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auxil.py
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import os
import numpy as np
import scipy.io as sio
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
from operator import truediv
def random_unison(a,b, rstate=None):
assert len(a) == len(b)
p = np.random.RandomState(seed=rstate).permutation(len(a))
return a[p], b[p]
def split_data(pixels, labels, percent, splitdset="custom", rand_state=69):
splitdset = "sklearn"
if splitdset == "sklearn":
return train_test_split(pixels, labels, test_size=(1-percent), stratify=labels, random_state=rand_state)
elif splitdset == "custom":
pixels_number = np.unique(labels, return_counts=1)[1]
train_set_size = [int(np.ceil(a*percent)) for a in pixels_number]
tr_size = int(sum(train_set_size))
te_size = int(sum(pixels_number)) - int(sum(train_set_size))
sizetr = np.array([tr_size]+list(pixels.shape)[1:])
sizete = np.array([te_size]+list(pixels.shape)[1:])
train_x = np.empty((sizetr)); train_y = np.empty((tr_size)); test_x = np.empty((sizete)); test_y = np.empty((te_size))
trcont = 0; tecont = 0;
for cl in np.unique(labels):
pixels_cl = pixels[labels==cl]
labels_cl = labels[labels==cl]
pixels_cl, labels_cl = random_unison(pixels_cl, labels_cl, rstate=rand_state)
for cont, (a,b) in enumerate(zip(pixels_cl, labels_cl)):
if cont < train_set_size[cl]:
train_x[trcont,:,:,:] = a
train_y[trcont] = b
trcont += 1
else:
test_x[tecont,:,:,:] = a
test_y[tecont] = b
tecont += 1
train_x, train_y = random_unison(train_x, train_y, rstate=rand_state)
test_x, test_y = random_unison(test_x, test_y, rstate=rand_state)
return train_x, test_x, train_y, test_y
def loadData(name, num_components=None):
data_path = os.path.join(os.getcwd(),'data')
if name == 'IP':
data = sio.loadmat(os.path.join(data_path, 'indian_pines_corrected.mat'))['indian_pines_corrected']
labels = sio.loadmat(os.path.join(data_path, 'indian_pines_gt.mat'))['indian_pines_gt']
elif name == 'SV':
data = sio.loadmat(os.path.join(data_path, 'salinas_corrected.mat'))['salinas_corrected']
labels = sio.loadmat(os.path.join(data_path, 'salinas_gt.mat'))['salinas_gt']
elif name == 'PU':
data = sio.loadmat(os.path.join(data_path, 'paviaU.mat'))['paviaU']
labels = sio.loadmat(os.path.join(data_path, 'paviaU_gt.mat'))['paviaU_gt']
elif name == 'KSC':
data = sio.loadmat(os.path.join(data_path, 'KSC.mat'))['KSC']
labels = sio.loadmat(os.path.join(data_path, 'KSC_gt.mat'))['KSC_gt']
else:
print("NO DATASET")
exit()
shapeor = data.shape
data = data.reshape(-1, data.shape[-1])
if num_components != None:
data = PCA(n_components=num_components).fit_transform(data)
shapeor = np.array(shapeor)
shapeor[-1] = num_components
#data = MinMaxScaler().fit_transform(data)
data = StandardScaler().fit_transform(data)
data = data.reshape(shapeor)
num_class = len(np.unique(labels)) - 1
return data, labels, num_class
def padWithZeros(X, margin=2):
newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))
x_offset = margin
y_offset = margin
newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X
return newX
def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):
margin = int((windowSize - 1) / 2)
zeroPaddedX = padWithZeros(X, margin=margin)
# split patches
patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))
patchesLabels = np.zeros((X.shape[0] * X.shape[1]))
patchIndex = 0
for r in range(margin, zeroPaddedX.shape[0] - margin):
for c in range(margin, zeroPaddedX.shape[1] - margin):
patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]
#import matplotlib.pyplot as plt
#plt.imshow(patch[:, :, 100])
#plt.show()
patchesData[patchIndex, :, :, :] = patch
patchesLabels[patchIndex] = y[r-margin, c-margin]
patchIndex = patchIndex + 1
ori_patchesLabels = patchesLabels
if removeZeroLabels:
patchesData = patchesData[patchesLabels>0,:,:,:]
patchesLabels = patchesLabels[patchesLabels>0]
patchesLabels -= 1
return ori_patchesLabels, patchesData, patchesLabels.astype("int")
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def AA_andEachClassAccuracy(confusion_matrix):
counter = confusion_matrix.shape[0]
list_diag = np.diag(confusion_matrix)
list_raw_sum = np.sum(confusion_matrix, axis=1)
each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))
average_acc = np.mean(each_acc)
return each_acc, average_acc
def reports(y_pred, y_test, name):
classification = classification_report(y_test, y_pred)
oa = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
each_acc, aa = AA_andEachClassAccuracy(confusion)
kappa = cohen_kappa_score(y_test, y_pred)
return classification, confusion, list(np.round(np.array([oa, aa, kappa] + list(each_acc)) * 100, 2))
def str2bool(s):
if s.lower() == 'true':
return True
elif s.lower() == 'false':
return False
else:
raise RuntimeError('Boolean value expected')