IntermediateDeep LearningFull notebook

Face Mask Detection using MobileNet Transfer Learning

Notebook-based face mask detection tutorial using TensorFlow, OpenCV, MobileNet transfer learning, face detection and realtime video logic.

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This download currently contains the uploaded notebook Final_FaceMask_during_Video.ipynb. Replace it later with your final cleaned code ZIP if required.

Face Mask Detection using MobileNet Transfer Learning

Overview

This tutorial is based on your uploaded Final_FaceMask_during_Video.ipynb notebook. It includes dataset loading, preprocessing, MobileNet transfer learning, prediction on still images, face ROI extraction and realtime webcam/video mask-detection logic. All notebook code cells and available output screenshots are placed into this tutorial template so you can refine the explanations later.

Editing tip: This page is controlled from data/tutorials.ts. Change the title, text, images, Python code, YouTube link, and download link there.

What you will learn

Prepare a face mask vs no-mask dataset
Resize and normalize images for MobileNet input
Build a transfer-learning classifier in TensorFlow/Keras
Detect face ROI using OpenCV Haar cascade
Run mask prediction on images and video frames

Suggested tutorial flow

01

Imports and dataset reading

Load TensorFlow, OpenCV, NumPy, Matplotlib, OS and dataset folders.

02

Training data creation

Loop through Face_Mask and No_Mask folders, resize images and build arrays.

03

Transfer learning model

Reuse MobileNet layers and add binary-classification layers.

04

Still-image prediction

Detect face region and run the trained mask classifier.

05

Realtime video demo

Apply the same logic frame-by-frame using webcam/video input.

Text explanation template

Notebook-based tutorial material

This tutorial is based on your uploaded Final_FaceMask_during_Video.ipynb notebook. It includes dataset loading, preprocessing, MobileNet transfer learning, prediction on still images, face ROI extraction and realtime webcam/video mask-detection logic. All notebook code cells and available output screenshots are placed into this tutorial template so you can refine the explanations later.

How this page is organized

The page shows the full notebook workflow as tutorial sections. Each code cell is rendered as a separate Python code block. If the notebook cell produced an image, graph, screenshot or display output, it is shown under the matching code block and also in the results gallery.

How to edit this later

Open data/tutorials.ts and edit the object with this tutorial slug. Replace resultImages with your final screenshots, update codeBlocks with cleaned code, change videoUrl to the exact YouTube link, and replace the download URL with your final source-code ZIP or GitHub link.

Images, screenshots and results

Add your result screenshots, graphs, model outputs, detected objects, confusion matrices, or interface snapshots here.

face-mask-detection-cnn notebook output cell 2 — Notebook output from cell 2: plt.imshow(img_array) ## BGR

face-mask-detection-cnn notebook output cell 3 — Notebook output from cell 3: plt.imshow(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))

face-mask-detection-cnn notebook output cell 5 — Notebook output from cell 5: Datadirectory = "Dataset/" ## training dataset

face-mask-detection-cnn notebook output cell 6 — Notebook output from cell 6: img_size= 224 ## ImageNet => 224 x 224

face-mask-detection-cnn notebook output cell 36 — Notebook output from cell 36: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

face-mask-detection-cnn notebook output cell 43 — Notebook output from cell 43: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

face-mask-detection-cnn notebook output cell 48 — Notebook output from cell 48: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

face-mask-detection-cnn notebook output cell 49 — Notebook output from cell 49: plt.imshow(cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB))

Python code explanation

Use one or more code blocks. After each block, explain what the code does in simple teaching language.

Notebook cell 0: import tensorflow as tf ## pip install tensorflow-gpu

pythonexample

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

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Notebook cell 1: img_array = cv2.imread("Dataset/Face_Mask/00000_Mask.jpg")

pythonexample

img_array = cv2.imread("Dataset/Face_Mask/00000_Mask.jpg")

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Notebook cell 2: plt.imshow(img_array) ## BGR

pythonexample

plt.imshow(img_array) ## BGR

Notebook output

<matplotlib.image.AxesImage at 0x1b592d6bf08>

face-mask-detection-cnn notebook output cell 2 — Notebook output from cell 2: plt.imshow(img_array) ## BGR

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Notebook cell 3: plt.imshow(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))

pythonexample

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

Notebook output

<matplotlib.image.AxesImage at 0x1b59322e4c8>

face-mask-detection-cnn notebook output cell 3 — Notebook output from cell 3: plt.imshow(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))

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Notebook cell 4: img_array.shape

pythonexample

img_array.shape

Notebook output

(1024, 1024, 3)

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Notebook cell 5: Datadirectory = "Dataset/" ## training dataset

pythonexample

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

face-mask-detection-cnn notebook output cell 5 — Notebook output from cell 5: Datadirectory = "Dataset/" ## training dataset

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Notebook cell 6: img_size= 224 ## ImageNet => 224 x 224

pythonexample

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()

face-mask-detection-cnn notebook output cell 6 — Notebook output from cell 6: img_size= 224 ## ImageNet => 224 x 224

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Notebook cell 8: training_Data = [] ## data

pythonexample

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

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Notebook cell 9: create_training_Data()

pythonexample

create_training_Data()

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Notebook cell 10: print(len(training_Data))

pythonexample

print(len(training_Data))

Notebook output

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Notebook cell 11: import random

pythonexample

import random

random.shuffle(training_Data)

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Notebook cell 12: X = [] ## data /feature

pythonexample

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)

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Notebook cell 13: X.shape

pythonexample

X.shape

Notebook output

(1951, 224, 224, 3)

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Notebook cell 14: # normalize the data

pythonexample

# normalize the data
X= X/255.0; ## we are normalizing it

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Notebook cell 15: y[1000]

pythonexample

y[1000]

Notebook output

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Notebook cell 16: Y= np.array(y)

pythonexample

Y= np.array(y)

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Notebook cell 17: import pickle

pythonexample

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()

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Notebook cell 18: pickle_in = open("X.pickle","rb")

pythonexample

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

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

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Notebook cell 20: import tensorflow as tf

pythonexample

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

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Notebook cell 21: model = tf.keras.applications.mobilenet.MobileNet() ## pre-trained model

pythonexample

model = tf.keras.applications.mobilenet.MobileNet() ## pre-trained model

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Notebook cell 22: model.summary()

pythonexample

model.summary()

Notebook output

Model: "mobilenet_1.00_224"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         
_________________________________________________________________
conv1 (Conv2D)               (None, 112, 112, 32)      864       
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32)      128       
_________________________________________________________________
conv1_relu (ReLU)            (None, 112, 112, 32)      0         
_________________________________________________________________
conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       
_________________________________________________________________
conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       
_________________________________________________________________
conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         
_________________________________________________________________
conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      
_________________________________________________________________
conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       
_________________________________________________________________
conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         
_________________________________________________________________
conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         
_________________________________________________________________
conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       
_________________________________________________________________
conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       
_________________________________________________________________
conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         
_________________________________________________________________
conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      
_________________________________________________________________
conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_pw_2_relu (ReLU)        (
... [output shortened for website readability]

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Notebook cell 24: base_input = model.layers[0].input

pythonexample

base_input = model.layers[0].input

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Notebook cell 25: base_output= model.layers[-4].output

pythonexample

base_output= model.layers[-4].output

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Notebook cell 26: Flat_layer= layers.Flatten()(base_output)

pythonexample

Flat_layer=  layers.Flatten()(base_output)
final_output = layers.Dense(1)(Flat_layer) ## 0 ,1
final_ouput = layers.Activation('sigmoid')(final_output)

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Notebook cell 27: new_model = keras.Model(inputs = base_input, outputs= final_output)

pythonexample

new_model = keras.Model(inputs = base_input, outputs= final_output)

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Notebook cell 28: new_model.summary()

pythonexample

new_model.summary()

Notebook output

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         
_________________________________________________________________
conv1 (Conv2D)               (None, 112, 112, 32)      864       
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32)      128       
_________________________________________________________________
conv1_relu (ReLU)            (None, 112, 112, 32)      0         
_________________________________________________________________
conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       
_________________________________________________________________
conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       
_________________________________________________________________
conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         
_________________________________________________________________
conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      
_________________________________________________________________
conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       
_________________________________________________________________
conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         
_________________________________________________________________
conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         
_________________________________________________________________
conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       
_________________________________________________________________
conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       
_________________________________________________________________
conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         
_________________________________________________________________
conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      
_________________________________________________________________
conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_pw_2_relu (ReLU)        (None, 56, 56,
... [output shortened for website readability]

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Notebook cell 30: new_model.compile(loss="binary_crossentropy", optimizer = "adam", metrics = ["accurac...

pythonexample

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

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Notebook cell 32: new_model.save('my_model3.h5')

pythonexample

new_model.save('my_model3.h5')

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Notebook cell 33: new_model = tf.keras.models.load_model('my_model.h5')

pythonexample

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

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Notebook cell 35: frame = cv2.imread('00002_Mask.jpg')

pythonexample

frame = cv2.imread('00002_Mask.jpg')

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Notebook cell 36: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

pythonexample

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

Notebook output

<matplotlib.image.AxesImage at 0x1b5ada5e988>

face-mask-detection-cnn notebook output cell 36 — Notebook output from cell 36: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

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Notebook cell 37: final_image =cv2.resize(frame, (224,224))

pythonexample

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

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Notebook cell 38: Predictions =new_model.predict(final_image)

pythonexample

Predictions =new_model.predict(final_image)

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Notebook cell 39: Predictions

pythonexample

Predictions

Notebook output

array([[-55.494904]], dtype=float32)

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Notebook cell 41: frame = cv2.imread('sad_women.jpg')

pythonexample

frame = cv2.imread('sad_women.jpg')

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Notebook cell 42: frame.shape

pythonexample

frame.shape

Notebook output

(480, 852, 3)

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Notebook cell 43: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

pythonexample

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

Notebook output

<matplotlib.image.AxesImage at 0x1b5f33aa7c8>

face-mask-detection-cnn notebook output cell 43 — Notebook output from cell 43: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

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Notebook cell 44: faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_...

pythonexample

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

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Notebook cell 45: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

pythonexample

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

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Notebook cell 46: gray.shape

pythonexample

gray.shape

Notebook output

(480, 852)

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Notebook cell 47: faces = faceCascade.detectMultiScale(gray,1.1,4)

pythonexample

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]

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Notebook cell 48: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

pythonexample

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

Notebook output

<matplotlib.image.AxesImage at 0x1b5eeee8648>

face-mask-detection-cnn notebook output cell 48 — Notebook output from cell 48: plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

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Notebook cell 49: plt.imshow(cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB))

pythonexample

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

Notebook output

<matplotlib.image.AxesImage at 0x1b5eef53488>

face-mask-detection-cnn notebook output cell 49 — Notebook output from cell 49: plt.imshow(cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB))

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Notebook cell 50: final_image =cv2.resize(face_roi, (224,224))

pythonexample

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

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Notebook cell 51: Predictions =new_model.predict(final_image)

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Predictions =new_model.predict(final_image)

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Notebook cell 52: Predictions

pythonexample

Predictions

Notebook output

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

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Notebook cell 55: import cv2 ### pip install opencv-python

pythonexample

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()

Notebook output

Face  not detected
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KeyboardInterrupt: 
[1;31m---------------------------------------------------------------------------
[1;31mKeyboardInterrupt                         Traceback (most recent call last)
[1;32m<ipython-input-56-9611355ee83c> in <module>[1;34m
     53     font [1;33m= cv2[1;33m.FONT_HERSHEY_SIMPLEX[1;33m[1;33m
     54 [1;33m
[1;32m---> 55[1;33m     Predictions [1;33m= new_model[1;33m.predict[1;33m(final_image[1;33m)[1;33m[1;33m
     56 [1;33m
     57     font_scale [1;33m= [1;36m1.5[1;33m[1;33m

[1;32mC:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
    907         max_queue_size[1;33m=max_queue_size[1;33m,[1;33m[1;33m
    908         workers[1;33m=workers[1;33m,[1;33m[1;33m
[1;32m--> 909[1;33m         use_multiprocessing=use_multiprocessing)
    910 [1;33m
    911   [1;32mdef reset_metrics[1;33m(self[1;33m)[1;33m:[1;33m[1;33m

[1;32mC:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict[1;34m(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)
    460     return self._model_iteration(
    461         model[1;33m, ModeKeys[1;33m.PREDICT[1;33m, x[1;33m=x[1;33m, batch_size[1;33m=batch_size[1;33m, verbose[1;33m=verbose[1;33m,[1;33m[1;33m
[1;32m--> 462[1;33m         steps=steps, callbacks=callbacks, **kwargs)
    463 [1;33m
    464 [1;33m

[1;32mC:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration[1;34m(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, **kwargs)
    394           sample_weights[1;33m=sample_weight[1;
... [output shortened for website readability]

This code block is extracted from the uploaded Jupyter notebook. Replace or rewrite the explanation later with your final teaching notes for this step.

Notebook cell 57: import winsound

pythonexample

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()

Notebook output

Face  not detected
Face  not detected
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Face  not detected

This code block is extracted from the uploaded Jupyter notebook. Replace or rewrite the explanation later with your final teaching notes for this step.

Download source code

Use this area to provide the complete source code, GitHub repository, Google Drive ZIP, notebook, dataset, or premium download link.

Download Notebook / Source Code

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