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SPD_version1.py
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SPD_version1.py
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#!/usr/bin/env python
# coding: utf-8
# In[19]:
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import RMSprop
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import cv2
import os
# In[20]:
train = ImageDataGenerator(rescale = 1/255)
# In[21]:
#specifiying the training and validation dataset path
train_dataset = train.flow_from_directory('D:/Projects/SPD/Datas/training/',
target_size = (200,200),
batch_size = 3,
class_mode = 'binary')
validation_dataset = train.flow_from_directory('D:/Projects/SPD/Datas/validation/',
target_size = (200,200),
batch_size = 3,
class_mode = 'binary')
# In[22]:
train_dataset.class_indices
# In[23]:
train_dataset.classes
# In[24]:
validation_dataset.classes
# In[25]:
#three layered Neural Network
model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(16,(3,3), activation = 'relu', input_shape = (200,200,3)),
tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Conv2D(32,(3,3), activation = 'relu'),
tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Conv2D(64,(3,3), activation = 'relu',),
tf.keras.layers.MaxPool2D(2,2),
#
#tf.keras.layers.Conv2D(128,(3,3), activation = 'relu',),
#tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Flatten(),
##
tf.keras.layers.Dense(512, activation = tf.nn.relu),
##
tf.keras.layers.Dense(1, activation = tf.nn.sigmoid)
])
# In[26]:
model.compile(loss = 'binary_crossentropy',
optimizer = RMSprop(learning_rate = 0.001),
metrics = ['accuracy'])
# In[27]:
#training the model with low epochs
model_fit = model.fit(train_dataset,
steps_per_epoch = 5,
epochs = 10,
validation_data = validation_dataset)
# In[28]:
url = 'http://192.168.1.79:8080/video'
cam = cv2.VideoCapture(url)
cv2.namedWindow("test")
img_counter = 0
while True:
ret, frame = cam.read()
resized = cv2.resize(frame, (800, 600))
if not ret:
#print("failed to grab frame")
break
cv2.imshow("test", resized)
k = cv2.waitKey(1)
if k%256 == 27:
# ESC pressed
#print("Escape hit, closing...")
break
elif k%256 == 32:
# SPACE pressed
img_name = "frame.jpg".format(img_counter)
cv2.imwrite(img_name, resized)
print("Image Taken!")
img = image.load_img('frame.jpg', target_size = (200,200))
plt.imshow(img)
x = image.img_to_array(img)
x = np.expand_dims(x,axis = 0)
images = np.vstack([x])
value = model.predict(images)
if value == 1:
print("Nude")
else:
print("Not Nude")
img_counter += 1
cam.release()
cv2.destroyAllWindows()
# In[18]:
#saving the model
model.save('D:/Projects/SPD/spd.xml')
# In[ ]: