common.py

# Copyright (c) 2017 May
#!_*_coding:UTF-8_*_
#中国七位字符车牌识别,34汉字+10数字+24字母=68
#中国车牌没有I和O
import numpy
__all__ = (
    # 'DIGITS',
    # 'LETTERS',
    'CHARS')

# chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新",  "警", "使", "学", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X","Y", "Z"]

# WORD = "京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新警使学"
# DIGITS = "0123456789"
# LETTERS = "ABCDEFGHJKLMNPQRSTUVWXYZ"
# CHARS = WORD + DIGITS + LETTERS

index_show = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,"闽": 13, "赣": 14, "鲁": 15,
              "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,"藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30,
              "警":31, "使":32, "学":33, "0": 34, "1": 35, "2": 36, "3": 37, "4": 38, "5": 39,"6": 40, "7": 41, "8": 42, "9": 43, "A": 44, "B": 45, "C": 46,
              "D": 47,  "E": 48, "F": 49, "G": 50, "H": 51,"J": 52, "K": 53, "L": 54, "M": 55, "N": 56, "P": 57, "Q": 58, "R":59, "S": 60, "T": 61, "U": 62,
              "V": 63,"W": 64, "X": 65, "Y": 66, "Z": 67}

show_index={0: "京", 1: "沪", 2: "津", 3: "渝", 4: "冀", 5: "晋", 6: "蒙", 7: "辽", 8: "吉", 9: "黑", 10: "苏", 11: "浙", 12: "皖",13: "闽", 14: "赣", 15: "鲁",
              16: "豫", 17: "鄂", 18: "湘", 19: "粤", 20: "桂", 21: "琼", 22: "川", 23: "贵", 24: "云", 25: "藏", 26: "陕", 27: "甘", 28: "青", 29: "宁", 30: "新",
              31: "警", 32: "使", 33: "学", 34: "0", 35: "1", 36: "2", 37: "3", 38: "4", 39: "5",40: "6", 41: "7", 42: "8", 43: "9", 44: "A", 45: "B", 46: "C",
              47: "D",  48: "E", 49: "F", 50: "G", 51: "H",52: "J", 53: "K", 54: "L", 55: "M", 56: "N", 57: "P", 58: "Q",59 :"R", 60: "S", 61: "T", 62: "U",
              63: "V",64: "W", 65: "X", 66: "Y", 67: "Z"}

# print show_index
num_class=len(index_show)    #68

def softmax(a):
    exps = numpy.exp(a.astype(numpy.float64))
    return exps / numpy.sum(exps, axis=-1)[:, numpy.newaxis]

def sigmoid(a):
  return 1. / (1. + numpy.exp(-a))