def save_video(project, split, label):
project = urllib.parse.unquote(project)
split = urllib.parse.unquote(split)
label = urllib.parse.unquote(label)
path = utils.lookup_project_path(project)
# Read given video to a file
input_stream = request.files['video']
output_path = os.path.join(path, f'videos_{split}', label)
temp_file_name = os.path.join(output_path, 'temp_video.webm')
with open(temp_file_name, 'wb') as temp_file:
temp_file.write(input_stream.read())
# Find a video name that is not used yet
existing_files = set(glob.glob(os.path.join(output_path, 'video_[0-9]*.mp4')))
video_idx = 0
output_file = os.path.join(output_path, f'video_{video_idx}.mp4')
while output_file in existing_files:
video_idx += 1
output_file = os.path.join(output_path, f'video_{video_idx}.mp4')
# Convert video to target frame rate and save to output name
subprocess.call(f'ffmpeg -i "{temp_file_name}" -r 30 "{output_file}"', shell=True)
# Remove temp video file
os.remove(temp_file_name)
return jsonify(success=True)
def project_details(project):
"""
Show the details for the selected project.
"""
project = urllib.parse.unquote(project)
path = utils.lookup_project_path(project)
config = utils.load_project_config(path)
stats = {}
for class_name, tags in config['classes'].items():
stats[class_name] = {}
for split in SPLITS:
videos_dir = directories.get_videos_dir(path, split, class_name)
tags_dir = directories.get_tags_dir(path, split, class_name)
stats[class_name][split] = {
'total':
len(os.listdir(videos_dir)),
'tagged':
len(os.listdir(tags_dir)) if os.path.exists(tags_dir) else 0,
}
return render_template('project_details.html',
config=config,
path=path,
stats=stats,
project=config['name'])
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 download_file(project, split, label, video_name, img_file):
"""
Load an image from the given path.
"""
dataset_path = utils.lookup_project_path(project)
img_dir = os.path.join(
directories.get_frames_dir(dataset_path, split, label), video_name)
return send_from_directory(img_dir, img_file, as_attachment=True)
def project_config():
"""
Provide the config for a given project.
"""
data = request.json
name = data['name']
path = utils.lookup_project_path(name)
# Get config
config = utils.load_project_config(path)
return jsonify(config)
def edit_class(project, class_name):
"""
Edit the class name and tags for an existing class in the given project.
"""
project = urllib.parse.unquote(project)
class_name = urllib.parse.unquote(class_name)
path = utils.lookup_project_path(project)
# Get new class name and tags
new_class_name, new_tag1, new_tag2 = utils.get_class_name_and_tags(
request.form)
# Update project config
config = utils.load_project_config(path)
del config['classes'][class_name]
config['classes'][new_class_name] = [new_tag1, new_tag2]
utils.write_project_config(path, config)
# Update directory names
data_dirs = []
for split in SPLITS:
data_dirs.extend([
directories.get_videos_dir(path, split),
directories.get_frames_dir(path, split),
directories.get_tags_dir(path, split),
])
# Feature directories follow the format <dataset_dir>/<split>/<model>/<num_layers_to_finetune>/<label>
features_dir = directories.get_features_dir(path, split)
model_dirs = [
os.path.join(features_dir, model_dir)
for model_dir in os.listdir(features_dir)
]
data_dirs.extend([
os.path.join(model_dir, tuned_layers) for model_dir in model_dirs
for tuned_layers in os.listdir(model_dir)
])
logreg_dir = directories.get_logreg_dir(path)
data_dirs.extend([
os.path.join(logreg_dir, model_dir)
for model_dir in os.listdir(logreg_dir)
])
for base_dir in data_dirs:
class_dir = os.path.join(base_dir, class_name)
if os.path.exists(class_dir):
new_class_dir = os.path.join(base_dir, new_class_name)
os.rename(class_dir, new_class_dir)
return redirect(url_for('project_details', project=project))
def record_video(project, split, label):
"""
Display the video recording screen.
"""
project = urllib.parse.unquote(project)
split = urllib.parse.unquote(split)
label = urllib.parse.unquote(label)
path = utils.lookup_project_path(project)
countdown, recording = utils.get_timer_default(path)
return render_template('video_recording.html', project=project, split=split, label=label, path=path,
countdown=countdown, recording=recording)
def annotate(project, split, label, idx):
"""
For the given class label, show all frames for annotating the selected video.
"""
project = urllib.parse.unquote(project)
path = utils.lookup_project_path(project)
label = urllib.parse.unquote(label)
split = urllib.parse.unquote(split)
frames_dir = join(path, f"frames_{split}", label)
features_dir = join(path, f"features_{split}", label)
tags_dir = join(path, f"tags_{split}", label)
logreg_dir = join(path, 'logreg', label)
videos = os.listdir(frames_dir)
videos.sort()
features = np.load(join(features_dir, f'{videos[idx]}.npy'))
features = features.mean(axis=(2, 3))
# Load logistic regression model if available
logreg_path = join(logreg_dir, 'logreg.joblib')
if os.path.isfile(logreg_path):
logreg = load(logreg_path)
classes = list(logreg.predict(features))
else:
classes = [-1] * len(features)
# The list of images in the folder
images = [image for image in glob.glob(join(frames_dir, videos[idx], '*'))
if utils.is_image_file(image)]
# Natural sort images, so that they are sorted by number
images = natsorted(images, alg=ns.IC)
# Extract image file name (without full path) and include class label
images = [(os.path.basename(image), _class) for image, _class in zip(images, classes)]
# Load existing annotations
annotations = []
annotations_file = join(tags_dir, f'{videos[idx]}.json')
if os.path.exists(annotations_file):
with open(annotations_file, 'r') as f:
data = json.load(f)
annotations = data['time_annotation']
# Read tags from config
config = utils.load_project_config(path)
tags = config['classes'][label]
return render_template('frame_annotation.html', images=images, annotations=annotations, idx=idx, fps=16,
n_images=len(images), video_name=videos[idx],
split=split, label=label, path=path, tags=tags, project=project, n_videos=len(videos))
def remove_class(project, class_name):
"""
Remove the given class from the config file of the given project. No data will be deleted.
"""
project = urllib.parse.unquote(project)
class_name = urllib.parse.unquote(class_name)
path = utils.lookup_project_path(project)
# Update project config
config = utils.load_project_config(path)
del config['classes'][class_name]
utils.write_project_config(path, config)
return redirect(url_for("project_details", project=project))
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))
def add_class(project):
"""
Add a new class to the given project.
"""
project = urllib.parse.unquote(project)
path = utils.lookup_project_path(project)
# Get class name and tags
class_name, tag1, tag2 = utils.get_class_name_and_tags(request.form)
# Update project config
config = utils.load_project_config(path)
config['classes'][class_name] = [tag1, tag2]
utils.write_project_config(path, config)
# Setup directory structure
for split in SPLITS:
videos_dir = directories.get_videos_dir(path, split, class_name)
if not os.path.exists(videos_dir):
os.mkdir(videos_dir)
return redirect(url_for("project_details", project=project))
def edit_class(project, class_name):
"""
Edit the class name and tags for an existing class in the given project.
"""
project = urllib.parse.unquote(project)
class_name = urllib.parse.unquote(class_name)
path = utils.lookup_project_path(project)
# Get new class name and tags
new_class_name, new_tag1, new_tag2 = utils.get_class_name_and_tags(request.form)
# Update project config
config = utils.load_project_config(path)
del config['classes'][class_name]
config['classes'][new_class_name] = [new_tag1, new_tag2]
utils.write_project_config(path, config)
# Update directory names
prefixes = ['videos', 'features', 'frames', 'tags']
for split in utils.SPLITS:
for prefix in prefixes:
main_dir = os.path.join(path, f'{prefix}_{split}')
class_dir = os.path.join(main_dir, class_name)
if os.path.exists(class_dir):
new_class_dir = os.path.join(main_dir, new_class_name)
os.rename(class_dir, new_class_dir)
logreg_dir = os.path.join(path, 'logreg')
class_dir = os.path.join(logreg_dir, class_name)
if os.path.exists(class_dir):
new_class_dir = os.path.join(logreg_dir, new_class_name)
os.rename(class_dir, new_class_dir)
return redirect(url_for("project_details", project=project))
def inject_class_labels(project):
path = utils.lookup_project_path(project)
class_labels = utils.get_class_labels(path)
return class_labels
def inject_temporal_status(project):
path = utils.lookup_project_path(project)
temporal_status = utils.get_project_setting(path, 'temporal')
return temporal_status
def download_file(project, split, label, video_name, img_file):
"""
Load an image from the given path.
"""
img_dir = utils.lookup_project_path(project) + f'/frames_{split}/{label}/{video_name}'
return send_from_directory(img_dir, img_file, as_attachment=True)
