Update app.py

app.py

@@ -63,7 +63,7 @@ def receive_image(image):
     # index = np.argmax(prediction)
     # class_name = class_names[index]
     # confidence_score = prediction[0][index]
-    emit("result",{"name":"mrmr","score":"34"})
+    # emit("result",{"name":"mrmr","score":"34"})
     # #######################
 
 
@@ -72,55 +72,57 @@ def receive_image(image):
 # # @app.route('/at')
 # # def attend():
 # #     # Face recognition variables
-#     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]
+    emit("result",{"name":"level1","score":"34"})
+    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]
+    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":"34"})
+    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)
     
-#     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"})
+    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: