import numpy as np
if __name__ == '__main__':
# root = '/Users/cengmeng/PycharmProjects/python/Deep-subspace-clustering-networks/Data/'
root = '/content/'
# im_, gt_ = 'SalinasA_corrected', 'SalinasA_gt'
im_, gt_ = 'Indian_pines_corrected', 'Indian_pines_gt'
# im_, gt_ = 'Pavia', 'Pavia_gt'
# im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
n_row, n_column, n_band = img.shape
train_inx, test_idx = p.get_tr_tx_index(p.get_correct(img, gt)[1],
test_size=0.9)
img = minmax_scale(img.reshape(n_row * n_column, n_band)).reshape(
(n_row, n_column, n_band))
x_input = img.reshape(n_row * n_column, n_band)
num_class = 15
snmf = BandSelection_SNMF(num_class)
X_new = snmf.predict(x_input).reshape(n_row, n_column, num_class)
a, b = p.get_correct(X_new, gt)
b = p.standardize_label(b)
print(eval_band_cv(a, b, times=5))
im_, gt_ = 'Indian_pines_corrected', 'Indian_pines_gt'
# im_, gt_ = 'Pavia', 'Pavia_gt'
# im_, gt_ = 'Botswana', 'Botswana_gt'
# im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
# Img, Label = Img[:256, :, :], Label[:256, :]
n_row, n_column, n_band = img.shape
X_img = minmax_scale(img.reshape(n_row * n_column, n_band)).reshape(
(n_row, n_column, n_band))
img_correct, gt_correct = p.get_correct(X_img, gt)
gt_correct = p.standardize_label(gt_correct)
X_img_2D = X_img.reshape(n_row * n_column, n_band)
train_inx, test_idx = p.get_tr_tx_index(gt_correct, test_size=0.4)
n_input = [n_row, n_column]
kernel_size = [11]
n_hidden = [32]
batch_size = n_band
model_path = './pretrain-model-COIL20/model.ckpt'
ft_path = './pretrain-model-COIL20/model.ckpt'
logs_path = './pretrain-model-COIL20/logs'
batch_size_test = n_band
iter_ft = 0
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
#
# gt_path = 'F:\Python\HSI_Files\Indian_pines_gt.mat'
# img_path = 'F:\Python\HSI_Files\Indian_pines_corrected.mat'
#
# gt_path = 'F:\Python\HSI_Files\Pavia_gt.mat'
# img_path = 'F:\Python\HSI_Files\Pavia.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
n_row, n_clo, n_bands = img.shape
print('img=', img.shape)
# pca_img = p.pca_transform(n_comp, img.reshape(n_row * n_clo, n_bands)).reshape(n_row, n_clo, n_comp)
X, y = p.get_correct(img, gt)
print(X.shape)
'''
___________________________________________________________________
Data pre-processing
'''
# remove these samples with small numbers
classes = np.unique(y)
print('size:', X.shape, 'n_classes:', classes.shape[0])
for c in classes:
if np.nonzero(y == c)[0].shape[0] < 10:
X = np.delete(X, np.nonzero(y == c), axis=0)
y = np.delete(y, np.nonzero(y == c))
y = p.standardize_label(y)
X = MinMaxScaler().fit_transform(X)
# im_, gt_ = 'KSC', 'KSC_gt'
im_, gt_ = 'wuhanTM', 'wuhanTM_gt'
#
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
n_row, n_clo, n_bands = img.shape
print('img=', img.shape)
if n_bands > 20:
pca_img = p.pca_transform(20,
img.reshape(n_row * n_clo,
n_bands)).reshape(n_row, n_clo, 20)
X, y = p.get_correct(pca_img, gt)
else:
X, y = p.get_correct(img, gt)
print(X.shape)
'''
___________________________________________________________________
Data pre-processing
'''
# remove these samples with small numbers
classes = np.unique(y)
print('size:', X.shape, 'n_classes:', classes.shape[0])
for c in classes:
if np.nonzero(y == c)[0].shape[0] < 10:
X = np.delete(X, np.nonzero(y == c), axis=0)
# im_, gt_ = 'Pavia', 'Pavia_gt'
# im_, gt_ = 'PaviaU', 'PaviaU_gt'
# im_, gt_ = 'Salinas_corrected', 'Salinas_gt'
# im_, gt_ = 'Botswana', 'Botswana_gt'
# im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
n_row, n_column, n_band = img.shape
X_img = minmax_scale(img.reshape(n_row * n_column, n_band).transpose())
X_img = X_img.transpose().reshape((n_row, n_column, n_band))
img_correct, gt_correct = p.get_correct(X_img, gt)
gt_correct = p.standardize_label(gt_correct)
X_img_2D = X_img.reshape(n_row * n_column, n_band)
X_img_2D = minmax_scale(X_img_2D.transpose()).transpose()
n_selected_band = 5
algorithm = [
EGCSR_BS_Clustering(n_selected_band,
regu_coef=1e4,
n_neighbors=3,
ro=0.8),
EGCSR_BS_Ranking(n_selected_band, regu_coef=1e4, n_neighbors=3, ro=0.8)
]
alg_key = ['EGCSR-Clustering', 'EGCSR-Ranking']
for i in range(algorithm.__len__()):
# im_, gt_ = 'SalinasA_corrected', 'SalinasA_gt'
# im_, gt_ = 'Indian_pines_corrected', 'Indian_pines_gt'
# im_, gt_ = 'Pavia', 'Pavia_gt'
im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
n_row, n_column, n_band = img.shape
X_img = minmax_scale(img.reshape(n_row * n_column, n_band)).reshape(
(n_row, n_column, n_band))
img_correct, gt_correct = p.get_correct(X_img, gt)
train_inx, test_idx = p.get_tr_tx_index(gt_correct, test_size=0.4)
# img_train = np.transpose(img_train, axes=(2, 0, 1)) # Img.transpose()
# img_train = np.reshape(img_train, (n_band, n_row, n_column, 1))
# x_input = img.reshape(n_row * n_column, n_band)
model_path = './pretrain-model-COIL20/model.ckpt'
n_select_band = 20
spabs = SpaBS(n_select_band)
X_new = spabs.predict(X_img) # 选出每个类中的代表波段
X_new, _ = p.get_correct(X_new, gt) # 带入没有压缩的数据
score = eval_band(X_new, gt_correct, train_inx, test_idx) # 进行评价
# im_, gt_ = 'Pavia', 'Pavia_gt'
# im_, gt_ = 'Botswana', 'Botswana_gt'
# im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
# Img, Label = Img[:256, :, :], Label[:256, :]
n_row, n_column, n_band = img.shape
X_img = minmax_scale(img.reshape(n_row * n_column, n_band)).reshape(
(n_row, n_column, n_band))
X_img_2D = X_img.reshape(n_row * n_column, n_band)
img_correct, gt_correct = p.get_correct(X_img, gt)
gt_correct = p.standardize_label(gt_correct)
train_inx, test_idx = p.get_tr_tx_index(gt_correct, test_size=0.4)
n_input = [n_row, n_column]
kernel_size = [7]
n_hidden = [32]
batch_size = n_band
model_path = './pretrain-model-COIL20/model.ckpt'
ft_path = './pretrain-model-COIL20/model.ckpt'
logs_path = './pretrain-model-COIL20/logs'
batch_size_test = n_band
iter_ft = 0
display_step = 1
