parser = argparse.ArgumentParser()
help_ = "Use color images"
parser.add_argument("-c", "--color", action='store_true', help=help_)
help_ = "Shapnet category or class (chair, airplane, etc)"
parser.add_argument("--category", default='chair', help=help_)
help_ = "Split file"
parser.add_argument("-s",
"--split_file",
default='data/chair_exp.json',
help=help_)
help_ = "Data png files folder"
parser.add_argument("--data", default=PRED_PATH, help=help_)
args = parser.parse_args()
split_file = args.split_file
js = get_ply(split_file)
start_time = datetime.datetime.now()
t = 0
gt = []
bl = []
pc = []
for key in js.keys():
# key eg 03001627
gt_path_main = os.path.join(GT_PATH, key)
pred_path_main = os.path.join(args.data, key)
data = js[key]
test = data['test']
test_len = len(test)
for tag in test:
# tag eg fff29a99be0df71455a52e01ade8eb6a
gt_path = os.path.join(gt_path_main, tag)
category=category,
evaluate=True)
ptcloud_ae.stop_sources()
exit(0)
if args.ptcloud_ae_weights:
print("Loading point cloud ae weights: ", args.ptcloud_ae_weights)
ptcloud_ae.use_emd = False
ptcloud_ae.ae.load_weights(args.ptcloud_ae_weights)
else:
print("Trained point cloud ae required to pc2pix")
exit(0)
pc2pix = PC2Pix(ptcloud_ae=ptcloud_ae, gw=args.generator, dw=args.discriminator, pc_code_dim=args.pc_code_dim, batch_size=batch_size, category=category)
js = get_ply(args.split_file)
datasets = ('test')
start_time = datetime.datetime.now()
os.makedirs(PLOTS_PATH, exist_ok=True)
t = 0
interpolate = False
for key in js.keys():
# key eg 03001627
data = js[key]
tags = data['test']
ply_path_main = os.path.join(args.ply, key)
tagslen = len(tags)
n_interpolate = 10
if not interpolate:
n_interpolate = 2