Eye_patch_Extractor_and_predictor.py

import numpy as np
import cv2
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import time
import streamlit as st
from webcam import webcam


face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
model = keras.models.load_model('my_model (1).h5')

st.set_option('deprecation.showPyplotGlobalUse', False)
st.title('Drowsiness Detection')
img = []

nav_choice = st.sidebar.radio('Navigation', ('Home', 'Sleep Detection', 'Help Us Improve'), index=0)
if nav_choice == 'Home':
    st.subheader('Prevents sleep deprivation road accidents, by alerting drowsy drivers.')
    st.image('ISHN0619_C3_pic.jpg')
    st.markdown('<b>In accordance with the survey taken by the Times Of India, about 40 % of road </b>'
                '<b>accidents are caused</b> '
                '<b>due to sleep deprivation & fatigued drivers. In order to address this issue, this app will </b>'
                '<b>alert such drivers with the help of deep learning models and computer vision.</b>'
                '', unsafe_allow_html=True)
    st.image('sleep.jfif', width=300)
    st.markdown('<h1>How to use?<br></h1>'
                '<b>1. Go to Sleep Detection page from the Navigation Side-Bar.</b><br>'
                '<b>2. Make sure that, you have sufficient amount of light, in your room.</b><br>'
                '<b>3. Align yourself such that, you are clearly visible in the web-cam and '
                'stay closer to the web-cam. </b><br>'
                '<b>4. Click "Capture" to capture the photo.</b><br>'
                '<b>5. If your eyes are closed, the model will write "Eyes are closed"(no sound can be made on cloud based web-app).</b><br>'
                '<font color="red"><br><b>For the purpose of the training process of the model, '
                'dataset used is available <a href="https://www.kaggle.com/kutaykutlu/drowsiness-detection", '
                'target="_blank">here</a></font></b>'
                , unsafe_allow_html=True)

elif nav_choice == 'Sleep Detection':
    st.header('Image Prediction')
    cap = 0
    st.success('Please look at your web-cam, while following all the instructions given on the Home page.')
    st.warning('Keeping the eyes in the same state is important but you can obviously blink your eyes, if they are open!!!')
    b = st.progress(0)
    for i in range(100):
        time.sleep(0.0001)
        b.progress(i + 1)

    start = st.radio('Options', ('Start', 'Stop'), key='Start_pred', index=1)
    if start == 'Start':
        cap = webcam()
        if cap is not None:
            frame = cap.convert('RGB')
            frame = np.array(frame, dtype='uint8')
            #frame = np.array(cap)
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            gray = np.array(gray, dtype='uint8')
            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
            for (x, y, w, h) in faces:
                cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 5)
                roi_gray = gray[y:y + w, x:x + w]
                roi_color = frame[y:y + h, x:x + w]
            # frame1 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            st.image(frame, width=250)

            try:
                centx, centy = roi_color.shape[:2]
                centx //= 2
                centy //= 2
                eye_1 = roi_color[centy - 40: centy, centx - 70: centx]
                eye_1 = cv2.resize(eye_1, (86, 86))
                eye_2 = roi_color[centy - 40: centy, centx: centx + 70]
                eye_2 = cv2.resize(eye_2, (86, 86))
                preds_eye1 = model.predict(np.expand_dims(eye_1, axis=0))
                preds_eye2 = model.predict(np.expand_dims(eye_2, axis=0))
                e1, e2 = np.argmax(preds_eye1), np.argmax(preds_eye2)
                st.write(e1, e2)
                if e1 == 1 or e2 == 1:
                    st.error('Eye(s) are closed')
                else:
                    st.success('Eyes are Opened')

                st.warning('Please click "Capture" to try again')

            except NameError:
                st.write('Face not detected please try again by clicking "Capture"')

else:
    st.header('Help Us Improve')
    st.success('We would appreciate your Help!!!')
    st.markdown(
        '<font face="Comic sans MS">To make this app better, we would appreciate your small amount of time.</font>'
        '<font face="Comic sans MS">Let me take you through, some of the basic statistical analysis of this </font>'
        '<font face="Comic sans MS">model. <br><b>Accuracy with naked eyes = 99.5%<br>Accuracy with spectacles = 96.8%</b><br></font> '
        '<font face="Comic sans MS">As we can see here, accuracy with spectacles is not at all spectacular, and hence to make this app </font>'
        '<font face="Comic sans MS">better, and to use it in real-time situations, we require as much data as we can gather.</font> '
        , unsafe_allow_html=True)
    st.warning('Your identity will be kept anonymous, and only your eye-patch will be extracted!!!')
    img_upload = st.file_uploader('Upload Image Here', ['png', 'jpg', 'jpeg'])
    if img_upload is not None:
        prog = st.progress(0)
        to_add = cv2.imread(str(img_upload.read()), 0)
        to_add = pd.DataFrame(to_add)
        to_add.to_csv('Data_from_users.csv', mode='a', header=False, index=False, sep=';')
        for i in range(100):
            time.sleep(0.001)
            prog.progress(i + 1)
        st.success('Uploaded Successfully!!! Thank you for contributing.')