self.stopped = True
while not self.queue.empty():
self.queue.get()
if __name__ == '__main__':
sys.path.append('../preprocessing/')
import create_file_lst as create
data = Pt_sdf_img(
res=256,
expr=1.5,
listinfo=[["03001627", "ff3581996365bdddc3bd24f986301745"],
["03001627", "ff3581996365bdddc3bd24f986301745"]],
info=create.get_all_info(),
maxnverts=6000,
maxntris=50000,
minsurbinvox=4096,
num_points=2048,
batch_size=2,
normalize=False,
norm_color=True)
batch1 = data.get_batch(0)
print(batch1.keys())
print(batch1["verts"].shape)
print(batch1["nverts"])
print(batch1["tris"].shape)
print(batch1["ntris"])
print(batch1["surfacebinvoxpc"].shape)
print(batch1["sdf"].shape)
import h5py
import struct
BASE_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(BASE_DIR) # model
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'cam_est'))
sys.path.append(os.path.join(BASE_DIR, 'data'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
sys.path.append(os.path.join(BASE_DIR, 'preprocessing'))
import model_normalization as model
import model_cam as model_cam
from concurrent.futures import ThreadPoolExecutor
import create_file_lst
import cv2
slim = tf.contrib.slim
lst_dir, cats, all_cats, raw_dirs = create_file_lst.get_all_info()
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, default='0', help='GPU to use [default: GPU 0]')
parser.add_argument('--max_epoch', type=int, default=1, help='Epoch to run [default: 201]')
parser.add_argument('--img_h', type=int, default=137, help='Image Height')
parser.add_argument('--img_w', type=int, default=137, help='Image Width')
parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 32]')
parser.add_argument('--learning_rate', type=float, default=1e-4, help='Initial learning rate [default: 0.001]')
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
parser.add_argument('--decay_rate', type=float, default=0.9, help='Decay rate for lr decay [default: 0.7]')
parser.add_argument('--num_classes', type=int, default=1024, help='vgg dim')
parser.add_argument('--num_points', type=int, default=1, help='Point Number [default: 2048]')
parser.add_argument('--sdf_res', type=int, default=64, help='sdf grid')
parser.add_argument('--alpha', action='store_true')
parser.add_argument('--rot', action='store_true')
return self.queue.get()
def shutdown(self):
self.stopped = True
while not self.queue.empty():
self.queue.get()
if __name__ == '__main__':
sys.path.append('../preprocessing/')
import create_file_lst as create
data = Pt_sdf_img(res=256, expr=1.5,
listinfo=[["03001627", "ff3581996365bdddc3bd24f986301745"],
["03001627", "ff3581996365bdddc3bd24f986301745"]],
info=create.get_all_info(), maxnverts=6000, maxntris=50000,
minsurbinvox=4096, num_points=2048, batch_size=2, normalize=False, norm_color=True)
batch1 = data.get_batch(0)
print(batch1.keys())
print(batch1["verts"].shape)
print(batch1["nverts"])
print(batch1["tris"].shape)
print(batch1["ntris"])
print(batch1["surfacebinvoxpc"].shape)
print(batch1["sdf"].shape)
print(batch1["sdf_params"])
print(batch1["img"].shape, batch1["img"][0, 64, 64, :])
print(batch1["img_cam"])
# (2048, 3)
cloud1 = batch1["surfacebinvoxpc"][0, ...]
