Update app.py

app.py

@@ -63,73 +63,73 @@ def receive_image(image):
     # index = np.argmax(prediction)
     # class_name = class_names[index]
     # confidence_score = prediction[0][index]
-    # emit("result",{"name":str(class_name),"score":str(confidence_score)})
+    emit("result",{"name":"mrmr","score":"34"})
     # #######################
 
 
 
 
-# @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)
-    small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
-    rgb_small_frame = small_frame[:, :, ::-1]
+#     # 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]
     
-    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)
-    emit("result",{"name":str(name),"score":"myScore"})
+#     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)
+#     emit("result",{"name":str(name),"score":"myScore"})
        
-        # 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
     
 @app.route ("/")
 def home():