'''
import cv2
import numpyas np
import PIL
from PILimport Image
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import keras# 导入Keras
from keras.datasets import mnist# 从keras中导入mnist数据集
from keras.models import Sequential# 导入序贯模型
from keras.layers import Dense# 导入全连接层
from keras.optimizers import SGD
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dropout, Dense
from keras.losses import categorical_crossentropy
from keras.optimizers import Adadelta
from keras.datasetsimport cifar10
(x_img_train,y_label_train),(x_img_test,y_label_test)=cifar10.load_data()
print('train:',len(x_img_train))
print('test :',len(x_img_test))
print('train_image :',x_img_train.shape)
print('train_label :',y_label_train.shape)
print('test_image :',x_img_test.shape)
print('test_label :',y_label_test.shape)
#data=x_img_train[0]
def Laplace_number(img):
outcome=0
for k1in range(1,31):
for k2in range(1,31):
outcome=outcome+img[k1-1,k2]+img[k1,k2-1]+img[k1+1,k2]+img[k1,k2+1]-4*img[k1,k2]
return outcome
x_img=x_img_train[0]
x_img_grey=cv2.cvtColor(x_img, cv2.COLOR_BGR2GRAY)
x_img_matrix=np.asmatrix(x_img_grey)
#x_img_matrix.shape
x_number=Laplace_number(x_img_matrix)
x_img_laplace = cv2.Laplacian(x_img, -1,ksize=3)
x_img_laplace_grey = cv2.cvtColor(x_img_laplace, cv2.COLOR_BGR2GRAY)
x_img_laplace_grey.shape
x_img_laplace_matrix=np.asmatrix(x_img_laplace_grey)
x_laplace_number=Laplace_number(x_img_laplace_matrix)
print(x_number)
print(x_laplace_number)
n_row=2
n_col=2
i=1
plt.subplot(n_row, n_col, i)
plt.imshow(x_img)
plt.show()
i=i+1
plt.subplot(n_row, n_col, i)
plt.imshow(x_img_grey,cmap='Greys_r')
plt.show()
i=i+1
plt.subplot(n_row, n_col, i)
plt.imshow(x_img_laplace_matrix,cmap='Greys_r')
plt.show()
plt.subplot(2,2,1)
x_train_0_org=x_img_train[0,:,:,0:3]
plt.imshow(x_train_0_org)
plt.title('org')
plt.subplot(2,2,2)
x_train_0_org_bilateral=cv2.bilateralFilter(x_train_0_org,9,75,75)
x_train_0_org_bilateral_canny = cv2.Canny(x_train_0_org_bilateral,75,200)
plt.imshow(x_train_0_org_bilateral_canny)
plt.title('org_bilateral_canny')
plt.subplot(2,2,3)
x_train_0_org_laplace=cv2.Laplacian(x_train_0_org, -1,ksize=3)
x_train_0_org_laplace_grey = cv2.cvtColor(x_train_0_org_laplace, cv2.COLOR_BGR2GRAY)
plt.imshow(x_train_0_org_laplace_grey)
plt.title('org_laplace_grey')
plt.show()
n_row=5
n_col=3
for Kin range(n_row):
print(K)
plt.subplot(n_row,3, K*3+1)
x_train_0_org = x_img_train[K, :, :,0:3]
# img_adv_laplacian=cv2.Laplacian(img_adv_np, -1, ksize=3)
plt.imshow(x_train_0_org)
# plt.axis["top", 'right'].set_visible(False)
if (K==0):
plt.title('org')
plt.subplot(n_row,3, K*3+2)
x_train_0_org_bilateral = cv2.bilateralFilter(x_train_0_org,9,75,75)
x_train_0_org_bilateral_canny = cv2.Canny(x_train_0_org_bilateral,75,200)
plt.imshow(x_train_0_org_bilateral_canny)
if (K==0):
plt.title('org_bilateral_canny')
plt.subplot(n_row,3, K*3+3)
x_train_0_org_laplace = cv2.Laplacian(x_train_0_org, -1,ksize=3)
x_train_0_org_laplace_grey = cv2.cvtColor(x_train_0_org_laplace, cv2.COLOR_BGR2GRAY)
plt.imshow(x_train_0_org_laplace_grey)
if (K==0):
plt.title('org_laplace_grey')
plt.show()
x_train_new=np.zeros((50000,32,32,4))
for kin range(50000):
x_train_new[k, :, :,0:3] = x_img_train[k]
x_train_temp=x_img_train[k]
x_train_temp=cv2.bilateralFilter(x_train_temp,9,75,75)
x_train_temp = cv2.Canny(x_train_temp,75,200)
#x_train_temp = cv2.Laplacian(x_train_temp, -1, ksize=3)
#x_train_temp = cv2.cvtColor(x_train_temp, cv2.COLOR_BGR2GRAY)
x_train_new[k, :, :,3] = x_train_temp
'''
