yolox训练自己的数据

Posted lishanlu136

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前言:此文是我从yolov5替换到yolox训练的过程,前提是我们有图片和标注文件,而且都是yolov5的txt格式的;之前在网上看了一圈,怎么用自己的数据训练yolox模型,都是需要把标注文件整理成voc格式或coco数据集格式,连文件夹的存放方式都必须一样,真是麻烦;而我之前的任务都是基于yolov5训练的,所以图片,标注文件已经有了,我也不想按voc,coco那样再去改变格式,于是就有了此文。

yolov5数据集目录如下:

步骤如下:

一、利用yolov5标注生成xml格式的标注

利用yolov5的txt格式的标注文件生成xml格式的标注文件,在生成的时候需注意:
1、yolov5的标注是经过归一化的c_x, c_y, w, h
2、背景图片yolov5可以不用标注,即没有对应的txt文件,但yolox训练却不行
3、图片名字不要带有空格,yolov5可以正常训练验证,但yolox在验证的时候会报错。
直接上生成xml的代码,文件名yolotxt2xml.py:

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2021/09/14 11:14
# @Author  : lishanlu
# @File    : yolotxt2xml.py
# @Software: PyCharm
# @Discription:

from __future__ import absolute_import, print_function, division
import os
from xml.dom.minidom import Document
import xml.etree.ElementTree as ET
import cv2


'''
import xml
xml.dom.minidom.Document().writexml()
def writexml(self,
             writer: Any,
             indent: str = "",
             addindent: str = "",
             newl: str = "",
             encoding: Any = None) -> None
'''


class YOLO2VOCConvert:
    def __init__(self, txts_path, xmls_path, imgs_path, classes_str_list):
        self.txts_path = txts_path   # 标注的yolo格式标签文件路径
        self.xmls_path = xmls_path   # 转化为voc格式标签之后保存路径
        self.imgs_path = imgs_path   # 读取读片的路径个图片名字,存储到xml标签文件中
        self.classes = classes_str_list  # 类别列表

    # 从所有的txt文件中提取出所有的类别, yolo格式的标签格式类别为数字 0,1,...
    # writer为True时,把提取的类别保存到'./Annotations/classes.txt'文件中
    def search_all_classes(self, writer=False):
        # 读取每一个txt标签文件,取出每个目标的标注信息
        all_names = set()
        txts = os.listdir(self.txts_path)
        # 使用列表生成式过滤出只有后缀名为txt的标签文件
        txts = [txt for txt in txts if txt.split('.')[-1] == 'txt']
        txts = [txt for txt in txts if not txt.split('.')[0] == "classes"]  # 过滤掉classes.txt文件
        print(len(txts), txts)
        # 11 ['0002030.txt', '0002031.txt', ... '0002039.txt', '0002040.txt']
        for txt in txts:
            txt_file = os.path.join(self.txts_path, txt)
            with open(txt_file, 'r') as f:
                objects = f.readlines()
                for object in objects:
                    object = object.strip().split(' ')
                    print(object)  # ['2', '0.506667', '0.553333', '0.490667', '0.658667']
                    all_names.add(int(object[0]))
            # print(objects)  # ['2 0.506667 0.553333 0.490667 0.658667\\n', '0 0.496000 0.285333 0.133333 0.096000\\n', '8 0.501333 0.412000 0.074667 0.237333\\n']

        print("所有的类别标签:", all_names, "共标注数据集:%d张" % len(txts))

        # 把从xmls标签文件中提取的类别写入到'./Annotations/classes.txt'文件中
        # if writer:
        #     with open('./Annotations/classes.txt', 'w') as f:
        #         for label in all_names:
        #             f.write(label + '\\n')

        return list(all_names)

    def yolo2voc(self):
        """
        可以转换图片和txtlabel数量不匹配的情况,即有些图片是背景
        :return:
        """
        # 创建一个保存xml标签文件的文件夹
        if not os.path.exists(self.xmls_path):
            os.makedirs(self.xmls_path)

        for img_name in os.listdir(self.imgs_path):
            # 读取图片的尺度信息
            print("读取图片:", img_name)
            try:
                img = cv2.imread(os.path.join(self.imgs_path, img_name))
                height_img, width_img, depth_img = img.shape
                print(height_img, width_img, depth_img)  # h 就是多少行(对应图片的高度), w就是多少列(对应图片的宽度)
            except Exception as e:
                print("%s read fail, %s"%(img_name, e))
                continue
            txt_name = img_name.replace(os.path.splitext(img_name)[1], '.txt')
            txt_file = os.path.join(self.txts_path, txt_name)
            all_objects = []
            if os.path.exists(txt_file):
                with open(txt_file, 'r') as f:
                    objects = f.readlines()
                    for object in objects:
                        object = object.strip().split(' ')
                        all_objects.append(object)
                        print(object)  # ['2', '0.506667', '0.553333', '0.490667', '0.658667']
            # 创建xml标签文件中的标签
            xmlBuilder = Document()
            # 创建annotation标签,也是根标签
            annotation = xmlBuilder.createElement("annotation")

            # 给标签annotation添加一个子标签
            xmlBuilder.appendChild(annotation)

            # 创建子标签folder
            folder = xmlBuilder.createElement("folder")
            # 给子标签folder中存入内容,folder标签中的内容是存放图片的文件夹,例如:JPEGImages
            folderContent = xmlBuilder.createTextNode(self.imgs_path.split('/')[-1])  # 标签内存
            folder.appendChild(folderContent)  # 把内容存入标签
            annotation.appendChild(folder)  # 把存好内容的folder标签放到 annotation根标签下

            # 创建子标签filename
            filename = xmlBuilder.createElement("filename")
            # 给子标签filename中存入内容,filename标签中的内容是图片的名字,例如:000250.jpg
            filenameContent = xmlBuilder.createTextNode(txt_name.split('.')[0] + '.jpg')  # 标签内容
            filename.appendChild(filenameContent)
            annotation.appendChild(filename)

            # 把图片的shape存入xml标签中
            size = xmlBuilder.createElement("size")
            # 给size标签创建子标签width
            width = xmlBuilder.createElement("width")  # size子标签width
            widthContent = xmlBuilder.createTextNode(str(width_img))
            width.appendChild(widthContent)
            size.appendChild(width)  # 把width添加为size的子标签
            # 给size标签创建子标签height
            height = xmlBuilder.createElement("height")  # size子标签height
            heightContent = xmlBuilder.createTextNode(str(height_img))  # xml标签中存入的内容都是字符串
            height.appendChild(heightContent)
            size.appendChild(height)  # 把width添加为size的子标签
            # 给size标签创建子标签depth
            depth = xmlBuilder.createElement("depth")  # size子标签width
            depthContent = xmlBuilder.createTextNode(str(depth_img))
            depth.appendChild(depthContent)
            size.appendChild(depth)  # 把width添加为size的子标签
            annotation.appendChild(size)  # 把size添加为annotation的子标签

            # 每一个object中存储的都是['2', '0.506667', '0.553333', '0.490667', '0.658667']一个标注目标
            for object_info in all_objects:
                # 开始创建标注目标的label信息的标签
                object = xmlBuilder.createElement("object")  # 创建object标签
                # 创建label类别标签
                # 创建name标签
                imgName = xmlBuilder.createElement("name")  # 创建name标签
                imgNameContent = xmlBuilder.createTextNode(self.classes[int(object_info[0])])
                imgName.appendChild(imgNameContent)
                object.appendChild(imgName)  # 把name添加为object的子标签

                # 创建pose标签
                pose = xmlBuilder.createElement("pose")
                poseContent = xmlBuilder.createTextNode("Unspecified")
                pose.appendChild(poseContent)
                object.appendChild(pose)  # 把pose添加为object的标签

                # 创建truncated标签
                truncated = xmlBuilder.createElement("truncated")
                truncatedContent = xmlBuilder.createTextNode("0")
                truncated.appendChild(truncatedContent)
                object.appendChild(truncated)

                # 创建difficult标签
                difficult = xmlBuilder.createElement("difficult")
                difficultContent = xmlBuilder.createTextNode("0")
                difficult.appendChild(difficultContent)
                object.appendChild(difficult)

                # 先转换一下坐标
                # (objx_center, objy_center, obj_width, obj_height)->(xmin,ymin, xmax,ymax)
                x_center = float(object_info[1]) * width_img + 1
                y_center = float(object_info[2]) * height_img + 1
                xminVal = int(
                    x_center - 0.5 * float(object_info[3]) * width_img)  # object_info列表中的元素都是字符串类型
                yminVal = int(y_center - 0.5 * float(object_info[4]) * height_img)
                xmaxVal = int(x_center + 0.5 * float(object_info[3]) * width_img)
                ymaxVal = int(y_center + 0.5 * float(object_info[4]) * height_img)

                # 创建bndbox标签(三级标签)
                bndbox = xmlBuilder.createElement("bndbox")
                # 在bndbox标签下再创建四个子标签(xmin,ymin, xmax,ymax) 即标注物体的坐标和宽高信息
                # 在voc格式中,标注信息:左上角坐标(xmin, ymin) (xmax, ymax)右下角坐标
                # 1、创建xmin标签
                xmin = xmlBuilder.createElement("xmin")  # 创建xmin标签(四级标签)
                xminContent = xmlBuilder.createTextNode(str(xminVal))
                xmin.appendChild(xminContent)
                bndbox.appendChild(xmin)
                # 2、创建ymin标签
                ymin = xmlBuilder.createElement("ymin")  # 创建ymin标签(四级标签)
                yminContent = xmlBuilder.createTextNode(str(yminVal))
                ymin.appendChild(yminContent)
                bndbox.appendChild(ymin)
                # 3、创建xmax标签
                xmax = xmlBuilder.createElement("xmax")  # 创建xmax标签(四级标签)
                xmaxContent = xmlBuilder.createTextNode(str(xmaxVal))
                xmax.appendChild(xmaxContent)
                bndbox.appendChild(xmax)
                # 4、创建ymax标签
                ymax = xmlBuilder.createElement("ymax")  # 创建ymax标签(四级标签)
                ymaxContent = xmlBuilder.createTextNode(str(ymaxVal))
                ymax.appendChild(ymaxContent)
                bndbox.appendChild(ymax)

                object.appendChild(bndbox)
                annotation.appendChild(object)  # 把object添加为annotation的子标签
            f = open(os.path.join(self.xmls_path, txt_name.split('.')[0] + '.xml'), 'w')
            xmlBuilder.writexml(f, indent='\\t', newl='\\n', addindent='\\t', encoding='utf-8')
            f.close()


if __name__ == '__main__':
    imgs_path1 = 'F:/Dataset/road/images/val'        # ['train', 'val']
    txts_path1 = 'F:/Dataset/road/labels/val'        # ['train', 'val']
    xmls_path1 = 'F:/Dataset/road/xmls/val'          # ['train', 'val']
    classes_str_list = ['road_crack','road_sag']     # class name

    yolo2voc_obj1 = YOLO2VOCConvert(txts_path1, xmls_path1, imgs_path1, classes_str_list)
    labels = yolo2voc_obj1.search_all_classes()
    print('labels: ', labels)
    yolo2voc_obj1.yolo2voc()

将train和val都转换生成后,目录格式如下:

二、定义数据读取文件

整个YOLOX的工程,训练过程,要想有一个大概浏览,可以见我的另一篇文章yolox训练解析
进入到YOLOX主目录
在yolox/data/datasets/目录下定义了数据的读取方式,有按coco方式读取,有按voc方式读取,另外mosaic增强也定义在这个文件夹下,我们添加新的读取方式就在这个目录下添加,添加yolo_style.py文件,代码如下:

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2021/12/23 9:13
# @Author  : lishanlu
# @File    : yolo_style.py
# @Software: PyCharm
# @Discription: 读入yolox风格的xmls数据

from __future__ import absolute_import, print_function, division
import os
import os.path
import pickle
import xml.etree.ElementTree as ET

import cv2
import numpy as np

from yolox.evaluators.voc_eval import voc_eval

from .datasets_wrapper import Dataset
from pathlib import Path
import glob
from tqdm import tqdm
from PIL import Image, ExifTags
import torch


class AnnotationTransform(object):

    """Transforms a annotation into a Tensor of bbox coords and label index
    Initilized with a dictionary lookup of classnames to indexes

    Arguments:
        classes_name: (str, str, ...): dictionary lookup of classnames -> indexes
        keep_difficult (bool, optional): keep difficult instances or not
            (default: False)
        height (int): height
        width (int): width
    """
    def __init__(self, classes_name, keep_difficult=True):
        self.class_to_ind = dict(zip(classes_name, range(len(classes_name))))
        self.keep_difficult = keep_difficult

    def __call__(self, target):
        """
        Arguments:
            target (annotation) : the target annotation to be made usable
                will be an ET.Element
        Returns:
            a list containing lists of bounding boxes  [bbox coords, class name]
        """
        res = np.empty((0, 5))
        for obj in target.iter("object"):
            difficult = obj.find("difficult")
            if difficult is not None:
                difficult = int(difficult.text) == 1
            else:
                difficult = False
            if not self.keep_difficult and difficult:
                continue
            name = obj.find("name").text.strip()
            bbox = obj.find("bndbox")

            pts = ["xmin", "ymin", "xmax", "ymax"]
            bndbox = []
            for i, pt in enumerate(pts):
                cur_pt = int(bbox.find(pt).text) - 1
                # scale height or width
                # cur_pt = cur_pt / width if i % 2 == 0 else cur_pt / height
                bndbox.append(cur_pt)
            label_idx = self.class_to_ind[name]
            bndbox.append(label_idx)
            res = np.vstack((res, bndbox))  # [xmin, ymin, xmax, ymax, label_ind]
            # img_id = target.find('filename').text[:-4]
        width = int(target.find("size").find("width").text)
        height = int(target.find("size").find("height").text)
        img_info = (height, width)

        return res, img_info


"""
generation yolo style dataloader.
"""
img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp']  # acceptable image suffixes
# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def img2xml_paths(img_paths):
    # Define xml paths as a function of image paths
    sa, sb = os.sep + 'images' + os.sep, os.sep + 'xmls' + os.sep  # /images/, /xmls/ substrings
    return ['xml'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths]


def get_hash(files):
    # Returns a single hash value of a list of files
    return sum(os.path.getsize(f) for f in files if os.path.isfile(f))


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    try:
        rotation = dict(img._getexif().items())[orientation]
        if rotation == 6:  # rotation 270
            s = (s[1], s[0])
        elif rotation == 8:  # rotation 90
            s = (s[1], s[0])
    except:
        pass

    return s


def xyxy2xywh(x):
    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
    y[YOLOX训练自己的数据集(超详细)

yolox训练自己的数据

yolox训练自己的数据

yolox训练自己的数据

yolox训练自己的数据

[深度学习][原创]yolox快速训练自己数据集方法