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HyperTools.py
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import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
def featureNormalize(X,type):
#type==1 x = (x-mean)/std(x)
#type==2 x = (x-max(x))/(max(x)-min(x))
if type==1:
mu = np.mean(X,0)
X_norm = X-mu
sigma = np.std(X_norm,0)
X_norm = X_norm/sigma
return X_norm
elif type==2:
minX = np.min(X,0)
maxX = np.max(X,0)
X_norm = X-minX
X_norm = X_norm/(maxX-minX)
return X_norm
def DrawResult(labels,imageID):
#ID=1:Pavia University
num_class = int(labels.max())
if imageID == 1:
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:
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 == 3:
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 == 4:
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 == 5:
row = 550
col = 400
palette = np.array([[255, 0, 0],
[239, 155, 0],
[255, 255, 0],
[0, 255, 0],
[0, 255, 255],
[0, 140, 140],
[0, 0, 255],
[255, 255, 255],
[160, 32, 240]])
palette = palette * 1.0 / 255
X_result = np.zeros((labels.shape[0],3))
for i in range(1,num_class+1):
X_result[np.where(labels==i),0] = palette[i-1,0]
X_result[np.where(labels==i),1] = palette[i-1,1]
X_result[np.where(labels==i),2] = palette[i-1,2]
X_result = np.reshape(X_result,(row,col,3))
plt.axis ( "off" )
plt.imshow(X_result)
return X_result
def CalAccuracy(predict,label):
n = label.shape[0]
OA = np.sum(predict==label)*1.0/n
correct_sum = np.zeros((max(label)+1))
reali = np.zeros((max(label)+1))
predicti = np.zeros((max(label)+1))
producerA = np.zeros((max(label)+1))
for i in range(0,max(label)+1):
correct_sum[i] = np.sum(label[np.where(predict==i)]==i)
reali[i] = np.sum(label==i)
predicti[i] = np.sum(predict==i)
producerA[i] = correct_sum[i] / reali[i]
Kappa = (n*np.sum(correct_sum) - np.sum(reali * predicti)) *1.0/ (n*n - np.sum(reali * predicti))
return OA,Kappa,producerA
def LoadHSI(dataID=1,num_label=150):
#ID=1:Pavia University
if dataID==1:
data = sio.loadmat('./Data/PaviaU.mat')
X = data['paviaU']
data = sio.loadmat('./Data/PaviaU_gt.mat')
Y = data['paviaU_gt']
elif dataID==2:
data = sio.loadmat('./Data/Salinas_corrected.mat')
X = data['salinas_corrected']
data = sio.loadmat('./Data/Salinas_gt.mat')
Y = data['salinas_gt']
elif dataID==3:
data = sio.loadmat('./Data/GRSS2013.mat')
X = data['GRSS2013']
data = sio.loadmat('./Data/GRSS2013_gt.mat')
Y = data['GRSS2013_gt']
elif dataID==4:
data = sio.loadmat('./Data/Indian_pines_corrected.mat')
X = data['indian_pines_corrected']
data = sio.loadmat('./Data/Indian_pines_gt.mat')
Y = data['indian_pines_gt']
num_label = [30, 50, 50, 50, 50, 50, 20, 50, 15, 50, 50, 50, 50, 30, 50, 50]
[row,col,n_feature] = X.shape
K = row*col
X = X.reshape(K, n_feature)
n_class = Y.max()
X = featureNormalize(X,2)
X = np.reshape(X,(row,col,n_feature))
X = np.moveaxis(X,-1,0)
Y = Y.reshape(K,).astype('int')
for i in range(1,n_class+1):
index = np.where(Y==i)[0]
n_data = index.shape[0]
np.random.seed(12345)
randomArray_label = np.random.permutation(n_data)
train_num = num_label
# train_num = num_label[i-1]
if i==1:
train_array = index[randomArray_label[0:train_num]]
test_array = index[randomArray_label[train_num:n_data]]
else:
train_array = np.append(train_array,index[randomArray_label[0:train_num]])
test_array = np.append(test_array,index[randomArray_label[train_num:n_data]])
return X,Y,train_array,test_array