Update app.py

app.py

@@ -29,86 +29,86 @@ socket = SocketIO(app,async_mode="eventlet")
 
 
 
-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):
-    global cnt
-    s = True
-    while s :
+# 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):
+#     global cnt
+#     s = True
+#     while s :
         
-        # Decode the base64-encoded image data
-        image = base64_to_image(image)
+#         # Decode the base64-encoded image data
+#         image = base64_to_image(image)
      
-        known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
-        known_face_encodings = []
+#         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)
-        small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
-        rgb_small_frame = small_frame[:, :, ::-1]
-        # # emit("result",{"name":"level " +str(cnt),"score":str(len(face_encodings))})
+#         # # csv_writer = csv.writer(csv_file)
+#         small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
+#         rgb_small_frame = small_frame[:, :, ::-1]
+#         # # emit("result",{"name":"level " +str(cnt),"score":str(len(face_encodings))})
         
-        face_locations = face_recognition.face_locations(rgb_small_frame)
-        face_encodings = face_recognition.face_encodings(small_frame, face_locations)
-        face_names = []
-        emit("result",{"name":"level2 " +str(cnt),"score":str(len(face_encodings))})
-        cnt = cnt +1
-        for face_encoding in face_encodings:
-            # emit("result",{"name":"in for ","score":"34"})
-            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_locations = face_recognition.face_locations(rgb_small_frame)
+#         face_encodings = face_recognition.face_encodings(small_frame, face_locations)
+#         face_names = []
+#         emit("result",{"name":"level2 " +str(cnt),"score":str(len(face_encodings))})
+#         cnt = cnt +1
+#         for face_encoding in face_encodings:
+#             # emit("result",{"name":"in for ","score":"34"})
+#             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)
-            s = False
-            break
+#             face_names.append(name)
+#             s = False
+#             break
         
-        emit("result",{"name":str(name),"score":"myScore"})
+#         emit("result",{"name":str(name),"score":"myScore"})