MobileNet.py

#!/usr/bin/env python
# coding: utf-8

# In[40]:


import sys
import os
import math

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#get_ipython().run_line_magic('matplotlib', 'inline')
import seaborn as sns

# uncomment the following line if 'imutils' is not installed in your python kernel
# !{sys.executable} -m pip install imutils
import imutils
from imutils import paths


import keras
from keras.layers import Dense, Conv2D, BatchNormalization, Dropout, Activation, MaxPooling2D, Flatten
from keras.models import Sequential, load_model
from keras.losses import categorical_crossentropy, binary_crossentropy
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.regularizers import l2
from keras import regularizers
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array
from keras.applications.mobilenet_v2 import preprocess_input
from keras.preprocessing.image import load_img
from keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer

import cv2
import time
import random
import shutil


# In[42]:


data_path = 'dataset'
mask_path = 'dataset/with_mask'
nomask_path = 'dataset/without_mask'
test_path = 'test'
train_path = 'train'


# In[43]:


def view(path):
    images = list()
    for img in random.sample(os.listdir(path),9):
        images.append(img)
    i = 0
    fig,ax = plt.subplots(nrows=3, ncols=3, figsize=(20,10))
    for row in range(3):
        for col in range(3):
            ax[row,col].imshow(cv2.imread(os.path.join(path,images[i])))
            i+=1


# In[44]:


view(mask_path)


# In[45]:


view(nomask_path)


# In[46]:


INIT_LR = 1e-4
EPOCHS = 20
BS = 32


# In[47]:


train_datagen = ImageDataGenerator(
        rescale=1./255,
        rotation_range=20,
        shear_range=0.2,
        zoom_range=0.2,
        width_shift_range=0.2,
        height_shift_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)


training_set = train_datagen.flow_from_directory(
        'train',
        target_size=(img_size,img_size),
        batch_size=batch_size,
        class_mode='binary')


test_set = test_datagen.flow_from_directory(
        'test',
        target_size=(img_size,img_size),
        batch_size=batch_size,
        class_mode='binary')


# In[48]:


from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import os


# In[49]:


baseModel = MobileNetV2(weights="imagenet", include_top=False, input_tensor=Input(shape=(224, 224, 3)))


# In[80]:


headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(128, activation="relu")(headModel)
headModel = Dropout(0.3)(headModel)
headModel = Dense(1, activation="sigmoid")(headModel)


# In[81]:


model = Model(inputs=baseModel.input, outputs=headModel)


# In[82]:


for layer in baseModel.layers:
    layer.trainable = False


# In[83]:


model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])


# In[84]:


history=model.fit(training_set, steps_per_epoch=34, validation_data=test_set, validation_steps=6, epochs=20)


# In[85]:


plt.plot(history.history['loss'],'r',label='Training Loss')
plt.plot(history.history['val_loss'],label='Validation Loss')
plt.xlabel('No. of Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

# Plotting the accuracy on validation set w.r.t the number of epochs
plt.plot(history.history['accuracy'],'r',label='Training Accuracy')
plt.plot(history.history['val_accuracy'],label='Validation Accuracy')
plt.xlabel('No. of Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()


# In[86]:


model.save('mobilenetmodel1.h5', history)


# In[2]:


get_ipython().system('pip install dlib')


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