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data_hand.py
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import random
import numpy as np
from Fei_dataset import *
from six.moves import xrange
from scipy.misc import imsave as ims
from HSICSupport import *
from ops import *
from Utlis2 import *
import gzip
import cv2
import keras as keras
#from copy import deepcopy
#from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.keras import datasets
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
def load_mnist(dataset_name):
data_dir = os.path.join("./data", dataset_name)
def extract_data(filename, num_data, head_size, data_size):
with gzip.open(filename) as bytestream:
bytestream.read(head_size)
buf = bytestream.read(data_size * num_data)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float)
return data
data = extract_data(data_dir + '/train-images-idx3-ubyte.gz', 60000, 16, 28 * 28)
trX = data.reshape((60000, 28, 28, 1))
data = extract_data(data_dir + '/train-labels-idx1-ubyte.gz', 60000, 8, 1)
trY = data.reshape((60000))
data = extract_data(data_dir + '/t10k-images-idx3-ubyte.gz', 10000, 16, 28 * 28)
teX = data.reshape((10000, 28, 28, 1))
data = extract_data(data_dir + '/t10k-labels-idx1-ubyte.gz', 10000, 8, 1)
teY = data.reshape((10000))
trY = np.asarray(trY)
teY = np.asarray(teY)
X = np.concatenate((trX, teX), axis=0)
y = np.concatenate((trY, teY), axis=0).astype(np.int)
seed = 547
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(y)
y_vec = np.zeros((len(y), 10), dtype=np.float)
for i, label in enumerate(y):
y_vec[i, y[i]] = 1.0
return X / 255., y_vec
def GiveMNIST_SVHN():
mnistName = "mnist"
data_X, data_y = load_mnist(mnistName)
#data_X = np.expand_dims(data_X, axis=3)
data_X = np.concatenate((data_X, data_X, data_X), axis=-1)
size = (int(32), int(32))
myArr = []
for i in range(np.shape(data_X)[0]):
image = cv2.resize(data_X[i],size, interpolation=cv2.INTER_AREA)
myArr.append(image)
data_X = np.array(myArr)
x_train = data_X[0:60000]
x_test = data_X[60000:70000]
y_train = data_y[0:60000]
y_test = data_y[60000:70000]
mnist_train_x = x_train
mnist_train_label = y_train
mnist_test = x_test
mnist_label_test = y_test
myTest = mnist_train_x[0:64]
ims("results/" + "gggg" + str(0) + ".jpg", merge2(myTest[:64], [8, 8]))
x_train, y_train, x_test, y_test = GetSVHN_DataSet()
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)
return mnist_train_x,mnist_train_label,mnist_test,mnist_label_test,x_train,y_train,x_test,y_test
def GiveFashion_32():
mnistName = "Fashion"
data_X, data_y = load_mnist(mnistName)
#data_X = np.expand_dims(data_X, axis=3)
data_X = np.concatenate((data_X, data_X, data_X), axis=-1)
size = (int(32), int(32))
myArr = []
for i in range(np.shape(data_X)[0]):
image = cv2.resize(data_X[i],size, interpolation=cv2.INTER_AREA)
myArr.append(image)
data_X = np.array(myArr)
x_train = data_X[0:60000]
x_test = data_X[60000:70000]
y_train = data_y[0:60000]
y_test = data_y[60000:70000]
fashion_train_x = x_train
fashion_train_label = y_train
fashion_test = x_test
fashion_label_test = y_test
return fashion_train_x,fashion_train_label,fashion_test,fashion_label_test
def GiveFashion32():
mnistName = "Fashion"
data_X, data_y = load_mnist(mnistName)
# data_X = np.expand_dims(data_X, axis=3)
data_X = np.concatenate((data_X, data_X, data_X), axis=-1)
size = (int(32), int(32))
myArr = []
for i in range(np.shape(data_X)[0]):
image = cv2.resize(data_X[i], size, interpolation=cv2.INTER_AREA)
myArr.append(image)
data_X = np.array(myArr)
x_train = data_X[0:60000]
x_test = data_X[60000:70000]
y_train = data_y[0:60000]
y_test = data_y[60000:70000]
mnist_train_x = x_train
mnist_train_label = y_train
mnist_test = x_test
mnist_label_test = y_test
return mnist_train_x,mnist_train_label,mnist_test,mnist_label_test
def ReturnSet_ByIndex(x,y,startIndex,endIndex):
xarr = []
yarr = []
difference = endIndex - 10
for i in range(np.shape(x)[0]):
if y[i] >= startIndex and y[i] <= endIndex:
xarr.append(x[i])
label = y[i] - difference
label = label-1
yarr.append(label)
xarr = np.array(xarr)
yarr = np.array(yarr)
return xarr,yarr
def Split_CIFAR100_ReturnTesting():
(x_train, y_train), (x_test, y_test) = datasets.cifar100.load_data()
x_train = x_train/255
x_test = x_test/ 255
from keras.utils.np_utils import to_categorical
x1_,y1_ = ReturnSet_ByIndex(x_test,y_test,1,10)
x2_,y2_ = ReturnSet_ByIndex(x_test,y_test,11,20)
x3_,y3_ = ReturnSet_ByIndex(x_test,y_test,21,30)
x4_,y4_ = ReturnSet_ByIndex(x_test,y_test,31,40)
x5_,y5_ = ReturnSet_ByIndex(x_test,y_test,41,50)
y1_ = to_categorical(y1_, num_classes=None)
y2_ = to_categorical(y2_, num_classes=None)
y3_ = to_categorical(y3_, num_classes=None)
y4_ = to_categorical(y4_, num_classes=None)
y5_ = to_categorical(y5_, num_classes=None)
return x1_,y1_,x2_,y2_,x3_,y3_,x4_,y4_,x5_,y5_
def Split_CIFAR100():
(x_train, y_train), (x_test, y_test) = datasets.cifar100.load_data()
x_train = x_train/255
x_test = x_test/ 255
x1,y1 = ReturnSet_ByIndex(x_train,y_train,1,10)
x2,y2 = ReturnSet_ByIndex(x_train,y_train,11,20)
x3,y3 = ReturnSet_ByIndex(x_train,y_train,21,30)
x4,y4 = ReturnSet_ByIndex(x_train,y_train,31,40)
x5,y5 = ReturnSet_ByIndex(x_train,y_train,41,50)
x1_,y1_ = ReturnSet_ByIndex(x_test,y_test,1,10)
x2_,y2_ = ReturnSet_ByIndex(x_test,y_test,11,20)
x3_,y3_ = ReturnSet_ByIndex(x_test,y_test,21,30)
x4_,y4_ = ReturnSet_ByIndex(x_test,y_test,31,40)
x5_,y5_ = ReturnSet_ByIndex(x_test,y_test,41,50)
x_ = np.concatenate((x1_,x2_,x3_,x4_,x5_),axis=0)
y_ = np.concatenate((y1_,y2_,y3_,y4_,y5_),axis=0)
return x1,y1,x2,y2,x3,y3,x4,y4,x5,y5,x_,y_
Split_CIFAR100()
def Split_dataset(x,y,n_label):
y = np.argmax(y,axis=1)
n_each = n_label / 10
isRun = True
x_train = []
y_train = []
index = np.zeros(10)
while(isRun):
a = random.randint(0, np.shape(x)[0])-1
x1 = x[a]
y1 = y[a]
if index[y1] < n_each:
x_train.append(x1)
y_train.append(y1)
index[y1] = index[y1]+1
isOk1 = True
for i in range(10):
if index[i] < n_each:
isOk1 = False
if isOk1:
break
x_train = np.array(x_train)
y_train = np.array(y_train)
return x_train,y_train
def Give_InverseFashion32():
mnistName = "Fashion"
data_X, data_y = load_mnist(mnistName)
data_X = np.reshape(data_X, (-1, 28, 28))
for i in range(np.shape(data_X)[0]):
for k1 in range(28):
for k2 in range(28):
data_X[i, k1, k2] = 1.0 - data_X[i, k1, k2]
data_X = np.reshape(data_X, (-1, 28, 28, 1))
data_X = np.concatenate((data_X, data_X, data_X), axis=-1)
size = (int(32), int(32))
myArr = []
for i in range(np.shape(data_X)[0]):
image = cv2.resize(data_X[i], size, interpolation=cv2.INTER_AREA)
myArr.append(image)
data_X = np.array(myArr)
x_train = data_X[0:60000]
x_test = data_X[60000:70000]
y_train = data_y[0:60000]
y_test = data_y[60000:70000]
mnist_train_x = x_train
mnist_train_label = y_train
mnist_test = x_test
mnist_label_test = y_test
return mnist_train_x,mnist_train_label,mnist_test,mnist_label_test
def GiveMNIST32():
mnistName = "mnist"
data_X, data_y = load_mnist(mnistName)
# data_X = np.expand_dims(data_X, axis=3)
data_X = np.concatenate((data_X, data_X, data_X), axis=-1)
size = (int(32), int(32))
myArr = []
for i in range(np.shape(data_X)[0]):
image = cv2.resize(data_X[i], size, interpolation=cv2.INTER_AREA)
myArr.append(image)
data_X = np.array(myArr)
x_train = data_X[0:60000]
x_test = data_X[60000:70000]
y_train = data_y[0:60000]
y_test = data_y[60000:70000]
mnist_train_x = x_train
mnist_train_label = y_train
mnist_test = x_test
mnist_label_test = y_test
return mnist_train_x, mnist_train_label, mnist_test, mnist_label_test