patch_size = (128, 128)
step_size = 32
"""
    BASE_DIR: main dataset folder
    DATASET : dataset name (using for later naming)
    DATASET_DIR : where the current dataset is stored
    FOLDER_SUFFIX : suffix to distinguish this dataset from others (optional)
    BASE_FOLDER : the base folder for the dataset
    BGR : True if we want to reverse the RGB order (Caffe/OpenCV convention)
    label_values : string names for the classes
"""
BASE_DIR = '/mnt/datagrid/personal/bartom40/'
BGR = True
label_values = ['Ground', 'Shadow', 'Cloud', 'NaN']
NUMBER_OF_CLASSES = len(label_values)

test_patch_size = (128, 128)
test_step_size = 64

CAFFE_ROOT = '../caffe/'
SOLVER_FILE = 'solver_irrg.prototxt'
MODEL_FOLDER = './'
INIT_MODEL = '../../Python/segnet_vaihingen_128x128_fold1_iter_60000.caffemodel'
CAFFE_MODE = 'gpu'
CAFFE_DEVICE = 3
IGNORE_LABEL = ''
TRAIN_DATA_SOURCE = BASE_DIR + 'rgbgendata.lmdb'
TRAIN_LABEL_SOURCE = BASE_DIR + 'rgbgendatalabel.lmdb'
TEST_DATA_SOURCE = BASE_DIR + 'rgbgenval.lmdb'
TEST_LABEL_SOURCE = BASE_DIR + 'rgbgenvallabel.lmdb'
MEAN_PIXEL = [32.97, 35.68, 40.70]
BATCH_SIZE = 25

