def load_data():
import cv2
from zca import ZCA
zca = ZCA()
# load dataset
dataset = np.load('dataset/{}.npy'.format(dataset_name)).item()
data = dataset['data']
data_map = dataset['label']
global nch, ncls
nch = data.shape[2]
ncls = len(np.unique(data_map)) - 1
# partition the training and test data
train_coord = np.empty(
(0, 2)).astype(np.int8) # coordinates of the training data
test_coord = np.empty(
(0, 2)).astype(np.int8) # coordinates of the test data
for cls in range(ncls):
coord_class = np.transpose(np.nonzero(data_map == cls + 1))
rng.shuffle(coord_class)
# count = int(np.round(len(coord_class) * percent))
samples_per_class = count
train_coord = np.concatenate(
(train_coord, coord_class[:samples_per_class]))
test_coord = np.concatenate(
(test_coord, coord_class[samples_per_class:]))
rng.shuffle(train_coord)
rng.shuffle(test_coord)
print(train_coord.shape, test_coord.shape)
train_map = np.zeros_like(data_map)
test_map = np.zeros_like(data_map)
for i in range(train_coord.shape[0]):
train_map[train_coord[i, 0],
train_coord[i, 1]] = data_map[train_coord[i, 0],
train_coord[i, 1]]
for i in range(test_coord.shape[0]):
test_map[test_coord[i, 0],
test_coord[i, 1]] = data_map[test_coord[i, 0], test_coord[i,
1]]
# data preprocessin
data = (
(data - np.min(data[train_map != 0])) /
np.max(data[train_map != 0] - np.min(data[train_map != 0])) - 0.5) * 2
zca.fit(data[train_map != 0])
data = zca.transform(data.reshape(-1,
nch)).reshape(data.shape[0],
data.shape[1],
data.shape[2])
# padding the HSI scene and the label map
data = cv2.copyMakeBorder(data, patch_size // 2, patch_size // 2,
patch_size // 2, patch_size // 2,
cv2.BORDER_REPLICATE)
train_map = cv2.copyMakeBorder(train_map, patch_size // 2, patch_size // 2,
patch_size // 2, patch_size // 2,
cv2.BORDER_REPLICATE)
test_map = cv2.copyMakeBorder(test_map, patch_size // 2, patch_size // 2,
patch_size // 2, patch_size // 2,
cv2.BORDER_REPLICATE)
train_coord += patch_size // 2
test_coord += patch_size // 2
return data, train_map, train_coord, test_map, test_coord
