def main():
from core.kitti import KITTI, ALL_OBJECTS, CARS_ONLY
DS_DIR = '/home/salam/datasets/KITTI/training'
reader = KITTI(DS_DIR, CARS_ONLY)
ids = reader.get_ids('train')
for t in [ids[4]]:
img = reader.get_image(t)
pts, ref = reader.get_velo(t, use_fov_filter=False)
boxes_2D = reader.get_boxes_2D(t)
boxes_3D = reader.get_boxes_3D(t)
P2 = reader.get_calib(t)[2]
for b in boxes_3D:
b.text = f"{np.random.uniform(0.0, 1.0, (1,))[0]:0.2f}"
imshow(bev(pts, pred_boxes=boxes_3D, title="GT"))
# Training
BATCH_SIZE = configs['hyperparams']['batch_size']
LEARNING_RATE = configs['hyperparams']['lr']
EPOCHS = configs['hyperparams']['epochs']
NUM_THREADS = configs['hyperparams']['num_threads']
MAX_Q_SIZE = configs['hyperparams']['max_q_size']
# Create dirs
OUTPUTS_DIR = os.path.join('new_outputs')
CKPTS_DIR = os.path.join(OUTPUTS_DIR, configs['ckpts_dir'])
for d in [OUTPUTS_DIR, CKPTS_DIR]:
print("Creating directory: " + d)
os.makedirs(d, exist_ok=True)
kitti = KITTI(DS_DIR, configs['training_target'])
train_ids = kitti.get_ids('train')
val_ids = kitti.get_ids('val')
micro_ids = kitti.get_ids('micro')
pc_encoder = configs['pc_encoder']
target_encoder = configs['target_encoder']
pprint.pprint(configs['stats'])
logdir = "{0}/logs/".format(CKPTS_DIR) + datetime.now().strftime(
"%m-%d-%H-%M") + configs['exp_name']
scalar_logdir = logdir + "/scalars/"
file_writer = tf.summary.create_file_writer(scalar_logdir)
# Training
BATCH_SIZE = configs['hyperparams']['batch_size']
LEARNING_RATE = configs['hyperparams']['lr']
EPOCHS = configs['hyperparams']['epochs']
NUM_THREADS = configs['hyperparams']['num_threads']
MAX_Q_SIZE = configs['hyperparams']['max_q_size']
# Create dirs
OUTPUTS_DIR = os.path.join('outputs')
CKPTS_DIR = os.path.join(OUTPUTS_DIR, configs['ckpts_dir'])
for d in [OUTPUTS_DIR, CKPTS_DIR]:
print("Creating directory: " + d)
os.makedirs(d, exist_ok=True)
kitti = KITTI(DS_DIR, configs['training_target'])
train_ids = kitti.get_ids('train')
val_ids = kitti.get_ids('val')
micro_ids = kitti.get_ids('micro')
pprint.pprint(configs['stats'])
logdir = "{0}/logs/".format(CKPTS_DIR) + datetime.now().strftime("%m-%d-%H-%M")
scalar_logdir = logdir + "/scalars/"
file_writer = tf.summary.create_file_writer(scalar_logdir)
file_writer.set_as_default()
imgs_logdir = logdir + "/images/"
img_writer = tf.summary.create_file_writer(imgs_logdir)
device_name = tf.test.gpu_device_name()
os.system('clear')
print('Conncted to Device:', device_name)
# Point Cloud Encoder
INPUT_SHAPE = configs['input_shape']
# Training
BATCH_SIZE = configs['hyperparams']['batch_size']
LEARNING_RATE = configs['hyperparams']['lr']
EPOCHS = configs['hyperparams']['epochs']
NUM_THREADS = configs['hyperparams']['num_threads']
MAX_Q_SIZE = configs['hyperparams']['max_q_size']
kitti = KITTI(DS_DIR, configs['training_target'])
train_ids = kitti.get_ids('train')
val_ids = kitti.get_ids('val')
micro_ids = kitti.get_ids('micro')
def bev2world(idx, jdx, bev_width, bev_length, world_width, world_length):
disc_factor_w, disc_factor_l = world_width / bev_width, world_length / bev_length
x = idx * disc_factor_w - world_width / 2.
z = (bev_length - jdx) * disc_factor_l
return np.array([x, 0.5, z])
def get_nearest_neighbour(pt_cloud,
bev_width,
'''
https://stackoverflow.com/questions/21037241/how-to-determine-a-point-is-inside-or-outside-a-cube
https://math.stackexchange.com/questions/1472049/check-if-a-point-is-inside-a-rectangular-shaped-area-3d
https://stackoverflow.com/questions/563198/how-do-you-detect-where-two-line-segments-intersect
https://stackoverflow.com/questions/1211212/how-to-calculate-an-angle-from-three-points
https://stackoverflow.com/questions/20677795/how-do-i-compute-the-intersection-point-of-two-lines
https://www.cs.hmc.edu/ACM/lectures/intersections.html
https://docs.yoyogames.com/source/dadiospice/002_reference/movement%20and%20collisions/collisions/rectangle_in_triangle.html
https://math.stackexchange.com/questions/3210769/how-do-i-check-if-a-3d-point-is-between-2-other-3d-points
https://math.stackexchange.com/questions/208577/find-if-three-points-in-3-dimensional-space-are-collinear
https://forum.thegamecreators.com/thread/179559#
'''
DS_DIR = '/home/salam/datasets/KITTI/training'
reader = KITTI(DS_DIR, CARS_ONLY)
ids = reader.get_ids('train')
def check_colinear(a, b, c):
ab = b - a
ac = c - a
cross = np.cross(ab, ac)
if cross.all() < 1e-12:
# print(cross)
# print(a, b, c)
return True
return False
# Training
BATCH_SIZE = configs['hyperparams']['batch_size']
LEARNING_RATE = configs['hyperparams']['lr']
EPOCHS = configs['hyperparams']['epochs']
NUM_THREADS = configs['hyperparams']['num_threads']
MAX_Q_SIZE = configs['hyperparams']['max_q_size']
# Create dirs
OUTPUTS_DIR = os.path.join('outputs')
CKPTS_DIR = os.path.join(OUTPUTS_DIR, configs['ckpts_dir'])
for d in [OUTPUTS_DIR, CKPTS_DIR]:
print("Creating directory: " + d)
os.makedirs(d, exist_ok=True)
kitti = KITTI(DS_DIR, configs['training_target'])
train_ids = kitti.get_ids('train')
val_ids = kitti.get_ids('val')
micro_ids = kitti.get_ids('micro')
pc_encoder = configs['pc_encoder']
target_encoder = configs['target_encoder']
pprint.pprint(configs['stats'])
logdir = "{0}/logs/".format(CKPTS_DIR) + datetime.now().strftime("%m-%d-%H-%M")
scalar_logdir = logdir + "/scalars/"
file_writer = tf.summary.create_file_writer(scalar_logdir)
file_writer.set_as_default()