Update app.py

app.py

@@ -52,25 +52,104 @@ def test_connect():
 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)
-    # 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":str(class_name),"score":str(confidence_score)})
-    #######################
+    # image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
+    
+    # # 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":str(class_name),"score":str(confidence_score)})
+    # #######################
+
+
+
+
+# @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]
+
+    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="")
+    
+    # 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"})
+       
+        # 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():
   return render_template("index.html")
 
 
-
+@app.route('/table')
+def show_table():
+    # Get the current date
+    current_date = datetime.now().strftime("%Y-%m-%d")
+    # Read the CSV file to get attendance data
+    attendance=[]
+    try:
+        with open(f"{current_date}.csv", newline="") as csv_file:
+            csv_reader = csv.reader(csv_file)
+            attendance = list(csv_reader)
+    except FileNotFoundError:
+        pass
+    # Render the table.html template and pass the attendance data
+    return render_template('attendance.html', attendance=attendance)