data_gen.py

# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project Name: yolov5-jishi
File Name: data_gen.py
Author: chenming
Create Date: 2021/11/8
Description：
-------------------------------------------------
"""
# -*- coding: utf-8 -*-
# @Time    : 20210610
# @Author  : dejahu
# @File    : gen_yolo_data.py
# @Software: PyCharm
# @Brief   : 生成测试、验证、训练的图片和标签

import os
import shutil
from pathlib import Path
from shutil import copyfile

import cv2
from PIL import Image, ImageDraw
from xml.dom.minidom import parse
import numpy as np
import os.path as osp
import random

# main，首先在当前目录生成数据的划分

# 开始转化数据集

# 更换代码中images替换的逻辑，和文件对应起来

# todo 修改为你的数据的根目录
FILE_ROOT = "/scm/data/xianyu/Mask/"
IMAGE_SET_ROOT = FILE_ROOT + "VOC2021_Mask/ImageSets/Main"   # 图片区分文件的路径
IMAGE_PATH = FILE_ROOT + "VOC2021_Mask/JPEGImages"  # 图片的位置
ANNOTATIONS_PATH = FILE_ROOT + "VOC2021_Mask/Annotations"  # 数据集标签文件的位置
LABELS_ROOT = FILE_ROOT + "VOC2021_Mask/Labels"  # 进行归一化之后的标签位置
DEST_PPP = FILE_ROOT + "mask_yolo_format"
DEST_IMAGES_PATH = "mask_yolo_format/images"  # 区分训练集、测试集、验证集的图片目标路径
DEST_LABELS_PATH = "mask_yolo_format/labels"  # 区分训练集、测试集、验证集的标签文件目标路径

if osp.isdir(LABELS_ROOT):
    shutil.rmtree(LABELS_ROOT)
    print("Labels存在，已删除")

if osp.isdir(DEST_PPP):
    shutil.rmtree(DEST_PPP)
    print("Dest目录存在，已删除")
# todo 修改为你数据集的标签名称
label_names = ['face', 'face_mask']


def cord_converter(size, box):
    """
    将标注的 xml 文件标注转换为 darknet 形的坐标
    :param size: 图片的尺寸： [w,h]
    :param box: anchor box 的坐标 [左上角x,左上角y,右下角x,右下角y,]
    :return: 转换后的 [x,y,w,h]
    """
    x1 = int(box[0])
    y1 = int(box[1])
    x2 = int(box[2])
    y2 = int(box[3])

    dw = np.float32(1. / int(size[0]))
    dh = np.float32(1. / int(size[1]))

    w = x2 - x1
    h = y2 - y1
    x = x1 + (w / 2)
    y = y1 + (h / 2)

    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return [x, y, w, h]


def save_file(img_jpg_file_name, size, img_box):
    # print("保存图片")
    save_file_name = LABELS_ROOT + '/' + img_jpg_file_name + '.txt'
    # print(save_file_name)
    file_path = open(save_file_name, "a+")
    # 默认给定的是id为0，防止错误数据的出现
    for box in img_box:
        box_name = box[0]
        cls_num = 0
        if box_name in label_names:
            cls_num = label_names.index(box_name)
        new_box = cord_converter(size, box[1:])
        file_path.write(f"{cls_num} {new_box[0]} {new_box[1]} {new_box[2]} {new_box[3]}\n")
    file_path.flush()
    file_path.close()


def test_dataset_box_feature(file_name, point_array):
    """
    使用样本数据测试数据集的建议框
    :param image_name: 图片文件名
    :param point_array: 全部的点 [建议框sx1,sy1,sx2,sy2]
    :return: None
    """
    im = Image.open(rf"{IMAGE_PATH}\{file_name}")
    imDraw = ImageDraw.Draw(im)
    for box in point_array:
        x1 = box[1]
        y1 = box[2]
        x2 = box[3]
        y2 = box[4]
        imDraw.rectangle((x1, y1, x2, y2), outline='red')
    im.show()


def get_xml_data(file_path, img_xml_file):
    img_path = file_path + '/' + img_xml_file + '.xml'
    # print(img_path)
    dom = parse(img_path)
    root = dom.documentElement
    img_name = root.getElementsByTagName("filename")[0].childNodes[0].data
    img_jpg_file_name = img_xml_file + '.jpg'
    # print(img_jpg_file_name)
    cv2.imread(img_jpg_file_name)
    img_size = root.getElementsByTagName("size")[0]
    if len(img_size) == 0:
        img_h, img_w, c = cv2.imread(img_jpg_file_name).shape
    else:
        img_w = img_size.getElementsByTagName("width")[0].childNodes[0].data
        img_h = img_size.getElementsByTagName("height")[0].childNodes[0].data
        img_c = img_size.getElementsByTagName("depth")[0].childNodes[0].data
    objects = root.getElementsByTagName("object")

    img_box = []
    for box in objects:
        cls_name = box.getElementsByTagName("name")[0].childNodes[0].data
        # todo 更换坐标点转换的逻辑
        x1 = int(float(box.getElementsByTagName("xmin")[0].childNodes[0].data))
        y1 = int(float(box.getElementsByTagName("ymin")[0].childNodes[0].data))
        x2 = int(float(box.getElementsByTagName("xmax")[0].childNodes[0].data))
        y2 = int(float(box.getElementsByTagName("ymax")[0].childNodes[0].data))
        # print("box:(c,xmin,ymin,xmax,ymax)", cls_name, x1, y1, x2, y2)

        img_box.append([cls_name, x1, y1, x2, y2])
    # test_dataset_box_feature(img_jpg_file_name, img_box)
    save_file(img_xml_file, [img_w, img_h], img_box)


def copy_data(img_set_source, img_labels_root, imgs_source, type):
    file_name = img_set_source + '/' + type + ".txt"
    file = open(file_name)

    # 判断文件夹是否存在，不存在则创建
    root_file = Path(FILE_ROOT + DEST_IMAGES_PATH + '/' + type)
    if not root_file.exists():
        print(f"Path {root_file} is not exit")
        os.makedirs(root_file)

    root_file = Path(FILE_ROOT + DEST_LABELS_PATH + '/' + type)
    if not root_file.exists():
        print(f"Path {root_file} is not exit")
        os.makedirs(root_file)

    # 遍历文件夹
    for line in file.readlines():
        # print(line)
        img_name = line.strip('\n')
        img_sor_file = imgs_source + '/' + img_name + '.jpg'
        label_sor_file = img_labels_root + '/' + img_name + '.txt'

        # 复制图片
        DICT_DIR = FILE_ROOT + DEST_IMAGES_PATH + '/' + type
        img_dict_file = DICT_DIR + '/' + img_name + '.jpg'
        copyfile(img_sor_file, img_dict_file)

        # 复制 label
        DICT_DIR = FILE_ROOT + DEST_LABELS_PATH + '/' + type
        img_dict_file = DICT_DIR + '/' + img_name + '.txt'
        copyfile(label_sor_file, img_dict_file)


if __name__ == '__main__':
    # 将文件进行 train 和 val 的区分
    # 用于生成测试集使用，目前还在测试阶段，暂时不发布
    img_set_root = IMAGE_SET_ROOT
    imgs_root = IMAGE_PATH
    img_labels_root = LABELS_ROOT
    if osp.isdir(img_labels_root) == False:
        os.makedirs(img_labels_root)
    os.makedirs(DEST_PPP)
    names = os.listdir(ANNOTATIONS_PATH)
    # real_names = []
    # for name in names:
    #     if name.split(".")[-1] == "xml":
    #         real_names.append(name.split(".")[0])
    # # print(real_names)
    # # print(real_names)
    # random.shuffle(real_names)
    # # print(real_names)
    # length = len(real_names)
    # split_point = int(length * 0.2)
    #
    # val_names = real_names[:split_point]
    # train_names = real_names[split_point:]
    #
    # # 开始生成文件
    # np.savetxt('data/val.txt', np.array(val_names), fmt="%s", delimiter="\n")
    # np.savetxt('data/test.txt', np.array(val_names), fmt="%s", delimiter="\n")
    # np.savetxt('data/train.txt', np.array(train_names), fmt="%s", delimiter="\n")
    # print("txt文件生成完毕，请放在VOC2012的ImageSets/Main的目录下")
    # 生成标签
    root = ANNOTATIONS_PATH
    files_all = os.listdir(root)
    files = []
    for file in files_all:
        if file.split(".")[-1] == "xml":
            files.append(file.split(".")[0])
    # print(len(files))
    for file in files:
        # print("file name: ", file)
        file_xml = file.split(".")
        # try:
        get_xml_data(root, file_xml[0])
        # except:
        #     print(file_xml[0])
        # break
    # copy_data(img_set_root, img_labels_root, imgs_root, "train")
    # copy_data(img_set_root, img_labels_root, imgs_root, "val")
    # copy_data(img_set_root, img_labels_root, imgs_root, "test")
    # data = list(np.loadtxt("tt100k.txt", dtype=str))
    # print(data)
    # print(len(data))
    print("数据转换已完成！")