def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--tolabel",
help="Preprocess images to create labels (out/tolabel)",
action="store_true",
default=False)
parser.add_argument("--augmentation",
help="Dataset augmentation (pass quantity)",
type=int)
parser.add_argument("--dataset",
help="Dataset name",
type=str,
default=constant.DATASET)
parser.add_argument("--train",
help="Train",
action="store_true",
default=False)
parser.add_argument("--test",
help="Predict",
action="store_true",
default=False)
parser.add_argument("--arch",
help="Neural Network architecture",
type=str,
default=constant.MODEL)
parser.add_argument("--dip",
help="Method for image processing",
type=str,
default=constant.IMG_PROCESSING)
parser.add_argument("--gpu",
help="Enable GPU mode",
action="store_true",
default=False)
args = parser.parse_args()
environment.setup(args)
exist = lambda x: len(x) > 0 and path.exist(path.data(x, mkdir=False))
if (args.tolabel):
generator.tolabel()
elif args.dataset is not None and exist(args.dataset):
if (args.augmentation):
generator.augmentation(args.augmentation)
elif (args.train):
nn.train()
elif (args.test):
nn.test()
else:
print("\n>> Dataset not found\n")
def main():
dataset = "crackconcrete"
train = True
test = False
environment.setup()
exist = lambda x: len(x) > 0 and path.exist(path.data(x, mkdir=False))
if dataset is not None and exist(dataset):
if train:
nn.train()
elif test:
nn.test()
else:
print("\n>> Dataset not found\n")
def augmentation(n=1):
batch_size = 1
target_size = const.IMAGE_SIZE[:2]
seed = int(np.random.rand(1)*100)
train_path = path.data(const.DATASET, const.dn_TRAIN)
image_folder = image_save_prefix = const.dn_IMAGE
label_folder = label_save_prefix = const.dn_LABEL
image_to_dir = path.dn_aug(const.dn_IMAGE)
label_to_dir = path.dn_aug(const.dn_LABEL)
image_gen = label_gen = ImageDataGenerator(
rotation_range=0.2,
fill_mode="constant",
rescale = 1./255,
width_shift_range=0.05,
height_shift_range=0.05,
channel_shift_range=0.05,
shear_range=0.05,
zoom_range=0.05,
vertical_flip=True,
horizontal_flip=True)
image_batch = image_gen.flow_from_directory(
directory = train_path,
classes = [image_folder],
target_size = target_size,
batch_size = batch_size,
save_to_dir = image_to_dir,
save_prefix = image_save_prefix,
seed = seed)
label_batch = label_gen.flow_from_directory(
directory = train_path,
classes = [label_folder],
target_size = target_size,
batch_size = batch_size,
save_to_dir = label_to_dir,
save_prefix = label_save_prefix,
seed = seed)
for i, (_,_) in enumerate(zip(image_batch, label_batch)):
if (i >= n-1): break
def fetchFromPath(origin, suborigin=""):
p = os.path.join(path.data(), origin)
dt_dir = os.path.join(p, suborigin)
data = listFolder(dt_dir, origin)
data[0] = fetchFromArray(data[0])
return data