如何转换为YOLO txt格式

YOLO训练的label bbox格式是txt文档，如果是PASCAL VOC XML格式的文档或者其他类型文档，需要另外转换格式。

YOLO格式要求

YOLO txt文档格式，它是由class id,归一化后的center_x,center_y中心坐标以及归一化后的w,h组成，如下图所示：

<class_id>  <center_x> <center_y> <w> <h>

如果$x_{min}、x_{max}、y_{min}、y_{max}$分别表示标注框的x轴方向的最小最大值和y轴方向的最小最大值，则计算公式如下：

WIDER FACE label转换

接下来介绍WIDER FACE数据集的label格式，是由图片位置路径，边框的数量以及边框的属性组成，其中边框的属性有：x1, y1, w, h, blur, expression, illumination, invalid, occlusion, pose
数据集所在地址：http://shuoyang1213.me/WIDERFACE/

• x1, y1 是指BBox的左上角坐标，w, h是指BBox的宽高
• blur 是指照片的模糊程度: 0清晰、1一般、2严重
• expression 是指照片的表情: 0正常、1夸张
• illumination 是指照片的曝光程度: 0正常、1极度
• invalid 是指照片是否无效: 0否、1是
• occlusion 是指照片是否有被遮挡: 0无、1部分、2大量
• pose 是指照片的姿势: 0正常，1非典型
  
  $$WIDERFACE数据集的label格式$$
  了解格式后就可以做转换啦~~或是也可以直接使用以下我提供的代码，我的目录格式如下。其中 cfg 是自己创建的文件夹，用来放生成的 train.txt, val.txt (会写入所有的训练集路径)。而 wider_face_split, WIDER_train, WIDER_val 则是 WIDER FACE 数据集的训练集、验证集、label。
  
  因為我的数据集类别只有一个，所以第80行 f.write(‘0 %s %s %s %s\n’ % (x, y, w, h)) 的第一个参数是0

import os
import shutil
import cv2

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 run_convert(data_file, wider_train, yolo_path, file_info_name, write_txt):
    now_path = os.getcwd()
    data_counter = 0

    with open(data_file, 'r') as f:
        print("read file...")
        data = f.readlines()
        
        for data_line in data:
            data_line = data_line.strip()
            data_info = data_line.split('/')

            # the image name
            if len(data_info) == 2:
                label_0_counter = 1
                data_info_path = os.path.join(data_info[0], data_info[1])
                data_path = os.path.join(wider_train, data_info_path)

                # copy image to yolo path and rename
                shutil.copyfile(data_path, yolo_path + str(data_counter) + '.jpg')
                
                image = cv2.imread(data_path)
                image_size = [image.shape[1],image.shape[0]]
                
                # image --> rename
                with open(file_info_name, 'a') as f:
                    line_txt = [data_info_path, ' --> ', yolo_path + str(data_counter) + '.jpg', '\n']
                    f.writelines(line_txt)

                with open(write_txt, 'a') as f:
                    path = os.path.join(now_path, yolo_path)
                    line_txt = [path + str(data_counter) + '.jpg', '\n']
                    f.writelines(line_txt)
                
                data_counter += 1
                label_list = []
                # process other info
                sub_count = 1
                continue

            # the count of bndBox
            if sub_count == 1:
                sub_count += 1
                continue

            # bndBox info
            print("process ", label_0_counter, " bndBox info...")
            if sub_count >= 2:
                label_0_counter += 1
                info_list = data_line.split(' ')
#                 print("WIDER FACE(x1, y1, w, h): ", info_list[0], info_list[1], info_list[2], info_list[3])
                
                xmin = int(info_list[0])
                xmax = int(info_list[0])+int(info_list[2])
                ymin = int(info_list[1])
                ymax = int(info_list[1])+int(info_list[3])
                
                box = [xmin, xmax, ymin, ymax]
                x, y, w, h = convert(image_size,box)
#                 print("YOLO txt(x, y, w, h): ", x, y, w, h)
                
                with open(yolo_path + str(data_counter-1) + '.txt', 'a+') as f:
                    f.write('0 %s %s %s %s\n' % (x, y, w, h))
                    
    print('the file is processed')


wider_train = "WIDER_train/images"
yolo_path = "yolo_train/"
data_file = "wider_face_split/wider_face_train_bbx_gt.txt"
file_info_name = 'file_info_train.txt'
write_txt = 'cfg/train.txt'

# wider_train = "WIDER_val/images"
# yolo_path = "yolo_val/"
# data_file = "wider_face_split/wider_face_val_bbx_gt.txt"
# file_info_name = 'file_info_val.txt'
# write_txt = 'cfg/val.txt'

if not os.path.exists(yolo_path):
    os.mkdir(yolo_path)
else:
    lsdir = os.listdir(yolo_path)
    for name in lsdir:
        if name.endswith('.txt') or name.endswith('.jpg'):
            os.remove(os.path.join(yolo_path, name))

if os.path.exists(file_info_name):
    file=open(file_info_name, 'w')
    
if os.path.exists(write_txt):
    file=open(write_txt, 'w')

run_convert(data_file, wider_train, yolo_path, file_info_name, write_txt)

转换好的格式会放在 yolo_train, yolo_val 文件夹里，所有 train, validate 的图片路径 txt文档则会放在cfg文件夹里(train.txt, val.txt)。转换完就可以开始训练了。

PASCAL VOC XML转换

以下就是 VOC xml 的格式， 里的 是指照片的 width, height, depth， 是检测目标信息: 类别 name,BBox的xmin, xmax, ymin, ymax，也就是指 BBox 的 x, y 坐标的最小与最大值。

<annotation>
 <folder>VOC2012folder>
 <filename>2007_000027.jpgfilename>
 <source>
  <database>The VOC2007 Databasedatabase>
  <annotation>PASCAL VOC2007annotation>
  <image>flickrimage>
 source>
 <size>
  <width>486width>
  <height>500height>
  <depth>3depth>
 size>
 <segmented>0segmented>
 <object>
  <name>personname>
  <pose>Unspecifiedpose>
  <truncated>0truncated>
  <difficult>0difficult>
  <bndbox>
   <xmin>174xmin>
   <ymin>101ymin>
   <xmax>349xmax>
   <ymax>351ymax>
  bndbox>
  <part>
   <name>headname>
   <bndbox>
    <xmin>169xmin>
    <ymin>104ymin>
    <xmax>209xmax>
    <ymax>146ymax>
   bndbox>
  part>
  <part>
   <name>handname>
   <bndbox>
    <xmin>278xmin>
    <ymin>210ymin>
    <xmax>297xmax>
    <ymax>233ymax>
   bndbox>
  part>
  <part>
   <name>footname>
   <bndbox>
    <xmin>273xmin>
    <ymin>333ymin>
    <xmax>297xmax>
    <ymax>354ymax>
   bndbox>
  part>
  <part>
   <name>footname>
   <bndbox>
    <xmin>319xmin>
    <ymin>307ymin>
    <xmax>340xmax>
    <ymax>326ymax>
   bndbox>
  part>
 object>
annotation>

接着就可以使用以下代码来转换：
要将第57行 all_classes设定为要预测的类别：

import os
import shutil
from bs4 import BeautifulSoup

def run_convert(all_classes, train_img, train_annotation, yolo_path, write_txt):
    now_path = os.getcwd()
    data_counter = 0

    for data_file in os.listdir(train_annotation):
        try:
            with open(os.path.join(train_annotation, data_file), 'r') as f:
                print("read file...")
                soup = BeautifulSoup(f.read(), 'xml')
                img_name = soup.select_one('filename').text

                for size in soup.select('size'):
                    img_w = int(size.select_one('width').text)
                    img_h = int(size.select_one('height').text)
                    
                img_info = []
                for obj in soup.select('object'):
                    xmin = int(obj.select_one('xmin').text)
                    xmax = int(obj.select_one('xmax').text)
                    ymin = int(obj.select_one('ymin').text)
                    ymax = int(obj.select_one('ymax').text)
                    objclass = all_classes.get(obj.select_one('name').text)

                    x = (xmin + (xmax-xmin)/2) * 1.0 / img_w
                    y = (ymin + (ymax-ymin)/2) * 1.0 / img_h
                    w = (xmax-xmin) * 1.0 / img_w
                    h = (ymax-ymin) * 1.0 / img_h
                    img_info.append(' '.join([str(objclass), str(x),str(y),str(w),str(h)]))

                # copy image to yolo path and rename
                img_path = os.path.join(train_img, img_name)
                img_format = img_name.split('.')[1]  # jpg or png
                shutil.copyfile(img_path, yolo_path + str(data_counter) + '.' + img_format)
                
                # create yolo bndbox txt
                with open(yolo_path + str(data_counter) + '.txt', 'a+') as f:
                    f.write('\n'.join(img_info))

                # create train or val txt
                with open(write_txt, 'a') as f:
                    path = os.path.join(now_path, yolo_path)
                    line_txt = [path + str(data_counter) + '.' + img_format, '\n']
                    f.writelines(line_txt)

                data_counter += 1
                    
        except Exception as e:
            print(e)
           
    print('the file is processed')


all_classes = {'class_2': 2, 'class_1': 1, 'class_0': 0}
train_img = "train/image"
train_annotation = "train/annotation"
yolo_path = "yolo_train/"
write_txt = 'cfg/train.txt'

if not os.path.exists(yolo_path):
    os.mkdir(yolo_path)
else:
    lsdir = os.listdir(yolo_path)
    for name in lsdir:
        if name.endswith('.txt') or name.endswith('.jpg') or name.endswith('.png'):
            os.remove(os.path.join(yolo_path, name))

cfg_file = write_txt.split('/')[0]
if not os.path.exists(cfg_file):
    os.mkdir(cfg_file)
    
if os.path.exists(write_txt):
    file=open(write_txt, 'w')

run_convert(all_classes, train_img, train_annotation, yolo_path, write_txt)

转换好的格式会放在yolo_train, yolo_val文件夹里，所有 train, validate 的图片路径txt文档会放在cfg文件夹里(train.txt, val.txt)。

labelme转YOLO格式

我的labelme是自己改过的，如果报 label_file.imageWidth错误，请自己读一下图片的宽高。用法：直接改一下dirpath,dstpath路径即可,dirpath里面可以有子文件夹，最后dstpath里面的标定内容是从0开始顺序编号的文件。

import os
import io
from io import BytesIO
import uuid
import PIL.Image
import codecs
import threadpool
import numpy as np
import cv2
import base64
from math import radians,fabs,sin,cos
from labelme.label_file import *
from labelme import utils
import shutil

dirpath = r'xxxxx'
dst_path_dir = r'xxxxx'


files = [os.path.join(y, file) for y, z, x in os.walk(dirpath)
         for file in x if os.path.splitext(file)[1] == '.json']

if not os.path.exists(dst_path_dir):
    os.makedirs(dst_path_dir)

index_files = []
for i in range(len(files)):
    index_files.append([i, files[i]])

def ThreadFun_c1(file):
    index = file[0]
    file = file[1]
    print(file)
    txtname = os.path.join(dst_path_dir, '{}.txt'.format(index))
    label_file = LabelFile()
    label_file.load(file)
    shutil.copy(file.replace('.json','.jpg'), txtname.replace('.txt','.jpg'))
    with codecs.open(txtname,'w','GBK') as f:
        for shape in label_file.shapes:
            x1 = shape['points'][0][0]/label_file.imageWidth
            y1 = shape['points'][0][1]/label_file.imageHeight
            x2 = shape['points'][1][0]/label_file.imageWidth
            y2 = shape['points'][1][1]/label_file.imageHeight
            f.write('{} {} {} {} {}\n'.format(shape['label'],(x1+x2)/2,(y1+y2)/2,abs(x1-x2),abs(y1-y2)))


# 定义了一个线程池，最多创建8个线程
pool = threadpool.ThreadPool(16)
# 创建要开启多线程的函数，以及函数相关参数和回调函数，其中回调数可以不写，default是none
requests = threadpool.makeRequests(ThreadFun_c1, index_files)
# 将所有要运行多线程的请求扔进线程池
[pool.putRequest(req) for req in requests]
# 所有的线程完成工作后退出
pool.wait()

'''
检查标注文件对不对
'''
for file in index_files:
    index = file[0]
    file = file[1]
    txtname = os.path.join(dst_path_dir, '{}.txt'.format(index))
    imgfile = txtname.replace('.txt','.jpg')
    img = cv2.imread(imgfile)
    h, w= img.shape[:2]
    with codecs.open(txtname,'r','GBK') as f:
        liness = f.readlines()
        for line in liness:
            lines = line.strip('\n').split(' ')
            for i in range(1,5):
                lines[i] = float(lines[i])
            x1 = (lines[1] - lines[3] /2) * w
            x2 = (lines[1] + lines[3]/2)*w
            y1 = (lines[2] - lines[4]/2)*h
            y2 = (lines[2] + lines[4]/2)*h

            x1 = int(x1)
            x2 = int(x2)
            y1 = int(y1)
            y2 = int(y2)

            if lines[0] == '0':
                color = (0,255,0)
            else:
                color = (0,0,255)

            cv2.rectangle(img,(x1,y1),(x2,y2),color)
    cv2.imshow('1',img)
    cv2.waitKey()

参考目录

https://blog.csdn.net/qq_16952303/article/details/114702534
https://medium.com/ching-i/%E5%A6%82%E4%BD%95%E8%BD%89%E6%8F%9B%E7%82%BAyolo-txt%E6%A0%BC%E5%BC%8F-f1d193736e5c
https://blog.csdn.net/qq_40622955/article/details/115733954