YoloV4训练自己的数据集(三)

上文已经测试GPU和OpenCV版本的YoloV4,本文主要介绍如何使用YoloV4建立自己的数据集并且把数据集转换为相应的格式。

1.建立数据集

如果你自己有数据集，可以直接跳过本部分。可以使用Labelimg对于自己的图片进行标注，关于Labelimg的安装，大家可以看这篇博客。标注之后会生成.xml文件，其中包含了图片路径、图片色彩通道、大小、标注框的大小。

2.建立数据集格式

Yolo使用PASCAL VOC数据集的目录结构，其结构如下：


目标检测仅使用Annotations以及JPEGImagets这两个文件夹。

3.转换并且划分数据集

需要把标注的.xml文件转换为Yolo的txt文本并且把数据集划分为训练集和测试集。我使用的Python3，程序如下：只需要把其中的 classes改为自己的类别。如果是多类别注意用英文逗号隔开。

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random

classes=["fire"]


def clear_hidden_files(path):
    dir_list = os.listdir(path)
    for i in dir_list:
        abspath = os.path.join(os.path.abspath(path), i)
        if os.path.isfile(abspath):
            if i.startswith("._"):
                os.remove(abspath)
        else:
            clear_hidden_files(abspath)

def convert(size, box):
    dw = 1./size[0]
    dh = 1./size[1]
    x = (box[0] + box[1])/2.0
    y = (box[2] + box[3])/2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

def convert_annotation(image_id):
    in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
    out_file = open('VOCdevkit/VOC2007/labels/%s.txt' %image_id, 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
    in_file.close()
    out_file.close()

wd = os.getcwd()
wd = os.getcwd()
work_sapce_dir = os.path.join(wd, "VOCdevkit/")
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
work_sapce_dir = os.path.join(work_sapce_dir, "VOC2007/")
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
if not os.path.isdir(annotation_dir):
        os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
if not os.path.isdir(image_dir):
        os.mkdir(image_dir)
clear_hidden_files(image_dir)
VOC_file_dir = os.path.join(work_sapce_dir, "ImageSets/")
if not os.path.isdir(VOC_file_dir):
        os.mkdir(VOC_file_dir)
VOC_file_dir = os.path.join(VOC_file_dir, "Main/")
if not os.path.isdir(VOC_file_dir):
        os.mkdir(VOC_file_dir)

train_file = open(os.path.join(wd, "2007_train.txt"), 'w')
test_file = open(os.path.join(wd, "2007_test.txt"), 'w')
train_file.close()
test_file.close()
VOC_train_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/train.txt"), 'w')
VOC_test_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/test.txt"), 'w')
VOC_train_file.close()
VOC_test_file.close()
if not os.path.exists('VOCdevkit/VOC2007/labels'):
    os.makedirs('VOCdevkit/VOC2007/labels')
train_file = open(os.path.join(wd, "2007_train.txt"), 'a')
test_file = open(os.path.join(wd, "2007_test.txt"), 'a')
VOC_train_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/train.txt"), 'a')
VOC_test_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/test.txt"), 'a')
list = os.listdir(image_dir) # list image files
probo = random.randint(1, 100)
print("Probobility: %d" % probo)
for i in range(0,len(list)):
    path = os.path.join(image_dir,list[i])
    if os.path.isfile(path):
        image_path = image_dir + list[i]
        voc_path = list[i]
        (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
        (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
        annotation_name = nameWithoutExtention + '.xml'
        annotation_path = os.path.join(annotation_dir, annotation_name)
    probo = random.randint(1, 100)
    print("Probobility: %d" % probo)
    if(probo < 75):
        if os.path.exists(annotation_path):
            train_file.write(image_path + '\n')
            VOC_train_file.write(voc_nameWithoutExtention + '\n')
            convert_annotation(nameWithoutExtention)
    else:
        if os.path.exists(annotation_path):
            test_file.write(image_path + '\n')
            VOC_test_file.write(voc_nameWithoutExtention + '\n')
            convert_annotation(nameWithoutExtention)
train_file.close()
test_file.close()
VOC_train_file.close()
VOC_test_file.close()

执行程序之后在darknet文件夹下会生成两个文件，分别是2007test.txt以及2007train.txt。（其中数据集中的75%划分为训练集，25%划分为测试集，这个划分可以自己修改。）
数据集的建立、转换与划分结束。下文介绍如何使用自己的数据集进行训练。