def show_video_list(split, label, path):
"""
Show the list of videos for the given split, class label and project.
If the necessary files for annotation haven't been prepared yet, this is done now.
"""
path = f'/{urllib.parse.unquote(path)}' # Make path absolute
split = urllib.parse.unquote((split))
label = urllib.parse.unquote(label)
frames_dir = join(path, f"frames_{split}", label)
tags_dir = join(path, f"tags_{split}", label)
logreg_dir = join(path, 'logreg', label)
os.makedirs(logreg_dir, exist_ok=True)
os.makedirs(tags_dir, exist_ok=True)
# load feature extractor if needed
_load_feature_extractor()
# compute the features and frames missing.
compute_frames_features(inference_engine, split, label, path)
videos = os.listdir(frames_dir)
videos.sort()
logreg_path = join(logreg_dir, 'logreg.joblib')
if os.path.isfile(logreg_path):
global logreg
logreg = load(logreg_path)
folder_id = zip(videos, list(range(len(videos))))
return render_template('video_list.html', folders=folder_id, split=split, label=label, path=path)
def prepare_annotation(project):
"""
Prepare all files needed for annotating the videos in the given project.
"""
project = urllib.parse.unquote(project)
dataset_path = utils.lookup_project_path(project)
# load feature extractor
inference_engine, model_config = utils.load_feature_extractor(dataset_path)
for split in SPLITS:
print(f'\n\tPreparing videos in the {split}-set')
for label in os.listdir(directories.get_videos_dir(
dataset_path, split)):
videos_dir = directories.get_videos_dir(dataset_path, split, label)
frames_dir = directories.get_frames_dir(dataset_path, split, label)
features_dir = directories.get_features_dir(dataset_path,
split,
model_config,
label=label)
compute_frames_features(inference_engine=inference_engine,
videos_dir=videos_dir,
frames_dir=frames_dir,
features_dir=features_dir)
return redirect(url_for("project_details", project=project))
def show_video_list(project, split, label):
"""
Show the list of videos for the given split, class label and project.
If the necessary files for annotation haven't been prepared yet, this is done now.
"""
project = urllib.parse.unquote(project)
path = utils.lookup_project_path(project)
split = urllib.parse.unquote(split)
label = urllib.parse.unquote(label)
frames_dir = join(path, f"frames_{split}", label)
tags_dir = join(path, f"tags_{split}", label)
logreg_dir = join(path, 'logreg', label)
os.makedirs(logreg_dir, exist_ok=True)
os.makedirs(tags_dir, exist_ok=True)
# load feature extractor
inference_engine = utils.load_feature_extractor(path)
# compute the features and frames missing.
compute_frames_features(inference_engine, split, label, path)
videos = os.listdir(frames_dir)
videos = natsorted(videos, alg=ns.IC)
tagged_list = set(os.listdir(tags_dir))
tagged = [f'{video}.json' in tagged_list for video in videos]
video_list = zip(videos, tagged, list(range(len(videos))))
return render_template('video_list.html', video_list=video_list, split=split, label=label, path=path,
project=project)
def prepare_annotation(path):
"""
Prepare all files needed for annotating the videos in the given project.
"""
path = f'/{urllib.parse.unquote(path)}' # Make path absolute
# load feature extractor if needed
_load_feature_extractor()
for split in ['train', 'valid']:
print("\n" + "-" * 10 + f"Preparing videos in the {split}-set" + "-" * 10)
for label in os.listdir(join(path, f'videos_{split}')):
compute_frames_features(inference_engine, split, label, path)
return redirect(url_for("project_details", path=path))
def prepare_annotation(project):
"""
Prepare all files needed for annotating the videos in the given project.
"""
project = urllib.parse.unquote(project)
path = utils.lookup_project_path(project)
# load feature extractor
inference_engine = utils.load_feature_extractor(path)
for split in utils.SPLITS:
print("\n" + "-" * 10 + f"Preparing videos in the {split}-set" + "-" * 10)
for label in os.listdir(join(path, f'videos_{split}')):
compute_frames_features(inference_engine, split, label, path)
return redirect(url_for("project_details", project=project))