def main():
import argparse
import cv2
parser = argparse.ArgumentParser()
parser.add_argument("dataset_loader", type=str)
parser.add_argument("dataset_path", type=str)
parser.add_argument("--video_name", type=str)
parser.add_argument("--random_seed",
type=int,
help="Optional random seed for deterministic results")
add_resizing_arguments(parser)
args = parser.parse_args()
random.seed(args.random_seed)
np.random.seed(args.random_seed)
synth_visualizer_gen = SyntheticSimpleVisualizer(**vars(args)).generate()
while True:
synth_image, _ = next(synth_visualizer_gen)
cv2.imshow("synth_image", synth_image)
cv2.waitKey()
def main():
import argparse
parser = argparse.ArgumentParser()
# inputs
parser.add_argument("dataset_loader", type=str)
parser.add_argument("dataset_path", type=str)
add_resizing_arguments(parser)
parser.add_argument(
"--video_names",
type=str,
nargs="+",
help="Only evaluate using a subset of videos. " "If not set, will include all videos in dataset_path.",
)
parser.add_argument(
"--num_samples",
type=int,
help="How many frames to perform the calibration in order to evaluate the image metric",
)
parser.add_argument("--theta_dims", type=int)
parser.add_argument("--work_dir", type=str, help="Root folder for all experiments")
parser.add_argument(
"--save_images", default=False, action="store_true", help="Also save the images used for the calibration"
)
parser.add_argument("--random_seed", type=int, help="Optional random seed for deterministic results")
args = parser.parse_args()
list(calibrate(**vars(args)))
def main():
import argparse
import cv2
parser = argparse.ArgumentParser()
parser.add_argument("dataset_loader", type=str)
parser.add_argument("dataset_path", type=str)
parser.add_argument(
"--video_name",
type=str,
help="Optional video name. If not set, only visualize one video.",
)
add_resizing_arguments(parser)
args = parser.parse_args()
real_visualizer = RealSimpleVisualizer(**vars(args))
for orig, processed in real_visualizer.generate():
cv2.imshow("orig", orig)
cv2.imshow("processed", processed)
cv2.waitKey()
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("dataset_loader", type=str)
parser.add_argument("dataset_path", type=str)
parser.add_argument(
"--video_names",
type=str,
nargs="+",
help="Only generate training data for a subset of videos. "
"If not set, will include all videos in dataset_path.",
)
parser.add_argument("--num_train_samples",
type=int,
help="How many training samples to generate")
parser.add_argument("--num_eval_samples",
type=int,
help="How many evaluation samples to generate")
parser.add_argument("--temp_dir",
type=str,
help="Where to store temporary intermediate results")
parser.add_argument("--work_dir",
type=str,
help="Root folder for all experiments")
parser.add_argument("--num_process",
type=int,
help="How many worker processes")
parser.add_argument("--random_seed",
type=int,
help="Optional random seed for deterministic results")
add_resizing_arguments(parser)
args = parser.parse_args()
last_progress = None
for progress in generate(**vars(args)):
prog_percent = int(progress * 100)
if prog_percent != last_progress:
logger.info(f"Generating training data: {prog_percent}% done")
last_progress = prog_percent