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face_mask_detector.py
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face_mask_detector.py
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import os
import tensorflow as tf
import shutil
import random
import cv2
import numpy as np
import matplotlib.pyplot as plt
from os import listdir
from shutil import copyfile
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import ModelCheckpoint
from sklearn.utils import shuffle
print("The number of images with facemask:",len(os.listdir('data/with_mask')))
print("The number of images without facemask:",len(os.listdir('data/without_mask')))
IMG_HEIGHT = 150
IMG_WIDTH = 150
def train_test_split(source, trainPath, testPath, split_size):
dataset = []
for crnImage in os.listdir(source):
data = source + '/' + crnImage
if(os.path.getsize(data) > 0):
dataset.append(crnImage)
train_len = int(len(dataset) * split_size)
test_len = int(len(dataset) - train_len)
shuffled = random.sample(dataset, len(dataset))
train = dataset[0:train_len]
test = dataset[train_len:len(dataset)]
print("train images with mask:",len(train))
print("test images without mask:",len(test))
#copying train and test images in seaparate directories
for trainDataPoint in train:
crnTrainDataPath = source + '/' + trainDataPoint
newTrainDataPath = trainPath + '/' + trainDataPoint
copyfile(crnTrainDataPath, newTrainDataPath)
for testDataPoint in test:
crnTestDataPath = source + '/' + testDataPoint
newTestDataPath = testPath + '/' + testDataPoint
copyfile(crnTestDataPath, newTestDataPath)
model = tf.keras.models.Sequential([
Conv2D(32, 3, activation='relu', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)),
MaxPooling2D(2,2),
Conv2D(64, 3, activation='relu'),
MaxPooling2D(2,2),
Conv2D(128, 3, padding='same', activation='relu'),
MaxPooling2D(2,2),
Flatten(),
Dropout(0.3),
Dense(256, activation='relu'),
Dense(2, activation='softmax') # dense layer has a shape of 2 as we have only 2 classes
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
acc = 0
val_loss = 0
val_acc = 0
loss = 0
val_loss = 0
epochs = 30
def trainModel():
training_dir = "data/train"
train_datagen = ImageDataGenerator(rescale=1.0/255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
train_generator = train_datagen.flow_from_directory(training_dir,
batch_size=10,
target_size=(IMG_WIDTH, IMG_HEIGHT))
validation_dir = "data/test"
validation_datagen = ImageDataGenerator(rescale=1.0/255)
validation_generator = validation_datagen.flow_from_directory(validation_dir,
batch_size=10,
target_size=(IMG_WIDTH, IMG_HEIGHT))
checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')
history = model.fit_generator(train_generator,
epochs=epochs,
validation_data=validation_generator,
callbacks=[checkpoint])
global acc
acc = history.history['accuracy']
global val_acc
val_acc = history.history['val_accuracy']
global loss
loss = history.history['loss']
global val_loss
val_loss = history.history['val_loss']
def plot():
epochs_range = range(epochs)
plt.figure(figsize=(6, 6))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
train_test_split('data/with_mask', 'data/train/training_with_mask', 'data/test/test_with_mask',0.8)
train_test_split('data/without_mask','data/train/training_without_mask', 'data/test/test_without_mask',0.8)
trainModel()
plot()
# Initializing webcam to live preview face mask detection ROIs on faces
# Seen this in this repo https://github.com/mk-gurucharan/Face-Mask-Detection
face_clsfr = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
labels_dict = {0:'without mask',1:'with mask'}
color_dict = {0:(0,0,255),1:(0,255,0)}
size = 4
webcam = cv2.VideoCapture(0)
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while True:
(rval, im) = webcam.read()
im = cv2.flip(im,1,1) #Flip to act as a mirror
# Resize the image to speed up detection
mini = cv2.resize(im, (im.shape[1] // size, im.shape[0] // size))
faces = classifier.detectMultiScale(mini)
# Draw rectangles around each face
for (x, y, w, h) in faces:
face_img = im[y:(y+h)*size, x:(x+w) * size]
resized=cv2.resize(face_img,(IMG_WIDTH, IMG_HEIGHT))
normalized = resized / 255.0
reshaped = np.reshape(normalized,(1,IMG_WIDTH,IMG_HEIGHT,3))
reshaped = np.vstack([reshaped])
result = model.predict(reshaped)
label = np.argmax(result,axis=1)[0]
cv2.rectangle(im,(x* size,y* size),((x+w)* size,(y+h)* size),color_dict[label],2)
cv2.rectangle(im,(x * size,(y* size)-40),((x+w)* size,y* size),color_dict[label],-1)
cv2.putText(im, labels_dict[label], (x* size + 10, (y* size)-10),cv2.FONT_HERSHEY_DUPLEX,0.8,(255,255,255),2)
cv2.imshow('Mask Detection', im)
key = cv2.waitKey(10)
if key == 27:
break
webcam.release()
cv2.destroyAllWindows()