def run(command, args):
if command == 'add':
add.tasks(args.descriptions, args.project)
if command == 'list':
listit.list(args)
if command == 'find':
find.find(args)
if command == 'update':
update.update(args)
if command == 'delete':
delete.delete(args)
if command == 'sync':
sync.sync(args)
if command == 'mail':
mail.send(args)
if command == 'print':
print.print(args)
def test_delete(build_potato_dataset, tree_type, hash_size, distance_metric, nearest_neighbors, leaf_size,
parallel, batch_size, threshold, backup_keep, backup_duplicate, safe_deletion, expected):
output_path = mkdir_output(os.path.join(str(PROJECT_DIR), "outputs"))
df_dataset, img_file_list = build_potato_dataset
to_keep, to_remove = delete(df_dataset, img_file_list, output_path, hash_size, tree_type, distance_metric,
nearest_neighbors, leaf_size, parallel, batch_size, threshold, backup_keep,
backup_duplicate, safe_deletion)
assert len(to_keep) == expected[0]
assert to_keep[0].split(os.sep)[-1] == expected[1]
assert to_keep[1].split(os.sep)[-1] == expected[2]
assert len(to_remove) == expected[3]
# delete_output(output_path)
print()
def main(args):
from _version import get_versions
__version__ = get_versions()['version']
dt = str(datetime.datetime.today().strftime('%Y-%m-%d-%H-%M'))
output_path = os.path.join(args.output_path, dt)
FileSystem.mkdir_if_not_exist(output_path)
if args.command == "delete":
# Config
images_path = args.images_path
hash_algo = args.hash_algorithm
hash_size = args.hash_size
tree_type = args.tree_type
distance_metric = args.distance_metric
nearest_neighbors = args.nearest_neighbors
leaf_size = args.leaf_size
parallel = args.parallel
batch_size = args.batch_size
threshold = args.threshold
backup_keep = args.backup_keep
backup_duplicate = args.backup_duplicate
safe_deletion = args.safe_deletion
image_w = args.image_w
image_h = args.image_h
df_dataset, img_file_list = ImageToHash(images_path, hash_size=hash_size, hash_algo=hash_algo) \
.build_dataset(parallel=parallel, batch_size=batch_size)
delete(df_dataset, img_file_list, output_path, hash_size, tree_type,
distance_metric, nearest_neighbors, leaf_size, parallel,
batch_size, threshold, backup_keep, backup_duplicate,
safe_deletion, image_w, image_h)
if args.command == "show":
# Config
images_path = args.images_path
hash_algo = args.hash_algorithm
hash_size = args.hash_size
parallel = args.parallel
batch_size = args.batch_size
df_dataset, _ = ImageToHash(images_path, hash_size=hash_size, hash_algo=hash_algo) \
.build_dataset(parallel=parallel, batch_size=batch_size)
show(df_dataset, output_path)
if args.command == "search":
# Config
images_path = args.images_path
hash_algo = args.hash_algorithm
hash_size = args.hash_size
tree_type = args.tree_type
distance_metric = args.distance_metric
nearest_neighbors = args.nearest_neighbors
leaf_size = args.leaf_size
parallel = args.parallel
batch_size = args.batch_size
threshold = args.threshold
image_w = args.image_w
image_h = args.image_h
query = args.query
df_dataset, _ = ImageToHash(images_path, hash_size=hash_size, hash_algo=hash_algo) \
.build_dataset(parallel=parallel, batch_size=batch_size)
search(df_dataset, output_path, tree_type, distance_metric,
nearest_neighbors, leaf_size, parallel, batch_size, threshold,
image_w, image_h, query)