Face Mask detection

/ Computer Vision / By aicodeschool

text to be added

import tensorflow as tf  ## pip install tensorflow-gpu
import cv2 ###  pip install opencv-python
 ## pip install opencv-contrib-python   fullpackage 
import os
import matplotlib.pyplot as plt  ## pip install matlplotlib
import numpy as np ## pip install numpy    
img_array = cv2.imread("Dataset/Face_Mask/00000_Mask.jpg")
plt.imshow(img_array) ## BGR
<matplotlib.image.AxesImage at 0x1b592d6bf08>

plt.imshow(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))
<matplotlib.image.AxesImage at 0x1b59322e4c8>

img_array.shape
(1024, 1024, 3)
Datadirectory = "Dataset/"  ## training dataset
Classes = ["Face_Mask","No_Mask"] ## list of classes
for category in Classes:
    path = os.path.join(Datadirectory, category)  ## //
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path,img))
        #backtorgb = cv2.cvtColor(img_array,cv2.COLOR_GRAY2RGB)
        plt.imshow(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))
        plt.show()
        break
    break

img_size= 224 ## ImageNet => 224 x 224

new_array= cv2.resize(img_array, (img_size,img_size))
plt.imshow(cv2.cvtColor(new_array, cv2.COLOR_BGR2RGB))
plt.show()

Reading the images and converting all to array

training_Data = [] ## data

def create_training_Data():
    for category in Classes:
        path = os.path.join(Datadirectory, category)
        class_num = Classes.index(category) ## 0 1,  ## Label
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path,img))
                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()
print(len(training_Data))
1951

import random

random.shuffle(training_Data)

X = []  ## data /feature
y = []  ## label

for features,label in training_Data:
    X.append(features)
    y.append(label)


X = np.array(X).reshape(-1, img_size, img_size, 3) 
X.shape
(1951, 224, 224, 3)

# normalize the data
X= X/255.0; ## we are normalizing it  
y[1000]
1
Y= np.array(y)

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()

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)

pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)

Deep learning model for training - Transfer Learning

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
model = tf.keras.applications.mobilenet.MobileNet() ## pre-trained model
model.summary()

Transfer Learning - Tuning , weights will start from last check point

base_input = model.layers[0].input
base_output= model.layers[-4].output
Flat_layer=  layers.Flatten()(base_output)
final_output = layers.Dense(1)(Flat_layer) ## 0 ,1
final_ouput = layers.Activation('sigmoid')(final_output)
new_model = keras.Model(inputs = base_input, outputs= final_output)
new_model.summary()

settings for binary classification (Face mask/without mask)

new_model.compile(loss="binary_crossentropy", optimizer = "adam", metrics = ["accuracy"])

new_model.fit(X,Y, epochs = 1,validation_split = 0.1)

new_model.save('my_model3.h5') 
new_model = tf.keras.models.load_model('my_model.h5')

Checking the network for Predictions

frame = cv2.imread('00002_Mask.jpg')
plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

final_image =cv2.resize(frame, (224,224))
final_image =  np.expand_dims(final_image,axis =0)  ## need fourth dimension  
final_image=final_image/255.0
Predictions =new_model.predict(final_image)
Predictions
array([[-55.494904]], dtype=float32)

Checking the network for unknown images

frame = cv2.imread('sad_women.jpg')
frame.shape
plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray.shape
(480, 852)
faces = faceCascade.detectMultiScale(gray,1.1,4)
for x,y,w,h in faces:
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = frame[y:y+h, x:x+w]
    cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
    facess = faceCascade.detectMultiScale(roi_gray)   
    if len(facess) == 0:
        print("Face  not detected")
    else:
        for (ex,ey,ew,eh) in facess:
            face_roi = roi_color[ey: ey+eh, ex:ex + ew]
            plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

plt.imshow(cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB))

final_image =cv2.resize(face_roi, (224,224))
final_image =  np.expand_dims(final_image,axis =0)  ## need fourth dimension  
final_image=final_image/255.0
Predictions =new_model.predict(final_image)
Predictions

array([[38.855743]], dtype=float32)

Realtime Video Demo Face mask Detection

import cv2 ###  pip install opencv-python
 ## pip install opencv-contrib-python   fullpackage 
#from deepface import DeepFace  ## pip install deepface
path = "haarcascade_frontalface_default.xml"
font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

# set the rectangle background to white
rectangle_bgr = (255, 255, 255)
# make a black image
img = np.zeros((500, 500))
# set some text
text = "Some text in a box!"
# get the width and height of the text box
(text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]
# set the text start position
text_offset_x = 10
text_offset_y = img.shape[0] - 25
# make the coords of the box with a small padding of two pixels
box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height - 2))
cv2.rectangle(img, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)
cv2.putText(img, text, (text_offset_x, text_offset_y), font, fontScale=font_scale, color=(0, 0, 0), thickness=1)

cap = cv2.VideoCapture(1)
# Check if the webcam is opened correctly
if not cap.isOpened():
    cap = cv2.VideoCapture(0)
if not cap.isOpened():
    raise IOError("Cannot open webcam")

while True:
    ret,frame = cap.read()
    #eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_eye.xml')
    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    #print(faceCascade.empty())
    faces = faceCascade.detectMultiScale(gray,1.1,4)
    for x,y,w,h in faces:
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        facess = faceCascade.detectMultiScale(roi_gray)   
        if len(facess) == 0:
            print("Face  not detected")
        else:
            for (ex,ey,ew,eh) in facess:
                face_roi = roi_color[ey: ey+eh, ex:ex + ew] ## cropping the face
    
    final_image =cv2.resize(face_roi, (224,224))
    final_image =  np.expand_dims(final_image,axis =0)  ## need fourth dimension  
    final_image=final_image/255.0
    
    font = cv2.FONT_HERSHEY_SIMPLEX 
    
    Predictions = new_model.predict(final_image)
    
    font_scale = 1.5
    font = cv2.FONT_HERSHEY_PLAIN

 
    if (Predictions>0):
        status = "No Mask"
        
        x1,y1,w1,h1 = 0,0,175,75
        # Draw black background rectangle
        cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
        # Add text
        cv2.putText(frame, status, (x1 + int(w1/10),y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
        
        cv2.putText(frame,status,(100, 150),font, 3,(0, 0, 255),2,cv2.LINE_4)
       
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255))
    else:
        status = "Face Mask"
        
        x1,y1,w1,h1 = 0,0,175,75
        # Draw black background rectangle
        cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
        # Add text
        cv2.putText(frame, status, (x1 + int(w1/10),y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,255,0), 2)
        cv2.putText(frame,status,(100, 150),font, 3,(0, 255,0),2,cv2.LINE_4)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255,0))
    
    #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    #print(faceCascade.empty())
    #faces = faceCascade.detectMultiScale(gray,1.1,4)

    # Draw a rectangle around the faces
    #for(x, y, w, h) in faces:
      #  cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
   
    
  
    # Use putText() method for 
    # inserting text on video 
  
    cv2.imshow('Face Mask Detection Tutorial',frame)

    if cv2.waitKey(2) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Lets add some beeps

import winsound
frequency = 2500  # Set Frequency To 2500 Hertz
duration = 1000  # Set Duration To 1000 ms == 1 second

import cv2 ###  pip install opencv-python
 ## pip install opencv-contrib-python   fullpackage 
#from deepface import DeepFace  ## pip install deepface
path = "haarcascade_frontalface_default.xml"
font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

# set the rectangle background to white
rectangle_bgr = (255, 255, 255)
# make a black image
img = np.zeros((500, 500))
# set some text
text = "Some text in a box!"
# get the width and height of the text box
(text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]
# set the text start position
text_offset_x = 10
text_offset_y = img.shape[0] - 25
# make the coords of the box with a small padding of two pixels
box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height - 2))
cv2.rectangle(img, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)
cv2.putText(img, text, (text_offset_x, text_offset_y), font, fontScale=font_scale, color=(0, 0, 0), thickness=1)

cap = cv2.VideoCapture(1)
# Check if the webcam is opened correctly
if not cap.isOpened():
    cap = cv2.VideoCapture(0)
if not cap.isOpened():
    raise IOError("Cannot open webcam")

while True:
    ret,frame = cap.read()
    #eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_eye.xml')
    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    #print(faceCascade.empty())
    faces = faceCascade.detectMultiScale(gray,1.1,4)
    for x,y,w,h in faces:
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        facess = faceCascade.detectMultiScale(roi_gray)   
        if len(facess) == 0:
            print("Face  not detected")
        else:
            for (ex,ey,ew,eh) in facess:
                face_roi = roi_color[ey: ey+eh, ex:ex + ew] ## cropping the face
    
    final_image =cv2.resize(face_roi, (224,224))
    final_image =  np.expand_dims(final_image,axis =0)  ## need fourth dimension  
    final_image=final_image/255.0
    
    font = cv2.FONT_HERSHEY_SIMPLEX 
    
    Predictions = new_model.predict(final_image)
    
    font_scale = 1.5
    font = cv2.FONT_HERSHEY_PLAIN

 
    if (Predictions>0):
        status = "No Mask"
        
        x1,y1,w1,h1 = 0,0,175,75
        # Draw black background rectangle
        cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
        # Add text
        cv2.putText(frame, status, (x1 + int(w1/10),y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
        
        cv2.putText(frame,status,(100, 150),font, 3,(0, 0, 255),2,cv2.LINE_4)
       
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255))
        winsound.Beep(frequency, duration)
    else:
        status = "Face Mask"
        
        x1,y1,w1,h1 = 0,0,175,75
        # Draw black background rectangle
        cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
        # Add text
        cv2.putText(frame, status, (x1 + int(w1/10),y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,255,0), 2)
        cv2.putText(frame,status,(100, 150),font, 3,(0, 255,0),2,cv2.LINE_4)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255,0))
    
    #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    #print(faceCascade.empty())
    #faces = faceCascade.detectMultiScale(gray,1.1,4)

    # Draw a rectangle around the faces
    #for(x, y, w, h) in faces:
      #  cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
   
    
  
    # Use putText() method for 
    # inserting text on video 
  
    cv2.imshow('Face Mask Detection Tutorial',frame)

    if cv2.waitKey(2) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()