YOLOV3 --BUG---No labels in D:yolov5 rain_data rain.cache. Can not train without labels.

Posted 2022-03-01 浩瀚之水_csdn

采坑：
No labels in D:\\yolov5\\train_data\\train.cache. Can not train without labels.
参考：https://blog.csdn.net/qq_44787464/article/details/99736670

解决办法：

STEP1:
一定要按照这个顺序：
新建Annotations(存放voc格式的xml）
新建JPEGImages(存放训练的图片）
新建ImageSets ,labels (这两个文件为空)
将JPEGImages的图片复制到images中

STEP2：
在工程的根目录下添加makeTxt.py文件,并执行
 

import os
import random
 
trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = 'data/Annotations'
txtsavepath = 'data/ImageSets'
total_xml = os.listdir(xmlfilepath)
 
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
 
ftrainval = open('data/ImageSets/trainval.txt', 'w')
ftest = open('data/ImageSets/test.txt', 'w')
ftrain = open('data/ImageSets/train.txt', 'w')
fval = open('data/ImageSets/val.txt', 'w')
 
for i in list:
    name = total_xml[i][:-4] + '\\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftest.write(name)
        else:
            fval.write(name)
    else:
        ftrain.write(name)
 
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

STEP3：

在工程根目录下新建voc_label.py,并执行（注意！！！里面的标签名要改成自己训练标签，否则labels里面的txt文件为空）

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
 
sets = ['train', 'test','val']
 ################################这里修改为自己的标签名###############
classes = ["RBC"]#我们只是检测细胞，因此只有一个类别
 
 
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('data/Annotations/%s.xml' % (image_id))
    out_file = open('data/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')
 
 
wd = getcwd()
print(wd)
for image_set in sets:
    if not os.path.exists('data/labels/'):
        os.makedirs('data/labels/')
    image_ids = open('data/ImageSets/%s.txt' % (image_set)).read().strip().split()
    list_file = open('data/%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        list_file.write('data/images/%s.jpg\\n' % (image_id))
        convert_annotation(image_id)
    list_file.close()

 

得到labels的具体内容以及data目录下的train.txt，test.txt，val.txt

STEP4：

创建自己yaml文件，在data目录下：
RBC.yaml

train: /home/zyc/anaconda3/envs/yolov3-master/data/train.txt 
val: /home/zyc/anaconda3/envs/yolov3-master/data/val.txt  
test: /home/zyc/anaconda3/envs/yolov3-master/data/test.txt  

# number of classes
nc: 1

# class names
names: [ 'RBC' ]

最后在train.py