Update app.py

app.py

@@ -4,74 +4,113 @@ import numpy as np
 import csv
 from datetime import datetime
 
-sir_image = face_recognition.load_image_file("photos/sir.jpeg")
-sir_encoding = face_recognition.face_encodings(sir_image)[0]
+###########################
+from flask import Flask,render_template
+from flask_socketio import SocketIO,emit
+import base64
 
-vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
-vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
 
-known_face_encoding = [sir_encoding, vikas_encoding]
-known_faces_names = ["Sarwan Sir", "Vikas"]
-students = known_faces_names.copy()
+app = Flask(__name__)
+app.config['SECRET_KEY'] = 'secret!'
+socket = SocketIO(app,async_mode="eventlet")
 
-face_locations = []
-face_encodings = []
-face_names = []
-s = True
 
-now = datetime.now()
-current_date = now.strftime("%Y-%m-%d")
+###################################
 
-f = open(current_date + '.csv', 'w+', newline='')
-lnwriter = csv.writer(f)
 
-import io
-import streamlit as st
 
-bytes_data = None
+video_capture=cv2.VideoCapture(1)
+if not video_capture.isOpened():
+    print("Failed to open the video capture.")
+    exit()
 
-run = st.checkbox('Run')
-FRAME_WINDOW = st.image([])
-camera = cv2.VideoCapture(0)
 
-while run:
-    _, frame = camera.read()
-    
-    if frame is None:
-        continue
-    
-    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
-    rgb_small_frame = small_frame[:, :, ::-1]
-    
-    if s:
-        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_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)
-            
-            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"])
+
+
+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]
+
+known_face_encoding=[sir_encoding,vikas_encoding]
+
+known_faces_names=["Sarwan Sir","Vikas"]
+
+students=known_faces_names.copy()
+
+face_locations=[]
+face_encodings=[]
+face_names=[]
+s=True
+
+
+
+now=datetime.now()
+current_date=now.strftime("%Y-%m-%d")
+
+
+f=open(current_date+'.csv','w+',newline='')
+lnwriter=csv.writer(f)
+
+############################
+def base64_to_image(base64_string):
+    # Extract the base64 encoded binary data from the input string
+    base64_data = base64_string.split(",")[1]
+    # Decode the base64 data to bytes
+    image_bytes = base64.b64decode(base64_data)
+    # Convert the bytes to numpy array
+    image_array = np.frombuffer(image_bytes, dtype=np.uint8)
+    # Decode the numpy array as an image using OpenCV
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    return image
+
+@socket.on("connect")
+def test_connect():
+    print("Connected")
+    emit("my response", {"data": "Connected"})
+
+@socket.on("image")
+def receive_image(image):
+    return render_template("index.html" , myimage = image , cname = class_name )
+    while True:
+        # _,frame=camera.read()
+        frame=base64_to_image(image)
+     
+        small_frame=cv2.resize(frame,(0,0),fx=0.25,fy=0.25)
+        rgb_small_frame=small_frame[:,:,::-1]
+        if s:
+            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_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]
     
-    cv2.imshow("attendance system", frame)
+                face_names.append(name)
+                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("attendence system",frame)
+        if cv2.waitKey(1) & 0xFF==ord('q'):
+            break
     
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-    else:
-        st.write('Stopped')
-        
-f.close()
+    f.close()
+
+
+#######################################
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+if __name__ == '__main__':
+    # app.run(debug=True)
+    socket.run(app,host="0.0.0.0", port=7860)
+#######################################