Create app.py

app.py

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+# Importing Project Dependencies
+import numpy as np
+import cv2
+import pandas as pd
+import tensorflow as tf
+from tensorflow import keras
+import time
+import winsound
+import streamlit as st
+
+# Setting up config for GPU usage
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+# Using  Har-cascade classifier from OpenCV
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Loading the trained model for prediction purpose
+model = keras.models.load_model('my_model (1).h5')
+
+# Title for GUI
+st.title('Drowsiness Detection')
+img = []
+
+# Navigation Bar
+nav_choice = st.sidebar.radio('Navigation', ('Home', 'Sleep Detection', 'Help Us Improve'), index=0)
+# Home page
+if nav_choice == 'Home':
+    st.header('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. Web-cam will take 3 pictures of you, so keep your eyes in the same state'
+                ' (open or closed) for about 5 seconds.</b><br>'
+                '<b>5. If your eyes are closed, the model will make a beep sound to alert you.</b><br>'
+                '<b>6. Otherwise, the model will continue taking your pictures at regular intervals of time.</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)
+    
+# Sleep Detection page
+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':
+        decision = 0
+        st.markdown('<font face="Comic sans MS"><b>Detected Facial Region of Interest(ROI)&emsp;&emsp;&emsp;&emsp;&emsp;Extractd'
+                    ' Eye Features from the ROI</b></font>', unsafe_allow_html=True)
+        
+        # Best of 3 mechanism for drowsiness detection
+        for _ in range(3):
+            cap = cv2.VideoCapture(0)
+            ret, frame = cap.read()
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+            # Proposal of face region by the har cascade classifier
+            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)
+
+            try:
+                # Cenentroid method for extraction of eye-patch 
+                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))
+                cv2.rectangle(frame1, (x + centx - 60, y + centy - 40), (x + centx - 10, y + centy), (0, 255, 0), 5)
+                cv2.rectangle(frame1, (x + centx + 10, y + centy - 40), (x + centx + 60, y + centy), (0, 255, 0), 5)
+                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)
+                
+                # Display of face image and extracted eye-patch
+                img_container = st.beta_columns(4)
+                img_container[0].image(frame1, width=250)
+                img_container[2].image(cv2.cvtColor(eye_1, cv2.COLOR_BGR2RGB), width=150)
+                img_container[3].image(cv2.cvtColor(eye_2, cv2.COLOR_BGR2RGB), width=150)
+                print(e1, e2)
+                
+                # Decision variable for prediction
+                if e1 == 1 or e2 == 1:
+                    pass
+                else:
+                    decision += 1
+
+            except NameError:
+                st.warning('Hold your camera closer!!!\nTrying again in 2s')
+                cap.release()
+                time.sleep(1)
+                continue
+
+            except:
+                cap.release()
+                continue
+
+            finally:
+                cap.release()
+
+        # If found drowsy, then make a beep sound to alert the driver
+        if decision == 0:
+            st.error('Eye(s) are closed')
+            winsound.Beep(2500, 2000)
+
+        else:
+            st.success('Eyes are Opened')
+        st.warning('Please select "Stop" and then "Start" to try again')
+
+# Help Us Improve page
+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('NOTE: Your identity will be kept anonymous, and only your eye-patch will be extracted!!!')
+    # Image upload
+    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)
+        
+        # Save it in the database
+        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.')