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main.py
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main.py
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"""
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.log_device_placement = True
set_session(tf.Session(config=config))
"""
from keras import backend as K
from PIL import Image
import cv2
import numpy as np
from scipy import ndimage
from scipy.misc import imresize
from sklearn.model_selection import train_test_split
import os
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.models import Model
from keras.layers import Input, merge,Conv2D,MaxPool2D,ZeroPadding2D,Dense,Flatten,concatenate,Lambda
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from preprocess import normalize_image,remove_background
lst=['1','2','3','4']
real=[]
forg=[]
for i in range(3):
real.append(os.listdir('Dataset/dataset'+lst[i]+'/real/'))
forg.append(os.listdir('Dataset/dataset'+lst[i]+'/forge/'))
img=plt.imread('Dataset/dataset'+lst[0]+'/real/'+real[0][0])
plt.imshow(img)
def res(path,nm):
mywidth = 500
img = Image.open(path)
wpercent = (mywidth/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
img = img.resize((mywidth,hsize), PIL.Image.ANTIALIAS)
img.save(nm)
for k in range(3):
pathg='Dataset/dataset'+lst[k]+'/real/'
pathf='Dataset/dataset'+lst[k]+'/forge/'
for j in range(len(real[k])):
path=pathg+real[k][j]
res(path,path)
for j in range(len(forg[k])):
path=pathf+forg[k][j]
res(path,path)
mxg=0
for k in range(3):
pathg='Dataset/dataset'+lst[k]+'/real/'
pathf='Dataset/dataset'+lst[k]+'/forge/'
for j in range(len(real[k])):
path=pathg+real[k][j]
img=cv2.imread(path)
if(img.shape[0]>mxg):
a=img.shape
mxg=img.shape[0]
for j in range(len(forg[k])):
path=pathf+forg[k][j]
img=cv2.imread(path)
if(img.shape[0]>mxg):
a=img.shape
mxg=img.shape[0]
for k in range(3):
pathg='Dataset/dataset'+lst[k]+'/real/'
pathf='Dataset/dataset'+lst[k]+'/forge/'
for j in range(len(real[k])):
path=pathg+real[k][j]
img=cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgnr=normalize_image(gray,(mxg,500))
cv2.imwrite(path,imgnr)
for j in range(len(forg[k])):
path=pathf+forg[k][j]
img=cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgnr=normalize_image(gray,(mxg,500))
cv2.imwrite(path,imgnr)
xx2=np.zeros((3,549,500))
x=[]
xx=[]
m=0
for k in range(3):
pathg='Dataset/dataset'+lst[k]+'/real/'
pathf='Dataset/dataset'+lst[k]+'/forge/'
for j in range(len(real[k])):
for i in range(len(real[k])):
if(real[k][j][-7:-4]==real[k][i][-7:-4] and real[k][i]!=real[k][j]):
for n in range(len(real[k])):
if(real[k][i][-7:-4]==forg[k][n][-7:-4]):
anchor=cv2.imread(pathg+real[k][j])
anchor = cv2.cvtColor(anchor, cv2.COLOR_BGR2GRAY)
positive=cv2.imread(pathg+real[k][i])
positive = cv2.cvtColor(positive, cv2.COLOR_BGR2GRAY)
negative=cv2.imread(pathf+forg[k][n])
negative = cv2.cvtColor(negative, cv2.COLOR_BGR2GRAY)
x.append([anchor,positive,negative])
final=np.stack((anchor,positive,negative))
np.hstack((xx2,final))
xx.append([real[k][j],real[k][i],forg[k][n]])
x=np.asarray(x)
y=np.zeros((6000,1))
x_train,x_test,y_train,y_test=train_test_split(x.reshape(6000, 3, 549, 500,1),y,test_size=0.2,random_state=42)
anchor_tr=[]
positive_tr=[]
negative_tr=[]
for i in range(len(x_train)):
anchor_tr.append(x_train[i][0])
positive_tr.append(x_train[i][1])
negative_tr.append(x_train[i][2])
anchor_ts=[]
positive_ts=[]
negative_ts=[]
for i in range(len(x_test)):
anchor_ts.append(x_test[i][0])
positive_ts.append(x_test[i][1])
negative_ts.append(x_test[i][2])
anchor_tr=np.asarray(anchor_tr)
positive_tr=np.asarray(positive_tr)
negative_tr=np.asarray(negative_tr)
anchor_ts=np.asarray(anchor_ts)
positive_ts=np.asarray(positive_ts)
negative_ts=np.asarray(negative_ts)
# Architecture of network
def create_shared_network(shape):
inp=Input(shape=shape)
layer=Conv2D(96,kernel_size=(11,11),strides=(4,4),padding="same",activation="relu")(inp)
layer=MaxPool2D(pool_size=(3,3),strides=(2,2))(layer)
layer=ZeroPadding2D(padding=(2,2))(layer)
layer=Conv2D(256,kernel_size=(5,5),padding="valid",activation="relu")(layer)
layer=MaxPool2D(pool_size=(3,3),strides=(2,2))(layer)
layer=Conv2D(384,kernel_size=(3,3),padding="same",activation="relu")(layer)
layer=Conv2D(384,kernel_size=(3,3),padding="same",activation="relu")(layer)
layer=Conv2D(256,kernel_size=(3,3),padding="valid",activation="relu")(layer)
layer=MaxPool2D(pool_size=(3,3),strides=(2,2))(layer)
layer=Flatten()(layer)
layer=Dense(1024,activation="relu")(layer)
layer=Dense(1024,activation="relu")(layer)
layer=Dense(256)(layer)
out = Lambda(lambda x: K.l2_normalize(x, axis=1), name='norm_layer')(layer)
model=Model(inputs=inp,outputs=out)
return model
md=create_shared_network((549,500,1))
md.summary()
ALPHA = 0.8
beta=128
epsilon=1e-8
N=128
def triplet_loss(x):
anchor, positive, negative = x
pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1)
neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1)
basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), ALPHA)
loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
return loss
def build_model(input_shape):
anchor_example = Input(shape=input_shape)
positive_example = Input(shape=input_shape)
negative_example = Input(shape=input_shape)
shared_network = create_shared_network(input_shape)
anchor_shared = shared_network(anchor_example)
positive_shared = shared_network(positive_example)
negative_shared = shared_network(negative_example)
loss = merge([anchor_shared, positive_shared, negative_shared],
mode=triplet_loss, output_shape=(1,))
model = Model(inputs=[anchor_example, positive_example, negative_example],
outputs=loss)
adam = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss="mae", optimizer='adam')
return model
model=build_model((549,500,1))
model.summary()
checkpoint = ModelCheckpoint('wt/model_2:'+str(k)+' {epoch:03d}-{val_loss:.4f}.hdf5', monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
model.fit([anchor_tr,positive_tr,negative_tr], y_train, batch_size=100, epochs=50, verbose=1,callbacks=[checkpoint], validation_data=([anchor_ts,positive_ts,negative_ts],y_test))