COVID-19: Face Mask Detector

Created by Cainvas Scholar-

1. Ritik Bompilwar ( GitHub | LinkedIn )

Photo by Syril Bobadilla on Dribbble

Face Mask Detector

Face Mask Detection system built with OpenCV, TensorFlow using Deep Learning and Computer Vision to detect face masks in real-time video streams.

Full Project is here

Two-phase COVID-19 face mask detector-

Dataset : Face Mask Detection Data from Kaggle.

Refrence : COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning

Download the dataset

!wget -N "https://cainvas-static.s3.amazonaws.com/media/user_data/cainvas-admin/face-mask-detection.zip"
!unzip -q -o "face-mask-detection.zip" -d "face-mask-detection-data"
!rm "face-mask-detection.zip"

--2021-09-08 11:29:17--  https://cainvas-static.s3.amazonaws.com/media/user_data/cainvas-admin/face-mask-detection.zip
Resolving cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)... 52.219.66.16
Connecting to cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)|52.219.66.16|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 133028516 (127M) [application/zip]
Saving to: ‘face-mask-detection.zip’

face-mask-detection 100%[===================>] 126.87M  99.5MB/s    in 1.3s    

2021-09-08 11:29:18 (99.5 MB/s) - ‘face-mask-detection.zip’ saved [133028516/133028516]

Importing all the dependencies and required libraries.

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.models import load_model
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.video import VideoStream
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import os
import cv2
import time
import imutils

Loading and labelling the images in the Dataset.

# initialize the initial learning rate, number of epochs to train for,
# and batch size
INIT_LR = 1e-4
EPOCHS = 20
BS = 32

# grab the list of images in our dataset directory, then initialize
# the list of data (i.e., images) and class images

imagePaths = list(paths.list_images('face-mask-detection-data'))
data = []
labels = []

# loop over the image paths
for imagePath in imagePaths:
	# extract the class label from the filename
	label = imagePath.split(os.path.sep)[-2]

	# load the input image (224x224) and preprocess it
	image = load_img(imagePath, target_size=(224, 224))
	image = img_to_array(image)
	image = preprocess_input(image)

	# update the data and labels lists, respectively
	data.append(image)
	labels.append(label)
print("No. of images loaded: {}".format(len(data)))

/opt/tljh/user/lib/python3.7/site-packages/PIL/Image.py:952: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  "Palette images with Transparency expressed in bytes should be "

No. of images loaded: 3833

# convert the data and labels to NumPy arrays
data = np.array(data, dtype="float32")
labels = np.array(labels)

# perform one-hot encoding on the labels
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)

Preparing the inputs for the model

# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels,
	test_size=0.20, stratify=labels, random_state=42)
# construct the training image generator for data augmentation
aug = ImageDataGenerator(
	rotation_range=20,
	zoom_range=0.15,
	width_shift_range=0.2,
	height_shift_range=0.2,
	shear_range=0.15,
	horizontal_flip=True,
	fill_mode="nearest")

Constructing and compiling the Model

# loading the MobileNetV2 network, ensuring the head FC layer sets are left off
baseModel = MobileNetV2(weights="imagenet", include_top=False,
	input_tensor=Input(shape=(224, 224, 3)))

# constructing the head of the model that will be placed on top of the base model
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(128, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation="softmax")(headModel)

# placing the head FC model on top of the base model (this will become the actual model we will train)
model = Model(inputs=baseModel.input, outputs=headModel)

# loop over all layers in the base model and freeze them so they will not be updated during the first training process
for layer in baseModel.layers:
	layer.trainable = False

# compiling our model

opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt,
	metrics=["accuracy"])

model.summary()

WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5
9412608/9406464 [==============================] - 0s 0us/step
Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  
__________________________________________________________________________________________________
bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      
__________________________________________________________________________________________________
Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   
__________________________________________________________________________________________________
expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 
__________________________________________________________________________________________________
expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    
__________________________________________________________________________________________________
expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] 
__________________________________________________________________________________________________
expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0
__________________________________________________________________________________________________
expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      
__________________________________________________________________________________________________
block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   
__________________________________________________________________________________________________
block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             
__________________________________________________________________________________________________
block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          
__________________________________________________________________________________________________
block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        
__________________________________________________________________________________________________
block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                
__________________________________________________________________________________________________
block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          
__________________________________________________________________________________________________
block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            
__________________________________________________________________________________________________
block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         
__________________________________________________________________________________________________
block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             
__________________________________________________________________________________________________
block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          
__________________________________________________________________________________________________
block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        
__________________________________________________________________________________________________
block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          
__________________________________________________________________________________________________
block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            
__________________________________________________________________________________________________
block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         
                                                                 block_2_project_BN[0][0]         
__________________________________________________________________________________________________
block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                
__________________________________________________________________________________________________
block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             
__________________________________________________________________________________________________
block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          
__________________________________________________________________________________________________
block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        
__________________________________________________________________________________________________
block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                
__________________________________________________________________________________________________
block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          
__________________________________________________________________________________________________
block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            
__________________________________________________________________________________________________
block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         
__________________________________________________________________________________________________
block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             
__________________________________________________________________________________________________
block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          
__________________________________________________________________________________________________
block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        
__________________________________________________________________________________________________
block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          
__________________________________________________________________________________________________
block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            
__________________________________________________________________________________________________
block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         
                                                                 block_4_project_BN[0][0]         
__________________________________________________________________________________________________
block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                
__________________________________________________________________________________________________
block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             
__________________________________________________________________________________________________
block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          
__________________________________________________________________________________________________
block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        
__________________________________________________________________________________________________
block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          
__________________________________________________________________________________________________
block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            
__________________________________________________________________________________________________
block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                
                                                                 block_5_project_BN[0][0]         
__________________________________________________________________________________________________
block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                
__________________________________________________________________________________________________
block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             
__________________________________________________________________________________________________
block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          
__________________________________________________________________________________________________
block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        
__________________________________________________________________________________________________
block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                
__________________________________________________________________________________________________
block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          
__________________________________________________________________________________________________
block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            
__________________________________________________________________________________________________
block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         
__________________________________________________________________________________________________
block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             
__________________________________________________________________________________________________
block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          
__________________________________________________________________________________________________
block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        
__________________________________________________________________________________________________
block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          
__________________________________________________________________________________________________
block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            
__________________________________________________________________________________________________
block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         
                                                                 block_7_project_BN[0][0]         
__________________________________________________________________________________________________
block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                
__________________________________________________________________________________________________
block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             
__________________________________________________________________________________________________
block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          
__________________________________________________________________________________________________
block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        
__________________________________________________________________________________________________
block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          
__________________________________________________________________________________________________
block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            
__________________________________________________________________________________________________
block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                
                                                                 block_8_project_BN[0][0]         
__________________________________________________________________________________________________
block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                
__________________________________________________________________________________________________
block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             
__________________________________________________________________________________________________
block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          
__________________________________________________________________________________________________
block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        
__________________________________________________________________________________________________
block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          
__________________________________________________________________________________________________
block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            
__________________________________________________________________________________________________
block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                
                                                                 block_9_project_BN[0][0]         
__________________________________________________________________________________________________
block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                
__________________________________________________________________________________________________
block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            
__________________________________________________________________________________________________
block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         
__________________________________________________________________________________________________
block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       
__________________________________________________________________________________________________
block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         
__________________________________________________________________________________________________
block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           
__________________________________________________________________________________________________
block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        
__________________________________________________________________________________________________
block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            
__________________________________________________________________________________________________
block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         
__________________________________________________________________________________________________
block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       
__________________________________________________________________________________________________
block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         
__________________________________________________________________________________________________
block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           
__________________________________________________________________________________________________
block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        
                                                                 block_11_project_BN[0][0]        
__________________________________________________________________________________________________
block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               
__________________________________________________________________________________________________
block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            
__________________________________________________________________________________________________
block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         
__________________________________________________________________________________________________
block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       
__________________________________________________________________________________________________
block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         
__________________________________________________________________________________________________
block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           
__________________________________________________________________________________________________
block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               
                                                                 block_12_project_BN[0][0]        
__________________________________________________________________________________________________
block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               
__________________________________________________________________________________________________
block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            
__________________________________________________________________________________________________
block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         
__________________________________________________________________________________________________
block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       
__________________________________________________________________________________________________
block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               
__________________________________________________________________________________________________
block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         
__________________________________________________________________________________________________
block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           
__________________________________________________________________________________________________
block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        
__________________________________________________________________________________________________
block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            
__________________________________________________________________________________________________
block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         
__________________________________________________________________________________________________
block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       
__________________________________________________________________________________________________
block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         
__________________________________________________________________________________________________
block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           
__________________________________________________________________________________________________
block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        
                                                                 block_14_project_BN[0][0]        
__________________________________________________________________________________________________
block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               
__________________________________________________________________________________________________
block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            
__________________________________________________________________________________________________
block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         
__________________________________________________________________________________________________
block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       
__________________________________________________________________________________________________
block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         
__________________________________________________________________________________________________
block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           
__________________________________________________________________________________________________
block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               
                                                                 block_15_project_BN[0][0]        
__________________________________________________________________________________________________
block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               
__________________________________________________________________________________________________
block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            
__________________________________________________________________________________________________
block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         
__________________________________________________________________________________________________
block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       
__________________________________________________________________________________________________
block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         
__________________________________________________________________________________________________
block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           
__________________________________________________________________________________________________
Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        
__________________________________________________________________________________________________
Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     
__________________________________________________________________________________________________
out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 1, 1, 1280)   0           out_relu[0][0]                   
__________________________________________________________________________________________________
flatten (Flatten)               (None, 1280)         0           average_pooling2d[0][0]          
__________________________________________________________________________________________________
dense (Dense)                   (None, 128)          163968      flatten[0][0]                    
__________________________________________________________________________________________________
dropout (Dropout)               (None, 128)          0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 2)            258         dropout[0][0]                    
==================================================================================================
Total params: 2,422,210
Trainable params: 164,226
Non-trainable params: 2,257,984
__________________________________________________________________________________________________

Training the Model

# training the head of the network

H = model.fit(
	aug.flow(trainX, trainY, batch_size=BS),
	steps_per_epoch=len(trainX) // BS,
	validation_data=(testX, testY),
	validation_steps=len(testX) // BS,
	epochs=EPOCHS)

Epoch 1/20

Predicting masked and unmasked images using the trained Model.

# making predictions on the testing set

predIdxs = model.predict(testX, batch_size=BS)

# for each image in the testing set we need to find the index of the label with corresponding largest predicted probability
predIdxs = np.argmax(predIdxs, axis=1)

# Creating a classification report
print(classification_report(testY.argmax(axis=1), predIdxs,
	target_names=lb.classes_))

Plotting the training loss and accuracy

N = EPOCHS
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["accuracy"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_accuracy"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower right")

Save model

model.save('face-mask-detector')

Compile using deepC

!deepCC face-mask-detector --format=tensorflow

Using Realtime Tracking

Click below to see the full video

Defining face detection/mask prediction function

def detect_and_predict_mask(frame, faceNet, maskNet):
	# grab the dimensions of the frame and then construct a blob
	# from it
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

	# pass the blob through the network and obtain the face detections
	faceNet.setInput(blob)
	detections = faceNet.forward()

	# initialize our list of faces, their corresponding locations,
	# and the list of predictions from our face mask network
	faces = []
	locs = []
	preds = []

	# loop over the detections
	for i in range(0, detections.shape[2]):
		# extract the confidence (i.e., probability) associated with
		# the detection
		confidence = detections[0, 0, i, 2]

		# filter out weak detections by ensuring the confidence is
		# greater than the minimum confidence
		if confidence > args["confidence"]:
			# compute the (x, y)-coordinates of the bounding box for
			# the object
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

			# ensure the bounding boxes fall within the dimensions of
			# the frame
			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))

			# extract the face ROI, convert it from BGR to RGB channel
			# ordering, resize it to 224x224, and preprocess it
			face = frame[startY:endY, startX:endX]
			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
			face = cv2.resize(face, (224, 224))
			face = img_to_array(face)
			face = preprocess_input(face)

			# add the face and bounding boxes to their respective
			# lists
			faces.append(face)
			locs.append((startX, startY, endX, endY))

	# only make a predictions if at least one face was detected
	if len(faces) > 0:
		# for faster inference we'll make batch predictions on *all*
		# faces at the same time rather than one-by-one predictions
		# in the above `for` loop
		faces = np.array(faces, dtype="float32")
		preds = maskNet.predict(faces, batch_size=32)

	# return a 2-tuple of the face locations and their corresponding
	# locations
	return (locs, preds)

Constructing the argument parser and parsing the arguments

args = {}
args["face"] = "face_detector"
args["model"] = "mask_detector.model"
args["confidence"] = 0.4

Loading the Face Detector Model

prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
	"res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

Loading the saved Facemask Detector Model

# load the face mask detector model from disk

maskNet = load_model(args["model"])

Initializing the webcam video stream and detecting mask

print("Starting video stream")

vs = VideoStream(src=0).start()
time.sleep(2.0)

# looping over the frames from the video stream
while True:
	# grab the frame from the threaded video stream and resize it to have a maximum width of 800 pixels
	frame = vs.read()
	frame = imutils.resize(frame, width=800)

	# detect faces in the frame and determine if they are wearing a face mask or not
	(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)

	# loop over the detected face locations and their corresponding locations
	for (box, pred) in zip(locs, preds):
		# unpack the bounding box and predictions
		(startX, startY, endX, endY) = box
		(mask, withoutMask) = pred

		# determine the class label and color we'll use to draw the bounding box and text
		label = "Mask" if mask > withoutMask else "No Mask"
		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

		# include the probability in the label
		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

		# display the label and bounding box rectangle on the output frame
		cv2.putText(frame, label, (startX, startY - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

	# showing the output frame
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF

	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break


cv2.destroyAllWindows()
vs.stop()