common.py

# coding=utf-8
import mxnet as mx
import numpy as np

index = {"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, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
         "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
         "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
         "W": 61, "X": 62, "Y": 63, "Z": 64}

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", "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"
         ]


def rand(val):
    return int(np.random.random() * val)


def plate_recognition_net(train=False):
    data = mx.symbol.Variable('data')

    conv1 = mx.symbol.Convolution(data=data, kernel=(5, 5), num_filter=32)
    pool1 = mx.symbol.Pooling(data=conv1, pool_type="max", kernel=(2, 2), stride=(1, 1))
    relu1 = mx.symbol.Activation(data=pool1, act_type="relu")

    conv2 = mx.symbol.Convolution(data=relu1, kernel=(5, 5), num_filter=32)
    pool2 = mx.symbol.Pooling(data=conv2, pool_type="avg", kernel=(2, 2), stride=(1, 1))
    relu2 = mx.symbol.Activation(data=pool2, act_type="relu")

    flatten = mx.symbol.Flatten(data=relu2)
    fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=120)
    fc21 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc22 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc23 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc24 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc25 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc26 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc27 = mx.symbol.FullyConnected(data=fc1, num_hidden=65)
    fc2 = mx.symbol.Concat(*[fc21, fc22, fc23, fc24, fc25, fc26, fc27], dim=0)

    if train:
        label = mx.symbol.Variable('softmax_label')
        label = mx.symbol.transpose(data=label)
        label = mx.symbol.Reshape(data=label, target_shape=(0,))

        return mx.symbol.SoftmaxOutput(data=fc2, label=label, name="softmax")
    else:
        return mx.symbol.SoftmaxOutput(data=fc2, name="softmax")