-
Notifications
You must be signed in to change notification settings - Fork 0
/
execute.py
140 lines (108 loc) · 4.71 KB
/
execute.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 12 14:27:05 2021
@author: saaransh
"""
import cv2
import numpy as np
import os
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model
#its loading the contents of the file my laptop is a kinda slow so thats why its taking time
cap= cv2.VideoCapture('C:/Users/saaransh/Downloads/train_sample_videos/btiysiskpf.mp4')
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
i=0
while(cap.isOpened()):
ret, frame = cap.read()
if ret == False:
break
if i%100 == 0:
cv2.imwrite(os.path.join('./images' , str(i) + '.jpg'),frame)
image = cv2.imread('./images/{}.jpg'.format(i))
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Make a copy of the original image to draw face detections on
image_copy = np.copy(image)
# Convert the image to gray
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
faces = face_cascade.detectMultiScale(gray_image, 1.25, 6)
# Print number of faces found
print('Number of faces detected:', len(faces))
# Get the bounding box for each detected face
for f in faces:
x, y, w, h = [ v for v in f ]
cv2.rectangle(image_copy, (x,y), (x+w, y+h), (255,0,0), 3)
# Define the region of interest in the image
face_crop = gray_image[y:y+h, x:x+w]
# Display the image with the bounding boxes
fig = plt.figure(figsize = (9,9))
axl = fig.add_subplot(111)
axl.set_xticks([])
axl.set_yticks([])
cv2.imwrite(os.path.join('./facial only' , str(i) + '.jpg'),face_crop)
i+=1
cap.release()
cv2.destroyAllWindows()
image_dimensions = {'height':256, 'width':256, 'channels':3}
class Classifier:
def __init__(self):
self.model = 0
def predict(self, x):
return self.model.predict(x)
def fit(self, x, y):
return self.model.train_on_batch(x, y)
def get_accuracy(self, x, y):
return self.model.test_on_batch(x, y)
def load(self, path):
self.model.load_weights(path)
class Meso4(Classifier):
def __init__(self,learning_rate = 0.001):
self.model = self.init_model()
optimizer = Adam(lr = learning_rate)
self.model.compile(optimizer = optimizer,
loss = 'mean squared error',
metrics = ['accuracy'])
def init_model(self):
x = Input(shape=(image_dimensions['height'],
image_dimensions['width'],
image_dimensions['channels']))
x1 = Conv2D(8, (3, 3), padding='same', activation = 'relu')(x)
x1 = BatchNormalization()(x1)
x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)
x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1)
x2 = BatchNormalization()(x2)
x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2)
x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2)
x3 = BatchNormalization()(x3)
x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)
x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)
x4 = BatchNormalization()(x4)
x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)
y = Flatten()(x4)
y = Dropout(0.5)(y)
y = Dense(16)(y)
y = LeakyReLU(alpha=0.1)(y)
y = Dropout(0.5)(y)
y = Dense(1, activation = 'sigmoid')(y)
return Model(inputs = x, outputs = y)
meso = Meso4()
meso.load('C:/Users/saaransh/Downloads/Meso4_DF')
dataGenerator = ImageDataGenerator(rescale=1./255)
# Instantiating generator to feed images through the network
generator = dataGenerator.flow_from_directory(
'./',
target_size=(256, 256),
batch_size=1,
class_mode='binary')
# Rendering image X with label y for MesoNet
X, y = generator.next()
# Evaluating prediction
print(f"Predicted likelihood: {meso.predict(X)[0][0]:.4f}")
print(f"Actual label: {int(y[0])}")
print(f"\nCorrect prediction: {round(meso.predict(X)[0][0])==y[0]}")
# Showing image
plt.imshow(np.squeeze(X));
#The model predicts the probability of Deepfakes, so the more closer it is to zero the more likely it is a deepfake and viceversa