CRNN_Chinese_Characters_Rec/lib/dataset/_360cc.py

from __future__ import print_function, absolute_import
import torch.utils.data as data
import os
import numpy as np
import cv2

class _360CC(data.Dataset):
    def __init__(self, config, is_train=True):

        self.root = config.DATASET.ROOT
        self.is_train = is_train
        self.inp_h = config.MODEL.IMAGE_SIZE.H
        self.inp_w = config.MODEL.IMAGE_SIZE.W

        self.dataset_name = config.DATASET.DATASET

        self.mean = np.array(config.DATASET.MEAN, dtype=np.float32)
        self.std = np.array(config.DATASET.STD, dtype=np.float32)

        char_file = config.DATASET.CHAR_FILE
        with open(char_file, 'rb') as file:
            char_dict = {num: char.strip().decode('gbk', 'ignore') for num, char in enumerate(file.readlines())}

        txt_file = config.DATASET.JSON_FILE['train'] if is_train else config.DATASET.JSON_FILE['val']

        # convert name:indices to name:string
        self.labels = []
        with open(txt_file, 'r', encoding='utf-8') as file:
            contents = file.readlines()
            for c in contents:
                imgname = c.split(' ')[0]
                indices = c.split(' ')[1:]
                string = ''.join([char_dict[int(idx)] for idx in indices])
                self.labels.append({imgname: string})

        print("load {} images!".format(self.__len__()))

    def __len__(self):
        return len(self.labels)

    # def __getitem__(self, idx):
    #
    #     img_name = list(self.labels[idx].keys())[0]
    #     img = cv2.imread(os.path.join(self.root, img_name))
    #     img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #
    #     img_h, img_w = img.shape
    #
    #     img = cv2.resize(img, (0,0), fx=self.inp_w / img_w, fy=self.inp_h / img_h, interpolation=cv2.INTER_CUBIC)
    #     img = np.reshape(img, (self.inp_h, self.inp_w, 1))
    #
    #     img = img.astype(np.float32)
    #     img = (img/255. - self.mean) / self.std
    #     img = img.transpose([2, 0, 1])
    #
    #     return img, idx


    def __getitem__(self, idx):

        img_name = list(self.labels[idx].keys())[0]
        img = cv2.imread(os.path.join(self.root, img_name))
        # img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # 数据增强
        img = img_process(img)

        img_h, img_w = img.shape[:2]

        # img = cv2.resize(img, (0,0), fx=self.inp_w / img_w, fy=self.inp_h / img_h, interpolation=cv2.INTER_CUBIC)
        # img = np.reshape(img, (self.inp_h, self.inp_w, 1))

        # 图片真实长宽比
        ratio = img_w / float(img_h)

        # 按比例缩放
        if math.ceil(self.inp_h * ratio) > self.inp_w:
            # 如大于默认宽度，则宽度为imgW
            resized_w = self.inp_w
        else:
            # 如小于默认宽度则以图片真实宽为准
            resized_w = int(math.ceil(self.inp_h * ratio))
        # 缩放
        img = cv2.resize(img, (resized_w, self.inp_h))


        img = img.astype(np.float32)
        # 标准化
        img = (img/255. - self.mean) / self.std
        img = img.transpose([2, 0, 1])

        # 对宽度不足的位置，补0
        padding_im = np.zeros((3, self.inp_h, self.inp_w), dtype=np.float32)
        # padding_im = np.full((3, self.inp_h, self.inp_w),255, dtype=np.float32)
        padding_im[:, :, 0:resized_w] = img
        # cv2.imshow('3', padding_im.transpose((1, 2, 0)))
        # cv2.waitKey(3000)
        return padding_im, idx

import math
def resize_norm_img(img):
    """
    数据缩放和归一化
    :param img: 输入图片
    """

    # 默认输入尺寸 英文为 (3,32,100)
    imgC = 3
    imgH = 32   # 高度 32 效果比较好
    imgW = 320  # 中文每个字长宽比为1:1，防止长宽比在resize后被压缩过小或拉伸过大，识别大概10个字左右，所以宽度 10倍 = 320

    # 图片的真实高宽
    h, w = img.shape[:2]
    # 图片真实长宽比
    ratio = w / float(h)

    # 按比例缩放
    if math.ceil(imgH * ratio) > imgW:
        # 如大于默认宽度，则宽度为imgW
        resized_w = imgW
    else:
        # 如小于默认宽度则以图片真实宽为准
        resized_w = int(math.ceil(imgH * ratio))
    # 缩放
    resized_image = cv2.resize(img, (resized_w, imgH))
    resized_image = resized_image.astype('float32')
    # 归一化
    resized_image = resized_image.transpose((2, 0, 1)) / 255
    resized_image -= 0.5
    resized_image /= 0.5
    # 对宽度不足的位置，补0
    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
    padding_im[:, :, 0:resized_w] = resized_image
    # 转置 padding 后的图片用于可视化
    draw_img = padding_im.transpose((1,2,0))
    return padding_im, draw_img


# 噪点增加
def add_noise(img):
    for i in range(20): #添加点噪声
        temp_x = np.random.randint(0,img.shape[0])
        temp_y = np.random.randint(0,img.shape[1])
        img[temp_x][temp_y] = 255
    return img

# 适当腐蚀
def add_erode(img):
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3, 3))
    img = cv2.erode(img,kernel)
    return img

# 适当膨胀
def add_dilate(img):
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3, 3))
    img = cv2.dilate(img,kernel)
    return img

import random
# 随机扰动
# def do(self,img_list=[]):
#     aug_list= copy.deepcopy(img_list)
#     for i in range(len(img_list)):
#         im = img_list[i]
#         if self.noise and random.random()<0.5:
#             im = self.add_noise(im)
#         if self.dilate and random.random()<0.25:
#             im = self.add_dilate(im)
#         if self.erode and random.random()<0.25:
#             im = self.add_erode(im)
#         aug_list.append(im)
#     return aug_list
def img_process(img):
    if random.random() < 0.5:
        img = add_noise(img)
    if random.random()<0.25:
        img = add_dilate(img)
    if random.random()<0.25:
        img = add_erode(img)

    return img