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func.py
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import numpy as np
import scipy.io as scio
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
class load():
def load_data(self,flag='indian'):
if flag == 'indian':
Ind_pines_dict = scio.loadmat('./Indian_pines_corrected.mat')
Ind_pines_gt_dict = scio.loadmat('./Indian_pines_gt.mat')
print(Ind_pines_dict['indian_pines_corrected'].shape)
print(Ind_pines_gt_dict['indian_pines_gt'].shape)
original = Ind_pines_dict['indian_pines_corrected'].reshape(145 * 145, 200)
gt = Ind_pines_gt_dict['indian_pines_gt'].reshape(145 * 145, 1)
r = Ind_pines_dict['indian_pines_corrected'].shape[0]
c = Ind_pines_dict['indian_pines_corrected'].shape[1]
categories = 17
if flag == 'pavia':
pav_univ_dict = scio.loadmat('./PaviaU.mat')
pav_univ_gt_dict = scio.loadmat('./PaviaU_gt.mat')
print(pav_univ_dict['paviaU'].shape)
print(pav_univ_gt_dict['paviaU_gt'].shape)
original = pav_univ_dict['paviaU'].reshape(610 * 340, 103)
gt = pav_univ_gt_dict['paviaU_gt'].reshape(610 * 340, 1)
r = pav_univ_dict['paviaU'].shape[0]
c = pav_univ_dict['paviaU'].shape[1]
categories = 10
if flag == 'ksc':
ksc_dict = scio.loadmat('./KSC.mat')
ksc_gt_dict=scio.loadmat('./KSC_gt.mat')
print(ksc_dict['KSC'].shape)
print(ksc_gt_dict['KSC_gt'].shape)
original = ksc_dict['KSC'].reshape(512 * 614, 176)
original[original>400]=0
gt = ksc_gt_dict['KSC_gt'].reshape(512 * 614, 1)
r = ksc_dict['KSC'].shape[0]
c = ksc_dict['KSC'].shape[1]
categories = 14
if flag == 'sali':
salinas_dict = scio.loadmat('./Salinas_corrected.mat')
salinas_gt_dict = scio.loadmat('./Salinas_gt.mat')
print(salinas_dict['salinas_corrected'].shape)
print(salinas_gt_dict['salinas_gt'].shape)
original = salinas_dict['salinas_corrected'].reshape(512 * 217, 204)
gt = salinas_gt_dict['salinas_gt'].reshape(512 * 217, 1)
r = salinas_dict['salinas_corrected'].shape[0]
c = salinas_dict['salinas_corrected'].shape[1]
categories = 17
if flag == 'sali_a':
salinas_a_dict = scio.loadmat('./SalinasA_corrected.mat')
salinas_a_gt_dict = scio.loadmat('./SalinasA_gt.mat')
print(salinas_a_dict['salinasA'].shape)
print(salinas_a_gt_dict['salinasA_gt'].shape)
original = salinas_a_dict['salinasA'].reshape(83 * 86, 204)
gt = salinas_a_gt_dict['salinasA_gt'].reshape(83 * 86, 1)
r = salinas_a_dict['salinasA'].shape[0]
c = salinas_a_dict['salinasA'].shape[1]
categories = 7
if flag == 'hanchuan':
hc_dict = scio.loadmat('./WHU_Hi_HanChuan.mat')
hc_gt_dict=scio.loadmat('./WHU_Hi_HanChuan_gt.mat')
print(hc_dict['WHU_Hi_HanChuan'].shape)
print(hc_gt_dict['WHU_Hi_HanChuan_gt'].shape)
original = hc_dict['WHU_Hi_HanChuan'].reshape(1217 * 303, 274)
gt = hc_gt_dict['WHU_Hi_HanChuan_gt'].reshape(1217 * 303, 1)
r = hc_dict['WHU_Hi_HanChuan'].shape[0]
c = hc_dict['WHU_Hi_HanChuan'].shape[1]
categories = 16+1
if flag == 'houston':
houston_dict = scio.loadmat('./Houston.mat')
houston_gt_dict = scio.loadmat('./Houston_GT.mat')
print(houston_dict['Houston'].shape)
print(houston_gt_dict['Houston_GT'].shape)
original = houston_dict['Houston'].reshape(349 * 1905, 144)
gt = houston_gt_dict['Houston_GT'].reshape(349 * 1905, 1)
r = houston_dict['Houston'].shape[0]
c = houston_gt_dict['Houston_GT'].shape[1]
categories = 15+1
if flag == 'honghu':
houston_dict = scio.loadmat('./WHU_Hi_HongHu.mat')
houston_gt_dict = scio.loadmat('./WHU_Hi_HongHu_gt.mat')
print(houston_dict['WHU_Hi_HongHu'].shape)
print(houston_gt_dict['WHU_Hi_HongHu_gt'].shape)
original = houston_dict['WHU_Hi_HongHu'].reshape(940 * 475, 270)
gt = houston_gt_dict['WHU_Hi_HongHu_gt'].reshape(940 * 475, 1)
r = houston_dict['WHU_Hi_HongHu'].shape[0]
c = houston_gt_dict['WHU_Hi_HongHu_gt'].shape[1]
categories = 22+1
if flag == 'longkou':
houston_dict = scio.loadmat('./WHU_Hi_LongKou.mat')
houston_gt_dict = scio.loadmat('./WHU_Hi_LongKou_gt.mat')
print(houston_dict['WHU_Hi_LongKou'].shape)
print(houston_gt_dict['WHU_Hi_LongKou_gt'].shape)
original = houston_dict['WHU_Hi_LongKou'].reshape(550 * 400, 270)
gt = houston_gt_dict['WHU_Hi_LongKou_gt'].reshape(550 * 400, 1)
r = houston_dict['WHU_Hi_LongKou'].shape[0]
c = houston_gt_dict['WHU_Hi_LongKou_gt'].shape[1]
categories = 9+1
rows = np.arange(gt.shape[0])
All_data = np.c_[rows, original, gt]
if flag == 'hanchuan' or flag == 'honghu' or flag == 'longkou':
All_data = All_data.astype(np.int64)
# 剔除非0类别，获取所有labeled数据
labeled_data = All_data[All_data[:, -1] != 0, :]
rows_num = labeled_data[:, 0] # 所有labeled数据的ID
return All_data, labeled_data, rows_num, categories, r, c, flag
##无放回抽样
def sampling(self,All_data,categories):
K=10
M=categories-1
origin=All_data[:,1:-1]
bands=np.arange(origin.shape[1])
ensumb_num={}
ensumb_feature_set={}
for i in range(K):
ensumb_num[str(i+1)]=np.random.choice(bands,M,replace=False)
idx=[j for j in range(len(bands)) if bands[j] in ensumb_num[str(i+1)]]
bands=np.delete(bands,idx)
for i in range(K):
ensumb_feature_set[str(i+1)]=origin[:,ensumb_num[str(i+1)]]
return ensumb_feature_set,K,M
class product():
def __init__(self,c,flag):
self.c=c
self.flag=flag
def generation_num(self,labeled_data, rows_num, All_data):
train_num = []
for i in np.unique(labeled_data[:, -1]):
temp = labeled_data[labeled_data[:, -1] == i, :]
temp_num = temp[:, 0] # 某类别的所有ID
np.random.shuffle(temp_num) # 打乱顺序
if self.flag == 'indian':
if i == 1:
train_num.append(temp_num[0:20])
elif i == 7:
train_num.append(temp_num[0:10])
elif i == 9:
train_num.append(temp_num[0:5])
elif i == 15:
train_num.append(temp_num[0:40])
elif i == 16:
train_num.append(temp_num[0:40])
else:
train_num.append(temp_num[0:90])
if self.flag == 'pavia':
train_num.append(temp_num[0:int(50)])
if self.flag == 'ksc':
train_num.append(temp_num[0:int(5)])
if self.flag == 'houston':
train_num.append(temp_num[0:int(50)])
if self.flag == 'sali':
train_num.append(temp_num[0:int(30)])
if self.flag == 'hanchuan':
train_num.append(temp_num[0:int(5)])
if self.flag == 'PU_split':
train_num.append(temp_num[0:int(10)])
if self.flag == 'honghu':
train_num.append(temp_num[0:int(5)])
if self.flag == 'longkou':
train_num.append(temp_num[0:int(5)])
trn_num = [x for j in train_num for x in j]
tes_num = list(set(rows_num) - set(trn_num))
pre_num = list(set(range(0, All_data.shape[0])) - set(trn_num))
return rows_num, trn_num, tes_num, pre_num
def production_data_trn(self, rows_num, trn_num, half_s, image_3d_mat):
trn_num = np.array(trn_num)
idx_2d_trn = np.zeros([trn_num.shape[0], 2]).astype(int)
idx_2d_trn[:, 0] = np.floor(trn_num / self.c)
idx_2d_trn[:, 1] = trn_num + 1 - self.c * idx_2d_trn[:, 0] - 1
# neibour area (2*half_s+1)
patch_size=2*half_s+1
trn_spat = np.zeros([trn_num.shape[0], patch_size, patch_size, image_3d_mat.shape[2]])
trn_spe = np.zeros([trn_num.shape[0], image_3d_mat.shape[2]])
neibour_num = []
for i in range(idx_2d_trn.shape[0]):
# 图像扩展
row = idx_2d_trn[i, 0] + half_s
col = idx_2d_trn[i, 1] + half_s
trn_spat[i, :, :, :] = image_3d_mat[(row - half_s):row + half_s + 1,
(col - half_s):col + half_s + 1, :]
# 把扩展后邻域像素ID映射回原图
neibour_num = neibour_num + [(row + j - half_s) * self.c + col + k - half_s for j in range(-half_s, half_s+1) for k
in range(-half_s, half_s+1)]
trn_spe[i, :] = image_3d_mat[row, col, :]
val_num = list(set(rows_num) - set(neibour_num)) # 差集(在rows_num不在 neibour_num): 防止数据偷看
return trn_spe, trn_spat, trn_num, val_num
def production_data_valtespre(self, tes_num, half_s, image_3d_mat, flag='Tes'):
tes_num = np.array(tes_num)
idx_2d_tes = np.zeros([tes_num.shape[0], 2]).astype(int)
idx_2d_tes[:, 0] = np.floor(tes_num / self.c)
idx_2d_tes[:, 1] = tes_num + 1 - self.c * idx_2d_tes[:, 0] - 1
# neibour area(2*half_s+1)
patch_size = 2 * half_s + 1
tes_spat = np.zeros([tes_num.shape[0], patch_size, patch_size, image_3d_mat.shape[2]])
tes_spe = np.zeros([tes_num.shape[0], image_3d_mat.shape[2]])
for i in range(idx_2d_tes.shape[0]):
# 图像扩展
row = idx_2d_tes[i, 0] + half_s
col = idx_2d_tes[i, 1] + half_s
tes_spat[i, :, :, :] = image_3d_mat[(row - half_s):row + half_s + 1,
(col - half_s):col + half_s + 1, :]
tes_spe[i, :] = image_3d_mat[row, col, :]
return tes_spe, tes_spat,tes_num
def production_data_trn_SpeAll(self, rows_num, trn_num, half_s, image_3d_mat, All_data_Spe_mat_origin):
trn_num = np.array(trn_num)
##Training set(spatial)
idx_2d_trn = np.zeros([trn_num.shape[0], 2]).astype(int)
idx_2d_trn[:, 0] = np.floor(trn_num / self.c)
idx_2d_trn[:, 1] = trn_num + 1 - self.c * idx_2d_trn[:, 0] - 1
# neibour area(2*half_s+1)
patch_size = 2 * half_s + 1
trn_spat = np.zeros([trn_num.shape[0], patch_size, patch_size, image_3d_mat.shape[2]])
trn_spe = np.zeros([trn_num.shape[0], All_data_Spe_mat_origin.shape[2]])
neibour_num = []
for i in range(idx_2d_trn.shape[0]):
# 图像扩展
row = idx_2d_trn[i, 0] + half_s
col = idx_2d_trn[i, 1] + half_s
trn_spat[i, :, :, :] = image_3d_mat[(row - half_s):row + half_s + 1,
(col - half_s):col + half_s + 1, :]
# 把扩展后邻域像素ID映射回原图
neibour_num = neibour_num + [(row + j - half_s) * self.c + col + k - half_s for j in
range(-half_s, half_s + 1) for k
in range(-half_s, half_s + 1)]
trn_spe[i, :] = All_data_Spe_mat_origin[row, col, :]
val_num = list(set(rows_num) - set(neibour_num)) # 差集(在rows_num不在neibour_num): 防止数据偷看
return trn_spe, trn_spat, trn_num, val_num
def production_data_valtespre_SpeAll(self, tes_num, half_s, image_3d_mat, All_data_Spe_mat_origin, flag='Tes'):
tes_num = np.array(tes_num)
idx_2d_tes = np.zeros([tes_num.shape[0], 2]).astype(int)
idx_2d_tes[:, 0] = np.floor(tes_num / self.c)
idx_2d_tes[:, 1] = tes_num + 1 - self.c * idx_2d_tes[:, 0] - 1
# neibour area(2*half_s+1)
patch_size = 2 * half_s + 1
tes_spat = np.zeros([tes_num.shape[0], patch_size, patch_size, image_3d_mat.shape[2]])
tes_spe = np.zeros([tes_num.shape[0], All_data_Spe_mat_origin.shape[2]])
for i in range(idx_2d_tes.shape[0]):
# 图像扩展
row = idx_2d_tes[i, 0] + half_s
col = idx_2d_tes[i, 1] + half_s
tes_spat[i, :, :, :] = image_3d_mat[(row - half_s):row + half_s + 1,
(col - half_s):col + half_s + 1, :]
tes_spe[i, :] = All_data_Spe_mat_origin[row, col, :]
return tes_spe, tes_spat,tes_num
def normlization(self, data_spat, mi, ma):
scaler = MinMaxScaler(feature_range=(mi, ma))
spat_data = data_spat.reshape(-1, data_spat.shape[-1])
data_spat_new = scaler.fit_transform(spat_data).reshape(data_spat.shape)
print('Dataset normalization Finished!')
return data_spat_new
class preprocess():
def __init__(self,t):
self.transform=t
def Dim_reduction(self, All_data):
Alldata_DR=All_data
if self.transform =='pca':
pca_data_pre = All_data[:, 1:-1] # except ID (0) and gt (-1)
print(pca_data_pre.shape)
pca_transformer = PCA(n_components=1)
pca_data = pca_transformer.fit_transform(All_data[:, 1:-1])
print(pca_data.shape)
Alldata_DR = pca_data
print('PCA Finished!')
return Alldata_DR
def DrawResult(labels, imageID):
labels -= 1
num_class = labels.max() + 1
if imageID == 1: # PU
row = 610
col = 340
palette = np.array([[216, 191, 216],
[0, 255, 0],
[0, 255, 255],
[45, 138, 86],
[255, 0, 255],
[255, 165, 0],
[159, 31, 239],
[255, 0, 0],
[255, 255, 0]])
palette = palette * 1.0 / 255
elif imageID == 2: # IP
row = 145
col = 145
palette = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 255, 0],
[0, 255, 255],
[255, 0, 255],
[176, 48, 96],
[46, 139, 87],
[160, 32, 240],
[255, 127, 80],
[127, 255, 212],
[218, 112, 214],
[160, 82, 45],
[127, 255, 0],
[216, 191, 216],
[238, 0, 0]])
palette = palette * 1.0 / 255
elif imageID == 3: # Botswana
row = 1476
col = 256
palette = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 255, 0],
[0, 255, 255],
[255, 0, 255],
[176, 48, 96],
[46, 139, 87],
[160, 32, 240],
[255, 127, 80],
[127, 255, 212],
[218, 112, 214],
[160, 82, 45],
[127, 255, 0]])
palette = palette * 1.0 / 255
elif imageID == 4: # Salinas
row = 512
col = 217
palette = np.array([[37, 58, 150],
[47, 78, 161],
[56, 87, 166],
[56, 116, 186],
[51, 181, 232],
[112, 204, 216],
[119, 201, 168],
[148, 204, 120],
[188, 215, 78],
[238, 234, 63],
[246, 187, 31],
[244, 127, 33],
[239, 71, 34],
[238, 33, 35],
[180, 31, 35],
[123, 18, 20]])
palette = palette * 1.0 / 255
elif imageID == 5: # Pavia Centre
row = 1096
col = 715
palette = np.array([[37, 97, 163],
[44, 153, 60],
[122, 182, 41],
[219, 36, 22],
[227, 156, 47],
[227, 221, 223],
[108, 35, 127],
[130, 67, 142],
[229, 225, 74]])
palette = palette * 1.0 / 255
elif imageID == 6: # KSC
row = 512
col = 614
palette = np.array([[94, 203, 55],
[255, 0, 255],
[217, 115, 0],
[179, 30, 0],
[0, 52, 0],
[72, 0, 0],
[255, 255, 255],
[145, 132, 135],
[255, 255, 172],
[255, 197, 80],
[60, 201, 255],
[11, 63, 124],
[0, 0, 255]])
palette = palette * 1.0 / 255
elif imageID == 7: # Houston
row = 349
col = 1905
palette = np.array([[0, 205, 0],
[127, 255, 0],
[46, 139, 87],
[0, 139, 0],
[160, 82, 45],
[0, 255, 255],
[255, 255, 255],
[216, 191, 216],
[255, 0, 0],
[139, 0, 0],
[0, 0, 0],
[255, 255, 0],
[238, 154, 0],
[85, 26, 139],
[255, 127, 80]])
palette = palette * 1.0 / 255
elif imageID == 8: # Hanchuan
row = 1217
col = 303
palette = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 255, 0],
[0, 255, 255],
[255, 0, 255],
[176, 48, 96],
[46, 139, 87],
[160, 32, 240],
[255, 127, 80],
[127, 255, 212],
[218, 112, 214],
[160, 82, 45],
[127, 255, 0],
[216, 191, 216],
[238, 0, 0]])
palette = palette * 1.0 / 255
elif imageID == 9: # Honghu
row = 1217
col = 303
palette = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 255, 0],
[0, 255, 255],
[255, 0, 255],
[176, 48, 96],
[46, 139, 87],
[160, 32, 240],
[255, 127, 80],
[127, 255, 212],
[218, 112, 214],
[160, 82, 45],
[127, 255, 0],
[216, 191, 216],
[238, 0, 0],
[238, 154, 0],
[85, 26, 139],
[0, 139, 0],
[37, 58, 150],
[47, 78, 161],
[123, 18, 20]])
palette = palette * 1.0 / 255
elif imageID == 10: # Longkou
row = 550
col = 400
palette = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 255, 0],
[0, 255, 255],
[255, 0, 255],
[176, 48, 96],
[46, 139, 87],
[160, 32, 240]])
palette = palette * 1.0 / 255
X_result = np.zeros((labels.shape[0], 3))
for i in range(0, num_class):
X_result[np.where(labels == i), 0] = palette[i, 0]
X_result[np.where(labels == i), 1] = palette[i, 1]
X_result[np.where(labels == i), 2] = palette[i, 2]
X_result = np.reshape(X_result, (row, col, 3))
return X_result