# coding=utf-8
import os, multiprocessing


RAW_FRAME_Q = multiprocessing.Queue() # 传递原始帧的队列
DISPLAY_IMAGE_Q = multiprocessing.Queue() # 传递需要显示的图片的队列，从识别器向窗体方向传递
PLAYER_INFO_Q = multiprocessing.Queue() # 显示运动员信息的队列，从识别器向窗体方向传递
LOG_Q = multiprocessing.Queue() # 处理日志的消息队列，，从识别器向窗体方向传递

CLICK_EVENT = multiprocessing.Event()
RECONGNIZER_EVENT = multiprocessing.Event()
INIT_EVENT = multiprocessing.Event()


# 程序工作路径
WORKING_PATH = os.path.abspath(os.path.dirname(__file__))

# 报名运动员存放信息的目录
REGISTERED_PLAYERS = os.path.join(WORKING_PATH, 'registered_players')

# 报名运动员照片存放路径
REGISTERED_PLAYERS_IMAGES = os.path.join(REGISTERED_PLAYERS, 'images')

# 报名运动员人脸信息数据存放路径
REGISTERED_PLAYERS_FACE_DESCRIPTION = os.path.join(REGISTERED_PLAYERS, 'face_description')



# 检录运动员信息的存放路径
CHECKIN_PLAYERS_PATH = os.path.join(WORKING_PATH, 'checkin_players')




# 依赖文件存放路径
SOURCE = os.path.join(WORKING_PATH, 'source')

TAKE_SUCCESS = os.path.join(SOURCE, 'take_success.wav') # 拍摄成功后播放的声音

TAKE_FAILED = os.path.join(SOURCE, 'take_failed.wav') # 拍摄失败后播放的声音

RECOGNIZE_SUCCESS = os.path.join(SOURCE, 'recognize_success.wav') # 识别成功后播放的声音

RECOGNIZE_FAILED = os.path.join(SOURCE, 'recognize_failed.wav') # 识别失败后播放的声音

NOBODY = os.path.join(SOURCE, 'nobody.wav') # 没有检测到人脸播放的声音

DEFAULT_IMAGE_PATH = os.path.join(SOURCE, 'default.jpeg')

# 人脸的68个特征点模型文件
PREDICTOR_PATH = os.path.join(SOURCE, 'shape_predictor_68_face_landmarks.dat')

# 人脸特征描述模型文件
FACE_REC_MODEL_PATH = os.path.join(SOURCE, 'dlib_face_recognition_resnet_model_v1.dat')

# 字体文件
FONT = os.path.join(SOURCE, 'Arial Unicode.ttf')

# 真实人脸与已经录入的人脸数据的相似程度的阀值，大于60认为同一人，越大就越相似
SIMILARITY_THRESHOLD = 55.0