
def occlusion(label, xyz):
if xyz.shape[0] == 0:
return 0
normals, lower, upper = dataset.box3d_to_normals(label)
projected = np.matmul(xyz, np.transpose(normals))
x_cover_rate = (np.max(projected[:, 0])-np.min(projected[:, 0]))\
/(upper[0] - lower[0])
y_cover_rate = (np.max(projected[:, 1])-np.min(projected[:, 1]))\
/(upper[1] - lower[1])
z_cover_rate = (np.max(projected[:, 2])-np.min(projected[:, 2]))\
/(upper[2] - lower[2])
return x_cover_rate*y_cover_rate*z_cover_rate
# setup model =================================================================
BOX_ENCODING_LEN = get_encoding_len(config['box_encoding_method'])
box_encoding_fn = get_box_encoding_fn(config['box_encoding_method'])
box_decoding_fn = get_box_decoding_fn(config['box_encoding_method'])
if config['input_features'] == 'irgb':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 4])
elif config['input_features'] == 'rgb':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 3])
elif config['input_features'] == '0000':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 4])
elif config['input_features'] == 'i000':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 4])
elif config['input_features'] == 'i':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 1])
elif config['input_features'] == '0':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 1])
elif config['input_features'] == 'is':
t_initial_vertex_features = tf.placeholder(
dtype=tf.float32, shape=[None, 5])
t_vertex_coord_list = [tf.placeholder(dtype=tf.float32, shape=[None, 3])]
for _ in range(len(config['runtime_graph_gen_kwargs']['level_configs'])):
t_vertex_coord_list.append(
tf.placeholder(dtype=tf.float32, shape=[None, 3]))
t_edges_list = []
for _ in range(len(config['runtime_graph_gen_kwargs']['level_configs'])):
t_edges_list.append(
tf.placeholder(dtype=tf.int32, shape=[None, 2]))
t_keypoint_indices_list = []
for _ in range(len(config['runtime_graph_gen_kwargs']['level_configs'])):
t_keypoint_indices_list.append(
tf.placeholder(dtype=tf.int32, shape=[None, 1]))
t_is_training = tf.placeholder(dtype=tf.bool, shape=[])
model = get_model(config['model_name'])(num_classes=NUM_CLASSES,
box_encoding_len=BOX_ENCODING_LEN, mode='test', **config['model_kwargs'])
t_logits, t_pred_box = model.predict(
t_initial_vertex_features, t_vertex_coord_list, t_keypoint_indices_list,
t_edges_list,
t_is_training)
t_probs = model.postprocess(t_logits)
t_predictions = tf.argmax(t_probs, axis=1, output_type=tf.int32)
# optimizers ==================================================================
global_step = tf.Variable(0, dtype=tf.int32, trainable=False)
fetches = {
'step': global_step,
'predictions': t_predictions,
'probs': t_probs,
'pred_box': t_pred_box
}
# setup Visualizer ============================================================
if VISUALIZATION_LEVEL == 1:
print("Configure the viewpoint as you want and press [q]")
calib = dataset.get_calib(0)
cam_points_in_img_with_rgb = dataset.get_cam_points_in_image_with_rgb(0,
calib=calib)
vis = open3d.Visualizer()
vis.create_window()
pcd = open3d.PointCloud()
pcd.points = open3d.Vector3dVector(cam_points_in_img_with_rgb.xyz)
pcd.colors = open3d.Vector3dVector(cam_points_in_img_with_rgb.attr[:,1:4])
line_set = open3d.LineSet()
graph_line_set = open3d.LineSet()
box_corners = np.array([[0, 0, 0]])
box_edges = np.array([[0,0]])
line_set.points = open3d.Vector3dVector(box_corners)
line_set.lines = open3d.Vector2iVector(box_edges)
graph_line_set.points = open3d.Vector3dVector(box_corners)
graph_line_set.lines = open3d.Vector2iVector(box_edges)
vis.add_geometry(pcd)
vis.add_geometry(line_set)
vis.add_geometry(graph_line_set)
ctr = vis.get_view_control()
ctr.rotate(0.0, 3141.0, 0)
vis.run()
color_map = np.array([(211,211,211), (255, 0, 0), (255,20,147), (65, 244, 101),
(169, 244, 65), (65, 79, 244), (65, 181, 244), (229, 244, 66)],
dtype=np.float32)
color_map = color_map/255.0
gt_color_map = {
'Pedestrian': (0,255,255),
'Person_sitting': (218,112,214),
'Car': (154,205,50),
'Truck':(255,215,0),
'Van': (255,20,147),
'Tram': (250,128,114),
'Misc': (128,0,128),
'Cyclist': (255,165,0),
}
# runing network ==============================================================
time_dict = {}
saver = tf.train.Saver()
graph = tf.get_default_graph()
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(graph=graph,
config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
sess.run(tf.variables_initializer(tf.global_variables()))
sess.run(tf.variables_initializer(tf.local_variables()))
model_path = tf.train.latest_checkpoint(CHECKPOINT_PATH)
print('Restore from checkpoint %s' % model_path)
saver.restore(sess, model_path)
previous_step = sess.run(global_step)
for frame_idx in tqdm(range(0, NUM_TEST_SAMPLE)):
start_time = time.time()
if VISUALIZATION_LEVEL == 2:
pcd = open3d.PointCloud()
line_set = open3d.LineSet()
graph_line_set = open3d.LineSet()
# provide input ======================================================
cam_rgb_points = dataset.get_cam_points_in_image_with_rgb_and_scores(frame_idx,
config['downsample_by_voxel_size'])
calib = dataset.get_calib(frame_idx)
image = dataset.get_image(frame_idx)
if not IS_TEST:
box_label_list = dataset.get_label(frame_idx)
input_time = time.time()
time_dict['fetch input'] = time_dict.get('fetch input', 0) \
+ input_time - start_time
graph_generate_fn= get_graph_generate_fn(config['graph_gen_method'])
(vertex_coord_list, keypoint_indices_list, edges_list) = \
graph_generate_fn(
cam_rgb_points.xyz, **config['runtime_graph_gen_kwargs'])
graph_time = time.time()
time_dict['gen graph'] = time_dict.get('gen graph', 0) \
+ graph_time - input_time
if config['input_features'] == 'irgb':
input_v = cam_rgb_points.attr
elif config['input_features'] == '0rgb':
input_v = np.hstack([np.zeros((cam_rgb_points.attr.shape[0], 1)),
cam_rgb_points.attr[:, 1:]])
elif config['input_features'] == '0000':
input_v = np.zeros_like(cam_rgb_points.attr)
elif config['input_features'] == 'i000':
input_v = np.hstack([cam_rgb_points.attr[:, [0]], np.zeros(
(cam_rgb_points.attr.shape[0], 3))])
elif config['input_features'] == 'i':
input_v = cam_rgb_points.attr[:, [0]]
elif config['input_features'] == '0':
input_v = np.zeros((cam_rgb_points.attr.shape[0], 1))
elif config['input_features'] == 'is':
input_v = np.hstack([cam_rgb_points.attr[:, [0]], cam_rgb_points.scores])
last_layer_graph_level = \
config['model_kwargs']['layer_configs'][-1]['graph_level']
last_layer_points_xyz = vertex_coord_list[last_layer_graph_level+1]
if config['label_method'] == 'yaw':
label_map = {'Background': 0, 'Car': 1, 'Pedestrian': 3,
'Cyclist': 5,'DontCare': 7}
if config['label_method'] == 'Car':
label_map = {'Background': 0, 'Car': 1, 'DontCare': 3}
if config['label_method'] == 'Pedestrian_and_Cyclist':
label_map = {'Background': 0, 'Pedestrian': 1, 'Cyclist':3,
'DontCare': 5}
# run forwarding =====================================================
feed_dict = {
t_initial_vertex_features: input_v,
t_is_training: True,
}
feed_dict.update(dict(zip(t_edges_list, edges_list)))
feed_dict.update(
dict(zip(t_keypoint_indices_list, keypoint_indices_list)))
feed_dict.update(dict(zip(t_vertex_coord_list, vertex_coord_list)))
results = sess.run(fetches, feed_dict=feed_dict)
gnn_time = time.time()
time_dict['gnn inference'] = time_dict.get('gnn inference', 0) \
+ gnn_time - graph_time
# box decoding =======================================================
box_probs = results['probs']
box_labels = np.tile(np.expand_dims(np.arange(NUM_CLASSES), axis=0),
(box_probs.shape[0], 1))
box_labels = box_labels.reshape((-1))
raw_box_labels = box_labels
box_probs = box_probs.reshape((-1))
pred_boxes = results['pred_box'].reshape((-1, 1, BOX_ENCODING_LEN))
last_layer_points_xyz = np.tile(
np.expand_dims(last_layer_points_xyz, axis=1), (1, NUM_CLASSES, 1))
last_layer_points_xyz = last_layer_points_xyz.reshape((-1, 3))
boxes_centers = last_layer_points_xyz
decoded_boxes = box_decoding_fn(np.expand_dims(box_labels, axis=1),
boxes_centers, pred_boxes, label_map)
box_mask = (box_labels > 0)*(box_labels < NUM_CLASSES-1)
box_mask = box_mask*(box_probs > 1./NUM_CLASSES)
box_indices = np.nonzero(box_mask)[0]
decode_time = time.time()
time_dict['decode box'] = time_dict.get('decode box', 0) \
+ decode_time - gnn_time
if box_indices.size != 0:
box_labels = box_labels[box_indices]
box_probs = box_probs[box_indices]
box_probs_ori = box_probs
decoded_boxes = decoded_boxes[box_indices, 0]
box_labels[box_labels==2]=1
box_labels[box_labels==4]=3
box_labels[box_labels==6]=5
detection_scores = box_probs
# nms ============================================================
if USE_BOX_MERGE and USE_BOX_SCORE:
(class_labels, detection_boxes_3d, detection_scores,
nms_indices) = nms.nms_boxes_3d_uncertainty(
box_labels, decoded_boxes, detection_scores,
overlapped_fn=nms.overlapped_boxes_3d_fast_poly,
overlapped_thres=config['nms_overlapped_thres'],
appr_factor=100.0, top_k=-1,
attributes=np.arange(len(box_indices)))
if USE_BOX_MERGE and not USE_BOX_SCORE:
(class_labels, detection_boxes_3d, detection_scores,
nms_indices) = nms.nms_boxes_3d_merge_only(
box_labels, decoded_boxes, detection_scores,
overlapped_fn=nms.overlapped_boxes_3d_fast_poly,
overlapped_thres=config['nms_overlapped_thres'],
appr_factor=100.0, top_k=-1,
attributes=np.arange(len(box_indices)))
if not USE_BOX_MERGE and USE_BOX_SCORE:
(class_labels, detection_boxes_3d, detection_scores,
nms_indices) = nms.nms_boxes_3d_score_only(
box_labels, decoded_boxes, detection_scores,
overlapped_fn=nms.overlapped_boxes_3d_fast_poly,
overlapped_thres=config['nms_overlapped_thres'],
appr_factor=100.0, top_k=-1,
attributes=np.arange(len(box_indices)))
if not USE_BOX_MERGE and not USE_BOX_SCORE:
(class_labels, detection_boxes_3d, detection_scores,
nms_indices) = nms.nms_boxes_3d(
box_labels, decoded_boxes, detection_scores,
overlapped_fn=nms.overlapped_boxes_3d_fast_poly,
overlapped_thres=config['nms_overlapped_thres'],
appr_factor=100.0, top_k=-1,
attributes=np.arange(len(box_indices)))
box_probs = detection_scores
# visualization ===================================================
if VISUALIZATION_LEVEL > 0:
last_layer_points_color = np.zeros(
(last_layer_points_xyz.shape[0], 3), dtype=np.float32)
last_layer_points_color[:, :] = color_map[raw_box_labels, :]
cam_points_color = cam_rgb_points.attr[:, 1:]
pcd.points = open3d.Vector3dVector(np.vstack(
[last_layer_points_xyz[box_indices][nms_indices],
last_layer_points_xyz, cam_rgb_points.xyz]))
pcd.colors = open3d.Vector3dVector(np.vstack(
[last_layer_points_color[box_indices][nms_indices],
np.tile([(1,0.,200./255)],
(last_layer_points_color.shape[0], 1)),
cam_points_color]))
output_points_idx = box_indices[nms_indices]//NUM_CLASSES
edge_mask = np.isin(
edges_list[last_layer_graph_level][:, 1],
output_points_idx)
last_layer_edges = np.hstack(
[edges_list[last_layer_graph_level][:, [0]][edge_mask],
keypoint_indices_list[-1][edges_list[
last_layer_graph_level][:, 1][edge_mask]]])
colors = last_layer_points_color[edges_list[
last_layer_graph_level][:, 1][edge_mask]]
for i in range(len(keypoint_indices_list)-2, -1, -1):
last_layer_edges = \
keypoint_indices_list[i][last_layer_edges, 0]
last_layer_edges += len(box_indices[nms_indices])
last_layer_edges += last_layer_points_xyz.shape[0]
lines = last_layer_edges
graph_line_set.points = pcd.points
graph_line_set.lines = open3d.Vector2iVector(lines)
graph_line_set.colors = open3d.Vector3dVector(colors)
# convert to KITTI ================================================
detection_boxes_3d_corners = nms.boxes_3d_to_corners(
detection_boxes_3d)
pred_labels = []
for i in range(len(detection_boxes_3d_corners)):
detection_box_3d_corners = detection_boxes_3d_corners[i]
corners_cam_points = Points(
xyz=detection_box_3d_corners, attr=None)
corners_img_points = dataset.cam_points_to_image(
corners_cam_points, calib)
corners_xy = corners_img_points.xyz[:, :2]
if config['label_method'] == 'yaw':
all_class_name = ['Background', 'Car', 'Car', 'Pedestrian',
'Pedestrian', 'Cyclist', 'Cyclist', 'DontCare']
if config['label_method'] == 'Car':
all_class_name = ['Background', 'Car', 'Car', 'DontCare']
if config['label_method'] == 'Pedestrian_and_Cyclist':
all_class_name = ['Background', 'Pedestrian', 'Pedestrian',
'Cyclist', 'Cyclist', 'DontCare']
if config['label_method'] == 'alpha':
all_class_name = ['Background', 'Car', 'Car', 'Pedestrian',
'Pedestrian', 'Cyclist', 'Cyclist', 'DontCare']
class_name = all_class_name[class_labels[i]]
xmin, ymin = np.amin(corners_xy, axis=0)
xmax, ymax = np.amax(corners_xy, axis=0)
clip_xmin = max(xmin, 0.0)
clip_ymin = max(ymin, 0.0)
clip_xmax = min(xmax, 1242.0)
clip_ymax = min(ymax, 375.0)
height = clip_ymax - clip_ymin
truncation_rate = 1.0 - (clip_ymax - clip_ymin)*(
clip_xmax - clip_xmin)/((ymax - ymin)*(xmax - xmin))
if truncation_rate > 0.4:
continue
x3d, y3d, z3d, l, h, w, yaw = detection_boxes_3d[i]
assert l > 0, str(i)
score = box_probs[i]
if USE_BOX_SCORE:
tmp_label = {"x3d": x3d, "y3d" : y3d, "z3d": z3d,
"yaw": yaw, "height": h, "width": w, "length": l}
# Rescore or not ===========================================
inside_mask = dataset.sel_xyz_in_box3d(tmp_label,
last_layer_points_xyz[box_indices])
points_inside = last_layer_points_xyz[
box_indices][inside_mask]
score_inside = box_probs_ori[inside_mask]
score = (1+occlusion(tmp_label, points_inside))*score
pred_labels.append((class_name, -1, -1, 0,
clip_xmin, clip_ymin, clip_xmax, clip_ymax,
h, w, l, x3d, y3d, z3d, yaw, score))
if VISUALIZATION_LEVEL > 0:
cv2.rectangle(image,
(int(clip_xmin), int(clip_ymin)),
(int(clip_xmax), int(clip_ymax)), (0, 255, 0), 2)
if class_name == "Pedestrian":
cv2.putText(image, '{:s} | {:.3f}'.format('P', score),
(int(clip_xmin), int(clip_ymin)-int(clip_xmin/10)),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0) ,1)
else:
cv2.putText(image, '{:s} | {:.3f}'.format('C', score),
(int(clip_xmin), int(clip_ymin)),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0) ,1)
nms_time = time.time()
time_dict['nms'] = time_dict.get('nms', 0) + nms_time - decode_time
# output ===========================================================
filename = OUTPUT_DIR+'/data/'+dataset.get_filename(
frame_idx)+'.txt'
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w") as f:
for pred_label in pred_labels:
for field in pred_label:
f.write(str(field)+' ')
f.write('\n')
f.write('\n')
if VISUALIZATION_LEVEL > 0:
if not IS_TEST:
gt_boxes = []
gt_colors = []
for label in box_label_list:
if label['name'] in gt_color_map:
gt_boxes.append([
label['x3d'], label['y3d'], label['z3d'],
label['length'], label['height'],
label['width'], label['yaw']])
gt_colors.append(gt_color_map[label['name']])
gt_boxes = np.array(gt_boxes)
gt_colors = np.array(gt_colors)/255.
gt_box_corners, gt_box_edges, gt_box_colors = \
dataset.boxes_3d_to_line_set(gt_boxes,
boxes_color=gt_colors)
if gt_box_corners is None or gt_box_corners.size<1:
gt_box_corners = np.array([[0, 0, 0]])
gt_box_edges = np.array([[0, 0]])
gt_box_colors = np.array([[0, 0, 0]])
box_corners, box_edges, box_colors = \
dataset.boxes_3d_to_line_set(detection_boxes_3d)
else:
if VISUALIZATION_LEVEL > 0:
last_layer_points_color = np.zeros(
(last_layer_points_xyz.shape[0], 3), dtype=np.float32)
last_layer_points_color[:, :] = color_map[raw_box_labels, :]
cam_points_color = cam_rgb_points.attr[:, 1:]
box_corners = np.array([[0, 0, 0]])
box_edges = np.array([[0, 0]])
box_colors = np.array([[0, 0, 0]])
pcd.points = open3d.Vector3dVector(np.vstack([
last_layer_points_xyz, cam_rgb_points.xyz]))
pcd.colors = open3d.Vector3dVector(np.vstack([np.tile(
[(128./255,0.,128./255)],
(last_layer_points_color.shape[0], 1)), cam_points_color]))
graph_line_set.points = open3d.Vector3dVector(
np.array([[0, 0, 0]]))
graph_line_set.lines = open3d.Vector2iVector(
[[0, 0]])
graph_line_set.colors = open3d.Vector3dVector(
np.array([[0, 0, 0]]))
if not IS_TEST:
gt_boxes = []
gt_colors = []
no_gt = True
for label in box_label_list:
if label['name'] in gt_color_map:
gt_boxes.append([label['x3d'], label['y3d'],
label['z3d'], label['length'], label['height'],
label['width'], label['yaw']])
gt_colors.append(gt_color_map[label['name']])
no_gt = False
gt_boxes = np.array(gt_boxes)
gt_colors = np.array(gt_colors)/255.
gt_box_corners, gt_box_edges, gt_box_colors = \
dataset.boxes_3d_to_line_set(gt_boxes,
boxes_color=gt_colors)
if gt_box_corners is None or gt_box_corners.size<1:
gt_box_corners = np.array([[0, 0, 0]])
gt_box_edges = np.array([[0, 0]])
gt_box_colors = np.array([[0, 0, 0]])
filename = OUTPUT_DIR+'/data/'+dataset.get_filename(
frame_idx)+'.txt'
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w") as f:
f.write('\n')
if VISUALIZATION_LEVEL > 0:
cv2.imshow('image', image)
cv2.waitKey(10)
if not IS_TEST:
box_edges += gt_box_corners.shape[0]
line_set.points = open3d.Vector3dVector(np.vstack(
[gt_box_corners, box_corners]))
line_set.lines = open3d.Vector2iVector(np.vstack(
[gt_box_edges, box_edges]))
line_set.colors = open3d.Vector3dVector(np.vstack(
[gt_box_colors, box_colors]))
else:
line_set.points = open3d.Vector3dVector(np.vstack(
[box_corners]))
line_set.lines = open3d.Vector2iVector(np.vstack(
[box_edges]))
line_set.colors = open3d.Vector3dVector(np.vstack(
[box_colors]))
if VISUALIZATION_LEVEL == 1:
vis.update_geometry()
vis.poll_events()
vis.update_renderer()
if VISUALIZATION_LEVEL == 2:
print("Configure the viewpoint as you want and press [q]")
def custom_draw_geometry_load_option(geometry_list):
vis = open3d.Visualizer()
vis.create_window()
for geometry in geometry_list:
vis.add_geometry(geometry)
ctr = vis.get_view_control()
ctr.rotate(0.0, 3141.0, 0)
vis.run()
vis.destroy_window()
custom_draw_geometry_load_option([pcd, line_set, graph_line_set])
total_time = time.time()
time_dict['total'] = time_dict.get('total', 0) \
+ total_time - start_time
for key in time_dict:
print(key + " time : " + str(time_dict[key]/NUM_TEST_SAMPLE))