# #!/usr/bin/env python
# # -*- coding: utf-8 -*-
# #from pygame import mixer 
# import json
# import pyaudio
# import wave
# import sys
# import csv
# import copy
# import argparse
# import itertools
# import time
# import winsound
# from collections import Counter
# from collections import deque

# import cv2 as cv
# import numpy as np
# import mediapipe as mp

# from utils import CvFpsCalc
# from model import KeyPointClassifier
# print("imported all the libraries")
# from model import PointHistoryClassifier


# import cv2 as cv
# import numpy as np
# import mediapipe as mp

# from model import KeyPointClassifier
# from utils import CvFpsCalc


# # Load your KeyPointClassifier and other necessary modules here
# mp_hands = mp.solutions.hands
# hands = mp_hands.Hands(
#     static_image_mode=False,
#     max_num_hands=2,
#     min_detection_confidence=0.5,
#     min_tracking_confidence=0.5,
# )
# keypoint_classifier = KeyPointClassifier()

# cvFpsCalc = CvFpsCalc(buffer_len=10)
# point_history = []
# keypoint_classifier_labels = {
#     0: "Fist",
#     1: "One",
#     2: "Two",
#     3: "Three",
#     4: "Four",
#     5: "Five",
#     6: "Rock",
#     7: "Spock",
#     8: "Live long and prosper"
# }

# def calc_bounding_rect(frame, landmarks):
#     # Calculate bounding box
#     brect = cv.boundingRect(np.array([landmark for landmark in landmarks]))
#     cv.rectangle(frame, (brect[0], brect[1]), (brect[0] + brect[2], brect[1] + brect[3]), (0, 255, 0), 2)
#     return brect

# def calc_landmark_list(frame, landmarks):
#     # Calculate landmark list
#     landmark_list = []
#     for i, landmark in enumerate(landmarks.landmark):
#         x = int(landmark.x * frame.shape[1])
#         y = int(landmark.y * frame.shape[0])
#         landmark_list.append([x, y])
#         cv.circle(frame, (x, y), 3, (0, 0, 255), thickness=5)
#     return landmark_list

# def pre_process_landmark(landmark_list):
#     # Convert to relative coordinates / normalized coordinates
#     pre_processed_landmark_list = []
#     base_x, base_y = landmark_list[0]
#     for landmark in landmark_list[1:]:
#         pre_processed_landmark_list.append([(landmark[0] - base_x), (landmark[1] - base_y)])
#     return pre_processed_landmark_list

# def draw_bounding_rect(use_brect, frame, brect):
#     # Draw bounding box
#     if use_brect:
#         cv.rectangle(frame, (brect[0], brect[1]), (brect[0] + brect[2], brect[1] + brect[3]), (255, 0, 0), 2)
#     return frame

# def draw_landmarks(frame, landmark_list):
#     # Draw landmarks
#     for landmark in landmark_list:
#         cv.circle(frame, (landmark[0], landmark[1]), 5, (0, 255, 0), thickness=-1)
#     return frame

# def draw_info_text(frame, brect, handedness, hand_sign):
#     # Draw information text
#     info_text = f"Handedness: {handedness.classification[0].label}"
#     cv.putText(frame, info_text, (brect[0], brect[1] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1, cv.LINE_AA)
#     cv.putText(frame, hand_sign, (brect[0], brect[1] - 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv.LINE_AA)
#     return frame

# def draw_point(frame, point):
#     # Draw point
#     cv.circle(frame, (point[0], point[1]), 5, (0, 255, 0), thickness=-1)
#     return frame

# def draw_line(frame, point1, point2):
#     # Draw line
#     cv.line(frame, (point1[0], point1[1]), (point2[0], point2[1]), (0, 0, 255), thickness=3)
#     return frame

# def draw_info(frame, fps, mode_text, keypoint_classifier_labels):
#     # Draw additional information
#     inf = [
#         ("Mode", mode_text),
#         ("FPS", f"{fps:.2f}"),
#     ]
#     for i, (key, value) in enumerate(inf):
#         y = 20 + i * 20
#         x = 20
#         cv.putText(frame, f"{key}: {value}", (x, y), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv.LINE_AA)

#     y = 50
#     for i, (key, value) in enumerate(keypoint_classifier_labels.items()):
#         y = 80 + i * 20
#         x = 20
#         cv.putText(frame, f"{key}: {value}", (x, y), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv.LINE_AA)

#     return frame

# def HAND(frame):
#     global point_history
#     global keypoint_classifier

#     debug_image = frame

#     frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
#     frame.flags.writeable = False
#     results = hands.process(frame)
#     frame.flags.writeable = True

#     if results.multi_hand_landmarks is not None:
#         for hand_landmarks, handedness in zip(results.multi_hand_landmarks, results.multi_handedness):
#             # Bounding box calculation
#             brect = calc_bounding_rect(debug_image, hand_landmarks)
#             # Landmark calculation
#             landmark_list = calc_landmark_list(debug_image, hand_landmarks)
#             # Conversion to relative coordinates / normalized coordinates
#             pre_processed_landmark_list = pre_process_landmark(landmark_list)
#             # Hand sign classification
#             hand_sign_id = keypoint_classifier(pre_processed_landmark_list)
#             if hand_sign_id == "Not applicable":
#                 point_history.append(landmark_list[8])
#             else:
#                 point_history.append([0, 0])
#             debug_image = draw_bounding_rect(use_brect, debug_image, brect)
#             debug_image = draw_landmarks(debug_image, landmark_list)
#             debug_image = draw_info_text(debug_image, brect, handedness, keypoint_classifier_labels[hand_sign_id])

#     else:
#         point_history.append([0, 0])

#     for i, point in enumerate(point_history):
#         if point[0] == 0 and point[1] == 0:
#             continue
#         debug_image = draw_point(debug_image, point)
#         if i != 0 and point_history[i - 1][0] != 0 and point_history[i - 1][1] != 0:
#             debug_image = draw_line(debug_image, point_history[i - 1], point)

#     mode_text = "Mode: Normal"
#     debug_image = draw_info(debug_image, 0.0, mode_text, keypoint_classifier_labels)

#     return debug_image