Update app.py

app.py

@@ -1,126 +1,127 @@
-from PIL import Image
+# from PIL import Image
 from flask import *
-import face_recognition
-import cv2
-import numpy as np
-import csv
-from datetime import datetime
+# import face_recognition
+# import cv2
+# import numpy as np
+# import csv
+# from datetime import datetime
 
-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
+# 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
 
-import io
-import streamlit as st
-bytes_data=None
+# import io
+# import streamlit as st
+# bytes_data=None
 
 app = Flask(__name__)
 
-@app.route('/at')
-def attend():
-    # Face recognition variables
-    known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
-    known_face_encodings = []
+# @app.route('/at')
+# def attend():
+#     # Face recognition variables
+#     known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
+#     known_face_encodings = []
 
-    # Load known face encodings
-    sir_image = face_recognition.load_image_file("photos/sir.jpeg")
-    sir_encoding = face_recognition.face_encodings(sir_image)[0]
+#     # Load known face encodings
+#     sir_image = face_recognition.load_image_file("photos/sir.jpeg")
+#     sir_encoding = face_recognition.face_encodings(sir_image)[0]
 
-    vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
-    vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
+#     vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
+#     vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
 
-    lalit_image = face_recognition.load_image_file("photos/lalit.jpg")
-    lalit_encoding = face_recognition.face_encodings(lalit_image)[0]
+#     lalit_image = face_recognition.load_image_file("photos/lalit.jpg")
+#     lalit_encoding = face_recognition.face_encodings(lalit_image)[0]
 
-    jasmine_image = face_recognition.load_image_file("photos/jasmine.jpg")
-    jasmine_encoding = face_recognition.face_encodings(jasmine_image)[0]
+#     jasmine_image = face_recognition.load_image_file("photos/jasmine.jpg")
+#     jasmine_encoding = face_recognition.face_encodings(jasmine_image)[0]
 
-    maam_image = face_recognition.load_image_file("photos/maam.png")
-    maam_encoding = face_recognition.face_encodings(maam_image)[0]
+#     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]
+#     known_face_encodings = [sir_encoding, vikas_encoding,lalit_encoding,jasmine_encoding,maam_encoding]
 
-    students = known_faces_names.copy()
+#     students = known_faces_names.copy()
 
-    face_locations = []
-    face_encodings = []
-    face_names = []
+#     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="")
+#     now = datetime.now()
+#     current_date = now.strftime("%Y-%m-%d")
+#     csv_file = open(f"{current_date}.csv", "a+", newline="")
     
-    csv_writer = csv.writer(csv_file)
-    def run_face_recognition():
-        video_capture = cv2.VideoCapture(0)
-        s = True
+#     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   
+#         existing_names = set(row[0] for row in csv.reader(csv_file))  # Collect existing names from the CSV file   
         
 
-        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]
+#         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]
             
-            face_locations = face_recognition.face_locations(rgb_small_frame)
-            face_encodings = face_recognition.face_encodings(small_frame, face_locations)
-            face_names = []
+#             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]
+#             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)
+#                 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
+#                 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)
+#                         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')
+    return 'hello'
+    # return render_template('index.html')
 
 
 if __name__ == '__main__':