Update app.py

app.py

@@ -1,72 +1,130 @@
+from PIL import Image
+from flask import *
 import face_recognition
 import cv2
 import numpy as np
 import csv
 from datetime import datetime
 
-video_capture = cv2.VideoCapture(1)
+from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
+import pylab # this allows you to control figure size 
+pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
 
-sir_image = face_recognition.load_image_file("photos/sir.jpeg")
-sir_encoding = face_recognition.face_encodings(sir_image)[0]
+import io
+import streamlit as st
+bytes_data=None
 
-vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
-vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
+app = Flask(__name__)
 
-known_face_encoding = [sir_encoding, vikas_encoding]
-known_faces_names = ["Sarwan Sir", "Vikas"]
+@app.route('/at')
+def attend():
+    # Face recognition variables
+    known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
+    known_face_encodings = []
 
-students = known_faces_names.copy()
+    # Load known face encodings
+    sir_image = face_recognition.load_image_file("photos/sir.jpeg")
+    sir_encoding = face_recognition.face_encodings(sir_image)[0]
 
-now = datetime.now()
-current_date = now.strftime("%Y-%m-%d")
+    vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
+    vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
 
-# Create and open the CSV file
-f = open(current_date + '.csv', 'w+', newline='')
-lnwriter = csv.writer(f)
+    lalit_image = face_recognition.load_image_file("photos/lalit.jpg")
+    lalit_encoding = face_recognition.face_encodings(lalit_image)[0]
 
-# Initialize variables
-face_locations = []
-face_encodings = []
-face_names = []
-s = True
+    jasmine_image = face_recognition.load_image_file("photos/jasmine.jpg")
+    jasmine_encoding = face_recognition.face_encodings(jasmine_image)[0]
 
-while True:
-    ret, frame = video_capture.read()
-    
-    if not ret:
-        continue
+    maam_image = face_recognition.load_image_file("photos/maam.png")
+    maam_encoding = face_recognition.face_encodings(maam_image)[0]
+
+    known_face_encodings = [sir_encoding, vikas_encoding,lalit_encoding,jasmine_encoding,maam_encoding]
+
+    students = known_faces_names.copy()
 
-    rgb_frame = frame[:, :, ::-1]
+    face_locations = []
+    face_encodings = []
+    face_names = []
+
+    now = datetime.now()
+    current_date = now.strftime("%Y-%m-%d")
+    csv_file = open(f"{current_date}.csv", "a+", newline="")
     
-    if s:
-        face_locations = face_recognition.face_locations(rgb_frame)
-        face_encodings = face_recognition.face_encodings(frame, face_locations)
-        face_names = []
+    csv_writer = csv.writer(csv_file)
+    def run_face_recognition():
+        video_capture = cv2.VideoCapture(0)
+        s = True
+
+        existing_names = set(row[0] for row in csv.reader(csv_file))  # Collect existing names from the CSV file   
         
-        for face_encoding in face_encodings:
-            matches = face_recognition.compare_faces(known_face_encoding, face_encoding)
-            name = ""
-            face_distance = face_recognition.face_distance(known_face_encoding, face_encoding)
-            best_match_index = np.argmin(face_distance)
-            
-            if matches[best_match_index]:
-                name = known_faces_names[best_match_index]
-            
-            face_names.append(name)
+
+        while s:
+            _, frame = video_capture.read()
+            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+            rgb_small_frame = small_frame[:, :, ::-1]
             
-            if name in known_faces_names:
-                if name in students:
-                    students.remove(name)
-                    print(students)
-                    current_time = now.strftime("%H-%M-%S")
-                    lnwriter.writerow([name, current_time, "Present"])
-    
-    cv2.imshow("attendance system", frame)
-    
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
+            face_locations = face_recognition.face_locations(rgb_small_frame)
+            face_encodings = face_recognition.face_encodings(small_frame, face_locations)
+            face_names = []
+
+            for face_encoding in face_encodings:
+                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+                name = ""
+                face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
+                best_match_index = np.argmin(face_distance)
+                if matches[best_match_index]:
+                    name = known_faces_names[best_match_index]
+
+                face_names.append(name)
+
+               
+                for name in face_names:
+                    if name in known_faces_names and name in students and name not in existing_names:
+                        students.remove(name)
+                        print(students)
+                        print(f"Attendance recorded for {name}")
+                        current_time = now.strftime("%H-%M-%S")
+                        csv_writer.writerow([name, current_time, "Present"])
+                        existing_names.add(name)  # Add the name to the set of existing names
+                        
+                        s = False  # Set s to False to exit the loop after recording attendance
+                        break  # Break the loop once attendance has been recorded for a name
+
+            cv2.imshow("Attendance System", frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+        video_capture.release()
+        cv2.destroyAllWindows()
+        csv_file.close()
+
+    # Call the function to run face recognition
+    run_face_recognition()
+
+    return redirect(url_for('show_table'))
+
+@app.route('/table')
+def show_table():
+    # Get the current date
+    current_date = datetime.now().strftime("%Y-%m-%d")
+    # Read the CSV file to get attendance data
+    attendance=[]
+    try:
+        with open(f"{current_date}.csv", newline="") as csv_file:
+            csv_reader = csv.reader(csv_file)
+            attendance = list(csv_reader)
+    except FileNotFoundError:
+        pass
+    # Render the table.html template and pass the attendance data
+    return render_template('attendance.html', attendance=attendance)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+if __name__ == '__main__':
+    # Start Flask application
+    app.run(debug=True)
 
-# Release the video capture and close the CSV file
-video_capture.release()
-cv2.destroyAllWindows()
-f.close()
+