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import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
#data directories
DATADIR = "your data dir"
CATEGORIES = ["sfw", "nsfw"]
for category in CATEGORIES:
path = os.path.join(DATADIR, category) #path to sfw and nfsw dir
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
plt.imshow(img_array, cmap ="gray")
plt.show()
break
break
# In[2]:
print (img_array).shape
# In[3]:
IMG_SIZE = 80
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
plt.imshow(new_array, cmap = 'gray')
plt.show()
# In[4]:
training_data = []
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR, category) #path to sfw and nsfw
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
create_training_data()
# In[5]:
print(len(training_data))
# In[6]:
import random
random.shuffle(training_data)
# In[7]:
for sample in training_data[:10]:
print(sample[1])
# In[8]:
X = []
y = []
# In[9]:
for features, label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
# In[10]:
import pickle
pickle_out = open("X.pickle", "wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle", "wb")
pickle.dump(y, pickle_out)
pickle_out.close()
# In[11]:
pickle_in = open("X.pickle", "rb")
X = pickle.load(pickle_in)
# In[13]:
X[1]
# In[14]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
X = pickle.load(open("X.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))
X = X/255.0
y = np.array(y)
model = Sequential()
model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=['accuracy'])
model.fit(X, y, batch_size=8, epochs=8, validation_split=0.1)
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