def main():
# Parse arguments (options)
opts = parse_opts()
# Read labels
# label_file = opts.datasets_dir + '/' + opts.datasets + '/splits/pkummd.json'
label_file = opts.datasets_dir + '/' + opts.datasets + '/splits/pkummd_cross_subject_background.json'
class_file = opts.datasets_dir + '/' + opts.datasets + '/splits/classInd.txt'
labels = video_utils.VideoLabels(label_file, class_file, opts.datasets)
txt_labels = read_txt_labels(class_file)
assert _FUSION_METHOD == "exponential_smoothing", "Fusion method NotImplemented"
use_siminet = (opts.sim_method == "siminet" or opts.corr_at_transition
or opts.corr_per_window)
if use_siminet:
assert opts.siminet_path != '', "Siminet model path cannot be empty"
siminet.load_siminet_model(opts.n_classes, opts.siminet_path)
# _VIDEOS_LST = labels.labels_key
for video in _VIDEOS_LST:
ret = get_predicted_actions(opts=opts, video=video, labels=labels)
if ret is not None:
local_predicted_actions, remote_predicted_actions, fusion_predicted_actions, true_actions = ret
predictions = list(
zip(local_predicted_actions, remote_predicted_actions,
fusion_predicted_actions))
assert len(predictions) == len(true_actions)
video_file = opts.datasets_dir + "/" + opts.datasets + f"/videos/{video}.{_VIDEO_EXTENSION}"
window_size = opts.window_size
try:
visualizer = Visualizer(video_file,
window_size,
predictions,
true_actions,
txt_labels,
target_fps=_TARGET_FPS,
use_qt=_USE_QT)
visualizer.display()
except Visualizer.VideoError as e:
print(f"Cannot open video file: {e.filename}")
