DBnet在windows端的训练与测试

DBnet是一种流行的场景文本检测算法，通常与文本识别算法结合使用（检测+识别）。

一、准备工作

1、代码与数据集准备

代码下载地址：GitHub - WenmuZhou/DBNet.pytorch: A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization数据集地址：Downloads - Incidental Scene Text - Robust Reading Competition

数据集下载后通过以下代码生成trian.txt和test.txt，自行修改输入输出路径（Linux直接用generate_lists.sh文件生成）

import os
def get_images(img_path):
    '''
    find image files in data path
    :return: list of files found
    '''
    files = []
    exts = ['jpg', 'png', 'jpeg', 'JPG', 'PNG']
    for parent, dirnames, filenames in os.walk(img_path):
        for filename in filenames:
            for ext in exts:
                if filename.endswith(ext):
                    files.append(os.path.join(parent, filename))
                    break
    print('Find {} images'.format(len(files)))
    return sorted(files)

def get_txts(txt_path):
    '''
    find gt files in data path
    :return: list of files found
    '''
    files = []
    exts = ['txt']
    for parent, dirnames, filenames in os.walk(txt_path):
        for filename in filenames:
            for ext