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utils.py
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utils.py
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import numpy as np
import tensorflow as tf
from keras.datasets import mnist
from keras.utils import to_categorical
from PIL import Image
import io
import json
import os
import IPython.display as display
import cv2
import librosa
import librosa.display
import math
import random
import re
# DIR = "/content/drive/My Drive/Deepfake Detection/data/deepfake_images/DeepFake00/DeepFake00"
DIR = "/content/drive/My Drive/Deepfake Detection/data/deepfake_images/"
def read_audio_tfrecords():
tfrecords = []
data_dir = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/audio_tfrecords/"
for tfrecord in sorted(os.listdir(data_dir)):
if tfrecord.endswith('.tfrecord') and tfrecord[:3]=='dev':
print(tfrecord)
tfrecords.append(os.path.join(data_dir, tfrecord))
real_raw_audio_dataset = tf.data.TFRecordDataset(tfrecords)
audio_feature_description = {
# 'audio': tf.io.FixedLenFeature([], tf.float32)
'audio': tf.io.FixedLenSequenceFeature([],tf.float32,allow_missing =True),
'label': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'height': tf.io.FixedLenFeature([], tf.int64),
'channels': tf.io.FixedLenFeature([], tf.int64)
}
def _parse_audio_function(serialized):
parsed_features = tf.io.parse_single_example(serialized, audio_feature_description)
img = tf.reshape(parsed_features['audio'],[parsed_features['width'],parsed_features['height'],parsed_features['channels']])
# audio = tf.io.decode_raw(parsed_features['audio'],out_type=float)
# audio = tf.cast(parsed_features['audio'], tf.float32)
label = tf.reshape(parsed_features['label'],[1,1])
return img,parsed_features['label']
dataset = real_raw_audio_dataset.map(_parse_audio_function)
fake = dataset.filter(lambda x,y: y == 0)
real = dataset.filter(lambda x,y: y == 1)
# dev- total: 21309 bonafide-2442 spoof-18867
real = real.repeat(5)
real = real.take(10000)
fake = fake.take(10000)
##To interleave real and fake
dataset = tf.data.Dataset.zip((real, fake)).flat_map(
lambda x0, x1: tf.data.Dataset.from_tensors(x0).concatenate(
tf.data.Dataset.from_tensors(x1)))
return dataset
def label_img(img,subdir):
digit = int(subdir[-2:])
f = open("/content/drive/My Drive/Deepfake Detection/data/metadata/metadata"+str(digit)+".json", "r")
data = json.loads(f.read().strip())
v = img.replace(".jpg",".mp4")
return data[v]["label"],data[v]["split"]
def loadData():
print("In loadData")
train_data = []
x_train = []
y_train = []
x_test = []
y_test = []
count = 0
for subdir, dirs, files in os.walk(DIR):
for img in files:
print(count,": ",img)
label,split = label_img(img,subdir)
if(label=="REAL"):
label = 1
elif(label=="FAKE"):
label = 0
path = os.path.join(subdir, img)
img = Image.open(path)
img = img.convert('L')
img = np.array(img)
img = img.astype("uint8")
if(split=="test"):
x_test.append(img)
y_test.append(label)
else:
x_train.append(img)
y_train.append(label)
count += 1
if count==2:
break
if count==2:
break
# img = img.resize((IMG_SIZE, IMG_SIZE), Image.ANTIALIAS)
# train_data.append([np.array(img), label])
# Basic Data Augmentation - Horizontal Flipping
# flip_img = Image.open(path)
# flip_img = flip_img.convert('L')
# flip_img = flip_img.resize((IMG_SIZE, IMG_SIZE), Image.ANTIALIAS)
# flip_img = np.array(flip_img)
# flip_img = np.fliplr(flip_img)
# train_data.append([flip_img, label])
# shuffle(train_data)
return (np.array(x_train), np.array(y_train)), (np.array(x_test), np.array(y_test))
def generate_batches(files, batch_size):
counter = 0
while True:
fname = files[counter]
print(fname)
counter = (counter + 1) % len(files)
data_bundle = pickle.load(open(fname, "rb"))
X_train = data_bundle[0].astype(np.float32)
y_train = data_bundle[1].astype(np.float32)
y_train = y_train.flatten()
for cbatch in range(0, X_train.shape[0], batch_size):
yield (X_train[cbatch:(cbatch + batch_size),:,:], y_train[cbatch:(cbatch + batch_size)])
def getTrainData():
tfrecords = []
data_dir = "/content/drive/My Drive/Deepfake Detection/data/tfrecords"
count = 0
for tfrecord in sorted(os.listdir(data_dir)):
if tfrecord.endswith('.tfrecords'):
count += 1
if(count>25):
break
print(tfrecord)
tfrecords.append(os.path.join(data_dir, tfrecord))
# raw_image_dataset = tf.data.TFRecordDataset("/content/drive/My Drive/Deepfake Detection/data/tfrecords/DeepFake0.tfrecords")
raw_image_dataset = tf.data.TFRecordDataset(tfrecords)
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'split': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
# image = tf.cast(tf.decode_raw(features['image_raw'], tf.uint8), tf.float32)
# height = tf.cast(features['height'], tf.int32)
# width = tf.cast(features['width'], tf.int32)
# depth = tf.cast(features['depth'], tf.int32)
# label = tf.cast(features['label'], tf.int32)
# split = tf.cast(features['split'], tf.int32)
def _parse_image_function(example_proto):
return tf.io.parse_single_example(example_proto, image_feature_description)
parsed_image_dataset = raw_image_dataset.map(_parse_image_function)
print("Parsed Image Dataset:",parsed_image_dataset)
# c=0
x_train = []
y_train = []
x_test = []
y_test = []
# parsed_image_dataset = parsed_image_dataset.batch(128).prefetch(10).take(5)
# l = len(parsed_image_dataset)
l = 0
r = 0
f =0
real =0
fake =0
for image_features in parsed_image_dataset:
l += 1
label = image_features['label']
if label == 1:
r+=1
else:
f+=1
print("Length:",l)
dc = 0
for image_features in parsed_image_dataset:
image_raw = image_features['image_raw'].numpy()
# print(image_raw.shape)
# print(type(image_raw))
img = Image.open(io.BytesIO(image_raw))
image = np.asarray(img)
# image = tf.cast(tf.io.decode_raw(image_raw, tf.uint8), tf.float32).numpy()
# image = np.fromstring(image_raw, dtype=np.uint8)
# print("Shape before reshaping:",image.shape,image)
#We have to convert it into (270, 480,3) in order to see as an image
# image = image.reshape((150,150,3))
img = np.dot(image[...,:3], [0.299, 0.587, 0.114]) # Converting RGB to Grayscale
# print("img Shape:",image.shape)
label = image_features['label']
if(label==1):
real+=1
else:
fake+=1
if(fake>=r and label==0):
continue
if(dc<0.8*(r*2)): # If split is test
x_train.append(img)
y_train.append(label)
else:
x_test.append(img)
y_test.append(label)
dc += 1
# print(c)
# f = open("/content/drive/My Drive/img/img{}.jpg".format(c),'wb')
# c+=1
# f.write(image_raw)
# cv2.imwrite("images/img1.jpg",image_raw)
# display.display(display.Image(data=image_raw))
# break
print("fake and real ",fake,real)
print("xtrain and ytrain", len(x_train), len(y_train))
print("xtest and ytest", len(x_test), len(y_test))
return (np.array(x_train), np.array(y_train)), (np.array(x_test), np.array(y_test))
def getTrainDataBatch():
tfrecords = []
data_dir = "/content/drive/My Drive/Deepfake Detection/data/tfrecords"
count = 0
# for tfrecord in sorted(os.listdir(data_dir)):
# count += 1
# if tfrecord.endswith('.tfrecords'):
# if(count<25):
# continue
# print(tfrecord)
# tfrecords.append(os.path.join(data_dir, tfrecord))
for tfrecord in sorted(os.listdir(data_dir)):
count += 1
if tfrecord.endswith('.tfrecords'):
if(count>=40):
break
print(tfrecord)
tfrecords.append(os.path.join(data_dir, tfrecord))
# raw_image_dataset = tf.data.TFRecordDataset("/content/drive/My Drive/Deepfake Detection/data/tfrecords/DeepFake0.tfrecords")
raw_image_dataset = tf.data.TFRecordDataset(tfrecords)
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'split': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
def _parse_image_function(serialized):
parsed_features = tf.io.parse_single_example(serialized, image_feature_description)
# print("TYPE IMAGE_RAW: ",type(parsed_features['image_raw']))
# print("IMAGE_RAW: ",parsed_features['image_raw'])
# parsed_features_train = parsed_features['image_raw'].numpy()
img = tf.image.decode_jpeg(parsed_features['image_raw'], channels=3)
img = tf.image.resize(img, [150, 150])
img = tf.tensordot(img, tf.constant([0.299, 0.587, 0.114]), 1)
img = tf.reshape(img,[150*150,1])
# img = tf.reshape(img,[150,150,3])
# img = tf.keras.applications.efficientnet.preprocess_input(img)
# img = tf.reshape(img,[1,150*150,3])
# print("Decoded: ",img)
# parsed_features_train = tf.io.decode_raw(parsed_features['image_raw'], tf.uint8)
# parsed_features_train = np.dot(parsed_features_train[...,:3], [0.299, 0.587, 0.114]) # Converting RGB to Grayscale
#print("ffew",parsed_features_train)
# print("Converted IMAGE-----------------: ",parsed_features_train,tf.shape(parsed_features_train) )
# parsed_features_train = tf.reshape(parsed_features_train[0], [150 * 150, 1])
#parsed_features_train = tf.reshape(parsed_features['image_raw'], [150 * 150, 1])
# parsed_features_train = parsed_features['image_raw'].reshape(150 * 150, 1)
# img = preprocess_input(img)
parsed_features_train = img
# num_classes = 2
# tf.print("Y",parsed_features['label'])
#print(tf.compat.v1.Session().run(parsed_features['label']))
# y_train = to_categorical([parsed_features['label']], num_classes)
# y_train = np.expand_dims(y_train, axis=2)
# parsed_features_train = parsed_features_train.astype('float32')
parsed_features_train /= 255
# l2=tf.cast(tf.constant(1),dtype=tf.int64)
# label = tf.stack([parsed_features['label'], l2],axis=-1)
# label = tf.reshape(label,[1,2])
# label = tf.reshape(parsed_features['label'],[1,1])
# return label,parsed_features_train
return parsed_features_train, parsed_features['label']
dataset = raw_image_dataset.map(_parse_image_function)
# print("Parsed Image Dataset:",dataset)
##Fix imbalanced data
fake = dataset.filter(lambda x,y: y == 0)
real = dataset.filter(lambda x,y: y == 1)
real=real.repeat(4)
fake=fake.take(50000)
real=real.take(50000)
##To interleave real and fake
dataset = tf.data.Dataset.zip((real, fake)).flat_map(
lambda x0, x1: tf.data.Dataset.from_tensors(x0).concatenate(
tf.data.Dataset.from_tensors(x1)))
return dataset
def data_generator():
# input image dimensions
img_rows, img_cols = 150, 150
# img_rows, img_cols = 28, 28
# (x_train, y_train), (x_test, y_test) = loadData()
(x_train, y_train), (x_test, y_test) = getTrainData()
# (x_train, y_train), (x_test, y_test) = getTrainDataBatch()
# (x_train, y_train), (x_test, y_test) = mnist.load_data()
# print("Shape:",x_train.shape)
x_train = x_train.reshape(-1, img_rows * img_cols, 1)
x_test = x_test.reshape(-1, img_rows * img_cols, 1)
num_classes = 2
# num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
y_train = np.expand_dims(y_train, axis=2)
y_test = np.expand_dims(y_test, axis=2)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
return (x_train, y_train), (x_test, y_test)
def audio_data_generator():
audio_dir = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_train/"
x_train=[]
y_train=[]
x_test=[]
y_test=[]
f = open("/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_cm_protocols/ASVspoof2019.LA.cm.train.trn.txt")
b = 0
s = 0
bonafide = 0
spoof = 0
dc = 0
lines = f.readlines()
# l = lines[:501] + lines[2581:3081]
# l = lines[:101] + lines[2981:3081]
l = lines[:2580] + lines[2600:5180]
for line in l:
filename = line.split()[1].strip()
print(dc, filename)
label = line.split()[-1].strip()
audio_data = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_train/flac/" + filename + ".flac"
y,sr = librosa.load(audio_data, sr=None)
# max_length = 80000
# max_length = 150000
# if(y.shape[0]>max_length):
# y = y[:max_length]
# else:
# silence = np.zeros(max_length-y.shape[0],)
# y = np.concatenate((y,silence))
# print(y.shape)
n_mels = 64
n_fft = int(np.ceil(0.025*sr))
win_length = int(np.ceil(0.025*sr))
hop_length = int(np.ceil(0.010*sr))
window = 'hamming'
fmin = 20
fmax = 8000
S = librosa.core.stft(y, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=False)
frames = np.log(librosa.feature.mfcc(y=y, sr=sr, S=S, n_mels=n_mels, fmin=fmin, fmax=fmax) + 1e-6)
# print("Affter mfcc",frames.shape)
# dimension_frame=frames.shape[1]
# pad_dimension=(100-dimension_frame%100)
# silence = np.zeros((20,pad_dimension))
# frames=np.concatenate((frames,silence),axis=1)
# print("Affter pad",frames.shape)
# window_size = 64
# window_hop = 30
# truncate at start and end to only have windows full data
# alternative would be to zero-pad
# start_frame = window_size
# end_frame = window_hop * math.floor(float(frames.shape[1]) / window_hop)
if(label=="bonafide"):
label = 1
b += 1
else:
label = 0
s += 1
if(label==1):
bonafide += 1
else:
spoof += 1
print(b,s,sep=" ")
# if(dc<=800):
if(dc<4128):
x_train.append(frames)
y_train.append(label)
else:
x_test.append(frames)
y_test.append(label)
# ctr = 0
# for frame_idx in range(start_frame, end_frame, window_hop):
# window = frames[:, frame_idx-window_size:frame_idx]
# if(dc<=400):
# x_train.append(window)
# y_train.append(label)
# else:
# x_test.append(window)
# y_test.append(label)
# ctr+=1
# # print('classify window', frame_idx, window.shape)
dc += 1
##Padding
max_dimension=0
for i in range(len(x_train)):
element_shape=x_train[i].shape[1]
max_dimension=max(max_dimension,element_shape)
print("Max length",max_dimension)
for i in range(len(x_train)):
# print("Before pad",x_train[i].shape)
cur_dimension=x_train[i].shape[1]
# pad_dimension=(100-dimension_frame%100)
silence = np.zeros((20,max_dimension-cur_dimension))
x_train[i]=np.concatenate((x_train[i],silence),axis=1)
print("After pad",x_train[i].shape)
max_dimension_test=0
for i in range(len(x_test)):
element_shape=x_test[i].shape[1]
max_dimension_test=max(max_dimension_test,element_shape)
# print("Max length",max_dimension)
for i in range(len(x_test)):
# print("Before pad",x_train[i].shape)
cur_dimension=x_test[i].shape[1]
# pad_dimension=(100-dimension_frame%100)
silence = np.zeros((20,max_dimension_test-cur_dimension))
x_test[i]=np.concatenate((x_test[i],silence),axis=1)
# print("Affter pad",x_train[i].shape)
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
# print("inside",x_train.shape)
audio_rows = 20
x_train = x_train.reshape(-1, audio_rows * max_dimension, 1)
x_test = x_test.reshape(-1, audio_rows * max_dimension_test, 1)
# print("Shape of x_train:",x_train.shape)
# print("Shape of y_train:",y_train.shape)
# x_train = x_train.reshape(len(x_train),20,max_dimension)
# x_test = x_test.reshape(len(x_test),20,max_dimension_test)
# x_train=x_train.reshape((x_train.shape[0],20*max_dimension,1))
# x_test=x_test.reshape((x_test.shape[0],20*max_dimension_test,1))
# x_test = x_test.reshape((len(x_test), max_dimension_test, 1))
num_classes = 2
# num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
# y_train = np.reshape(y_train, (-1, 1))
# y_test = np.reshape(y_test, (-1, 1))
# y_train = np.expand_dims(y_train, axis=2)
# print("y_train:",y_train)
# y_test = np.expand_dims(y_test, axis=2)
y_train = np.expand_dims(y_train, axis=1)
# print("y_train:",y_train)
y_test = np.expand_dims(y_test, axis=1)
# y_train=y_train.reshape((len(y_train)1,2,1))
# y_test=y_test.reshape((len(y_test),2,1))
# y_train = x_train.reshape(20,max_dimension,len(x_train))
# y_train = np.asarray(y_train).astype('float32').reshape((-1,1))
# y_test = np.asarray(y_test).astype('float32').reshape((-1,1))
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
return (x_train, y_train), (x_test, y_test)
def _linear_to_mel(spectogram):
_mel_basis = librosa.filters.mel(16000, 1000, n_mels=240,fmin=0, fmax=8000)
return np.dot(_mel_basis, spectogram)
def _amp_to_db(x):
min_level = np.exp(-100 / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))
def get_melspectrogram(wav):
D = librosa.stft(y=wav, n_fft=1000, hop_length=200, win_length=800, pad_mode='constant')
S = _amp_to_db(_linear_to_mel(np.abs(D)**2.)) - 20
return np.clip((2 * 1.) * ((S - (-100)) / (-(100))) - 1., -(1.), 1.)
def get_spectogram():
audio_dir = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_train/"
x_train=[]
y_train=[]
x_test=[]
y_test=[]
f = open("/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_cm_protocols/ASVspoof2019.LA.cm.train.trn.txt")
b = 0
s = 0
bonafide = 0
spoof = 0
dc = 0
lines = f.readlines()
l = lines[:2580] + lines[2600:5180]
# l = lines[:101] + lines[2981:3081]
# l = lines[:6] + lines[3075:3081]
random.shuffle(l)
for line in l:
filename = line.split()[1].strip()
print(dc, filename)
label = line.split()[-1].strip()
audio_data = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_train/flac/" + filename + ".flac"
audio_array,sr = librosa.load(audio_data, sr=16000)
trim_audio_array, index = librosa.effects.trim(audio_array)
S = get_melspectrogram(trim_audio_array).T
# S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,fmax=8000)
# print(type(S))
# print(S.shape)
max_dimension = 240
max_dimension_test = 240
curr_dimension = S.shape[0]
if(max_dimension_test>curr_dimension):
silence = np.zeros((max_dimension_test-curr_dimension,240))
S = np.concatenate((S,silence),axis=0)
else:
S = S[:240,:]
print(S.shape)
if(label=="bonafide"):
label = 1
else:
label = 0
if(dc<=4128):
x_train.append(S)
y_train.append(label)
else:
x_test.append(S)
y_test.append(label)
dc += 1
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
x_train = x_train.reshape(-1, 240 * max_dimension, 1)
x_test = x_test.reshape(-1, 240 * max_dimension_test, 1)
num_classes = 2
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
y_train = np.expand_dims(y_train, axis=1)
y_test = np.expand_dims(y_test, axis=1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
return (x_train, y_train), (x_test, y_test)
def scale_minmax(X, min=0.0, max=1.0):
X_std = (X - X.min()) / (X.max() - X.min())
X_scaled = X_std * (max - min) + min
return X_scaled
def get_spectogram_new():
audio_dir = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_train/"
x_train=[]
y_train=[]
x_test=[]
y_test=[]
f = open("/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_cm_protocols/ASVspoof2019.LA.cm.train.trn.txt")
b = 0
s = 0
bonafide = 0
spoof = 0
dc = 0
lines = f.readlines()
l = lines[:2580] + lines[2600:5180]
# l = lines[:101] + lines[2981:3081]
# l = lines[:6] + lines[3075:3081]
random.shuffle(l)
for line in l:
filename = line.split()[1].strip()
print(dc, filename)
label = line.split()[-1].strip()
audio_data = "/content/drive/My Drive/Deepfake Detection/data/deepfake_audio/LA/ASVspoof2019_LA_train/flac/" + filename + ".flac"
audio_array,sr = librosa.load(audio_data, sr=16000)
trim_audio_array, index = librosa.effects.trim(audio_array)
S = librosa.feature.melspectrogram(trim_audio_array)
mels = np.log(S + 1e-9) # add small number to avoid log(0)
# min-max scale to fit inside 8-bit range
img = scale_minmax(mels, 0, 255).astype(np.uint8)
img = np.flip(img, axis=0) # put low frequencies at the bottom in image
img = 255-img # invert. make black==more energy
if img.shape[1]<60:
continue
itr=0
if(label=="bonafide"):
label = 1
else:
label = 0
while(itr+60<img.shape[1]):
img = cv2.cvtColor(img[:,itr:itr+60],cv2.COLOR_GRAY2RGB)
x_train.append(img[:,itr:itr+60])
y_train.append(label)
itr+=60
dc += 1
x_test= x_train[:int(0.2*len(x_train))]
x_train= x_train[int(0.2*len(x_train)):]
y_test = y_train[:int(0.2*len(y_train))]
y_train = y_train[int(0.2*len(y_train)):]
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
# x_train = x_train.reshape(-1, 128 * 60, 1)
# x_test = x_test.reshape(-1, 128 * 60, 1)
# num_classes = 2
# y_train = to_categorical(y_train, num_classes)
# y_test = to_categorical(y_test, num_classes)
# y_train = np.expand_dims(y_train, axis=1)
# y_test = np.expand_dims(y_test, axis=1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
return (x_train, y_train), (x_test, y_test)
'''
def body_data_generator():
real_body_dir = "/content/drive/My Drive/Deepfake Detection/data/body_language/Final Dataset/Real"
fake_body_dir = "/content/drive/My Drive/Deepfake Detection/data/body_language/Final Dataset/Fake"
x_train=[]
y_train=[]
x_test=[]
y_test=[]
for vid in os.listdir(real_body_dir):
vidcap = cv2.VideoCapture(real_body_dir+"/"+vid)
cv2.waitKey(delay_time)
success,image = vidcap.read()
count = 0
while success:
count += 1
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if(count%60!=0):
continue
x_train.append(img)
y_train.append(1)
x_test = x_train[int(len(x_train)*0.9):]
y_test = y_train[int(len(y_train)*0.9):]
x_train = x_train[:int(len(x_train)*0.9)]
y_train = y_train[:int(len(y_train)*0.9)]
print("Real body dir: x_train",len(x_train))
for vid in os.listdir(fake_body_dir):
vidcap = cv2.VideoCapture(fake_body_dir+"/"+vid)
success,image = vidcap.read()
count = 0
while success:
count += 1
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if(count%60!=0):
continue
x_train.append(img)
y_train.append(0)
x_test += x_train[int(len(x_train)*0.9):]
y_test = y_train[int(len(y_train)*0.9):]
x_train = x_train[:int(len(x_train)*0.9)]
y_train = y_train[:int(len(y_train)*0.9)]
print("Fake body dir: x_train",len(x_train))
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
img_rows = 150
img_cols = 150
x_train = x_train.reshape(-1, img_rows * img_cols, 1)
x_test = x_test.reshape(-1, img_rows * img_cols, 1)
num_classes = 2
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
y_train = np.expand_dims(y_train, axis=2)
y_test = np.expand_dims(y_test, axis=2)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
indices = np.arange(x_train.shape[0])
np.random.shuffle(indices)
x_train = x_train[indices]
y_train = y_train[indices]
indices = np.arange(x_test.shape[0])
np.random.shuffle(indices)
x_test = x_test[indices]
y_test = y_test[indices]
return (x_train, y_train), (x_test, y_test)
'''
def get_Body_Data():
real_tfrecords = []
real_datasets=[]
fake_datasets=[]
real_data_dir = "/content/drive/My Drive/Deepfake Detection/data/body_language/TFRecords/Real"
for tfrecord in sorted(os.listdir(real_data_dir)):
if tfrecord.endswith('.tfrecords'):
print(tfrecord)
d=tf.data.TFRecordDataset(os.path.join(real_data_dir, tfrecord))
d=d.take(4000)
real_datasets.append(d)
real_tfrecords.append(os.path.join(real_data_dir, tfrecord))
fake_tfrecords = []
fake_data_dir = "/content/drive/My Drive/Deepfake Detection/data/body_language/TFRecords/Fake"
for tfrecord in sorted(os.listdir(fake_data_dir)):
if tfrecord.endswith('.tfrecords'):
print(tfrecord)
d=tf.data.TFRecordDataset(os.path.join(fake_data_dir, tfrecord))
d=d.take(1300)
fake_datasets.append(d)
fake_tfrecords.append(os.path.join(fake_data_dir, tfrecord))
real_raw_image_dataset = real_datasets[0]
for i in real_datasets[1:]:
real_raw_image_dataset=real_raw_image_dataset.concatenate(i)
# fake_raw_image_dataset = fake_datasets[0].concatenate(i for i in fake_datasets[1:])
fake_raw_image_dataset = fake_datasets[0]
for i in fake_datasets[1:]:
fake_raw_image_dataset=fake_raw_image_dataset.concatenate(i)
# real_raw_image_dataset = tf.data.TFRecordDataset(real_tfrecords)
# fake_raw_image_dataset = tf.data.TFRecordDataset(fake_tfrecords)
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
def _parse_image_function_real(serialized):
parsed_features = tf.io.parse_single_example(serialized, image_feature_description)
img = tf.image.decode_jpeg(parsed_features['image_raw'], channels=3)
# Original IMage Dimensions
# img = tf.image.resize(img, [512, 1024])
img = tf.image.resize(img, [150,150])
img = tf.tensordot(img, tf.constant([0.299, 0.587, 0.114]), 1)
img = tf.reshape(img,[150*150,1])
parsed_features_train = img
num_classes = 2
parsed_features_train /= 255
# label = tf.reshape(parsed_features['label'],[1,1])
# label = tf.convert_to_tensor([1], dtype=tf.int64)
# label = tf.reshape(label,[2,1])
return parsed_features_train, 1
def _parse_image_function_fake(serialized):
parsed_features = tf.io.parse_single_example(serialized, image_feature_description)
img = tf.image.decode_jpeg(parsed_features['image_raw'], channels=3)
img = tf.image.resize(img, [150, 150])
img = tf.tensordot(img, tf.constant([0.299, 0.587, 0.114]), 1)
img = tf.reshape(img,[150*150,1])
parsed_features_train = img
num_classes = 2
parsed_features_train /= 255
# label = tf.reshape(parsed_features['label'],[1,1])
#Hard Coded
label = tf.convert_to_tensor([0], dtype=tf.int64)
# label = tf.reshape(label,[2,1])
return parsed_features_train, 0
real_dataset = real_raw_image_dataset.map(_parse_image_function_real)
fake_dataset = fake_raw_image_dataset.map(_parse_image_function_fake)
# real_dataset = real_dataset.shuffle(250, reshuffle_each_iteration=True)
# fake_dataset = fake_dataset.shuffle(250, reshuffle_each_iteration=True)
# real_dataset = real_dataset.take(30000)
# fake_dataset = fake_dataset.take(30000)
dataset = tf.data.Dataset.zip((real_dataset, fake_dataset)).flat_map(
lambda x0, x1: tf.data.Dataset.from_tensors(x0).concatenate(
tf.data.Dataset.from_tensors(x1)))
# dataset = dataset.shuffle(buffer_size=256)
# dataset = dataset.take(50)
# print(dataset)
return dataset
def body_data_generator():
x_train=[]
y_train=[]
x_test=[]
y_test=[]
real_body_dir = "/content/drive/My Drive/Deepfake Detection/data/body_language/List Dataset/Real"
fake_body_dir = "/content/drive/My Drive/Deepfake Detection/data/body_language/List Dataset/Fake"
for img_name in os.listdir(real_body_dir):
#read in grayscale
image = cv2.imread(os.path.join(real_body_dir,img_name), 0)
##Resize to 256*256 since fake images are of size 256*256
# image = cv2.resize(image, (256, 256))
image = cv2.resize(image, (150, 150))
x_train.append(image)
# real -> 1
y_train.append(1)
for img_name in os.listdir(fake_body_dir):
#read in grayscale
# image = cv2.imread(os.path.join(fake_body_dir,img_name), 0)
image = cv2.imread(os.path.join(fake_body_dir,img_name), 0)
##Resize to 256*256 since fake images are of size 256*256
# image = cv2.resize(image, (256, 256))
image = cv2.resize(image, (150, 150))
x_train.append(image)
# real -> 1
y_train.append(0)
temp = list(zip(x_train, y_train))
random.shuffle(temp)
x_train, y_train = zip(*temp)
#Reduce images for temporary arrangements
# x_train= x_train[int(len(x_train)*0.5):]
# y_train= y_train[int(len(y_train)*0.5):]
x_test = x_train[int(len(x_train)*0.8):]
x_train = x_train[:int(len(x_train)*0.8)]
y_test = y_train[int(len(y_train)*0.8):]
y_train = y_train[:int(len(y_train)*0.8)]
print("Shape:",x_train[0].shape)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
print("Afet Shape:",x_train.shape)
img_rows = 150
img_cols = 150
x_train = x_train.reshape(-1, img_rows * img_cols, 1)
x_test = x_test.reshape(-1, img_rows * img_cols, 1)
num_classes = 2
# num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
y_train = np.expand_dims(y_train, axis=2)
y_test = np.expand_dims(y_test, axis=2)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
return (x_train, y_train), (x_test, y_test)
if __name__ == '__main__':
# print(data_generator())
getTrainDataBatch()
# (x_train, y_train), (x_test, y_test) = body_data_generator()
# print(x_train.shape,y_train.shape)
# print(x_test.shape,y_test.shape)
# audio_data_generator()
# (x_train, y_train), (x_test, y_test) = audio_data_generator()
# (x_train, y_train), (x_test, y_test) = get_spectogram()