Update app.py

app.py

@@ -50,80 +50,66 @@ def test_connect():
 
 @socket.on("image")
 def receive_image(image):
-    # Decode the base64-encoded image data
-    image = base64_to_image(image)
-    # image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
+    s = True
+    while s :
+        
+        # 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 = []
     
-    # # emit("processed_image", image)
-    # # Make the image a numpy array and reshape it to the models input shape.
-    # image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
-    # image = (image / 127.5) - 1
-    # # Predicts the model
-    # prediction = model.predict(image)
-    # index = np.argmax(prediction)
-    # class_name = class_names[index]
-    # confidence_score = prediction[0][index]
-    # 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 = []
-
-    # 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]
-
-    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]
-
-    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]
-    emit("result",{"name":"level1","score":"34"})
-    students = known_faces_names.copy()
-
-    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="")
+        # 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]
+    
+        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]
+    
+        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]
+        emit("result",{"name":"level1","score":"34"})
+        students = known_faces_names.copy()
+    
+        face_locations = []
+        face_encodings = []
+        face_names = []
     
-    # 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":"level222","score":"34"})
-    face_locations = face_recognition.face_locations(rgb_small_frame)
-    face_encodings = face_recognition.face_encodings(small_frame, face_locations)
-    face_names = []
-    emit("result",{"name":"level 33","score":str(len(face_encodings))})
-    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)
+        # 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":"level222","score":"34"})
+        face_locations = face_recognition.face_locations(rgb_small_frame)
+        face_encodings = face_recognition.face_encodings(small_frame, face_locations)
+        face_names = []
+        emit("result",{"name":"level 33","score":str(len(face_encodings))})
+        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]
     
-    emit("result",{"name":str(name),"score":"myScore"})
-    break
+            face_names.append(name)
+            s = False
+            break
+        
+        emit("result",{"name":str(name)+"------------","score":"myScore"})
+     
     
        
 #         # for name in face_names: