def split_randomly(db_file,
dataset,
config,
args,
test_clips=[],
balance_bins=True):
# split data randomly such that a clip is only in one dataset
# have tried many ways to split i.e. location and cameras found this is simplest
# and the results are the same
train = Dataset(db_file, "train", config)
train.enable_augmentation = True
validation = Dataset(db_file, "validation", config)
test = Dataset(db_file, "test", config)
test_c = get_test_set_camera(dataset, test_clips, args.date)
test_cameras = [test_c]
validate_cameras = []
train_cameras = []
for label in dataset.labels:
existing_test_count = len(test.tracks_by_label.get(label, []))
train_c, validate_c, test_c = split_label(
dataset, label, existing_test_count=existing_test_count)
if train_c is not None:
train_cameras.append(train_c)
if validate_c is not None:
validate_cameras.append(validate_c)
if test_c is not None:
test_cameras.append(test_c)
add_camera_tracks(dataset.labels, train, train_cameras, balance_bins)
add_camera_tracks(dataset.labels, validation, validate_cameras,
balance_bins)
add_camera_tracks(dataset.labels, test, test_cameras, balance_bins)
return train, validation, test
def split_dataset_by_cameras(db, dataset, config, args):
validation_percent = 0.3
train = Dataset(db, "train", config)
train.enable_augmentation = True
validation = Dataset(db, "validation", config)
train_cameras = []
cameras = list(dataset.cameras_by_id.values())
camera_count = len(cameras)
num_validate_cameras = max(
MIN_VALIDATE_CAMERAS, round(camera_count * validation_percent)
)
wallaby, wallaby_validate = split_wallaby_cameras(dataset, cameras)
if wallaby:
train_cameras.append(wallaby)
# has all the rabbits so put in training
rabbits = dataset.cameras_by_id.get("ruru19w44a-[-36.03915 174.51675]")
if rabbits:
cameras.remove(rabbits)
train_cameras.append(rabbits)
validate_cameras, cameras = diverse_validation(
cameras, dataset.labels, num_validate_cameras
)
if wallaby_validate:
validate_cameras.append(wallaby_validate)
train_cameras.extend(cameras)
add_camera_tracks(dataset.labels, train, train_cameras)
add_camera_tracks(dataset.labels, validation, validate_cameras)
return train, validation