一、数据集准备
数据集下载地址：https://github.com/YimianDai/sirst
1. 需要将数据集转换为YOLO所需要的txt格式
参考链接：https://github.com/pprp/voc2007_for_yolo_torch
1.1 检测图片及其xml文件
import os, shutil
def checkPngXml(dir1, dir2, dir3, is_move=True):
"""
dir1 是图片所在文件夹
dir2 是标注文件所在文件夹
dir3 是创建的，如果图片没有对应的xml文件，那就将图片放入dir3
is_move 是确认是否进行移动，否则只进行打印
"""
if not os.path.exists(dir3):
os.mkdir(dir3)
cnt = 0
for file in os.listdir(dir1):
f_name,f_ext = file.split(".")
if not os.path.exists(os.path.join(dir2, f_name+".xml")):
print(f_name)
if is_move:
cnt += 1
shutil.move(os.path.join(dir1,file), os.path.join(dir3, file))
if cnt > 0:
print("有%d个文件不符合要求，已打印。"%(cnt))
else:
print("所有图片和对应的xml文件都是一一对应的。")
if __name__ == "__main__":
dir1 = r"dataset/images/images" # 修改为自己的图片路径
dir2 = r"dataset/masks/masks" # 修改为自己的图片路径
dir3 = r"dataset/Allempty" # 修改为自己的图片路径
checkPngXml(dir1, dir2, dir3, False)1.2 划分训练集
import os
import random
import os, fnmatch
trainval_percent = 0.8
train_percent = 0.8
xmlfilepath = r"dataset/masks/masks"
txtsavepath = r"dataset"
total_xml = fnmatch.filter(os.listdir(xmlfilepath), '*.xml')
print(total_xml)
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('dataset/trainval.txt', 'w')
ftest = open('dataset/test.txt', 'w')
ftrain = open('dataset/train.txt', 'w')
fval = open('dataset/val.txt', 'w')
for i in list:
name=total_xml[i][:-4]+'\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftrain.write(name)
else:
fval.write(name)
else:
ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()1.3 转为txt标签
# -*- coding: utf-8 -*-
"""
需要修改的地方：
1. sets中替换为自己的数据集
2. classes中替换为自己的类别
3. 将本文件放到VOC2007目录下
4. 直接开始运行
"""
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] #替换为自己的数据集
classes = ["Target"] #修改为自己的类别
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(year, image_id):
in_file = open('dataset/masks/masks/%s.xml'%(image_id)) #将数据集放于当前目录下
out_file = open('dataset/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)
print(w,h)
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)
print(cls_id)
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)
print(bb)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('dataset/labels/'):
os.makedirs('dataset/labels/')
image_ids = open('dataset/%s.txt'%(image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w')
for image_id in image_ids:
list_file.write('dataset/images/images/%s.png\n'%(image_id))
convert_annotation(year, image_id)
list_file.close()
# os.system("cat 2007_train.txt 2007_val.txt > train.txt") #修改为自己的数据集用作训练1.4 构造数据集
import os, shutil
"""
需要满足以下条件：
1. 在JPEGImages中准备好图片
2. 在labels中准备好labels
3. 创建好如下所示的文件目录：
- images
- train2014
- val2014
- labels(由于voc格式中有labels文件夹，所以重命名为label)
- train2014
- val2014
"""
def make_for_torch_yolov3(dir_image,
dir_label,
dir1_train,
dir1_val,
dir2_train,
dir2_val,
main_trainval,
main_test):
if not os.path.exists(dir1_train):
os.mkdir(dir1_train)
if not os.path.exists(dir1_val):
os.mkdir(dir1_val)
if not os.path.exists(dir2_train):
os.mkdir(dir2_train)
if not os.path.exists(dir2_val):
os.mkdir(dir2_val)
with open(main_trainval, "r") as f1:
for line in f1:
print(line[:-1])
# print(os.path.join(dir_image, line[:-1]+".png"), os.path.join(dir1_train, line[:-1]+".png"))
shutil.copy(os.path.join(dir_image, line[:-1]+".png"),
os.path.join(dir1_train, line[:-1]+".png"))
shutil.copy(os.path.join(dir_label, line[:-1]+".txt"),
os.path.join(dir2_train, line[:-1]+".txt"))
with open(main_test, "r") as f2:
for line in f2:
print(line[:-1])
shutil.copy(os.path.join(dir_image, line[:-1]+".png"),
os.path.join(dir1_val, line[:-1]+".png"))
shutil.copy(os.path.join(dir_label, line[:-1]+".txt"),
os.path.join(dir2_val, line[:-1]+".txt"))
if __name__ == "__main__":
'''
https://github.com/ultralytics/yolov3
这个pytorch版本的数据集组织
- images
- train2014 # dir1_train
- val2014 # dir1_val
- labels
- train2014 # dir2_train
- val2014 # dir2_val
trainval.txt, test.txt 是由create_main.py构建的
'''
dir_image = r"dataset/images/images"
dir_label = r"dataset/labels"
dir1_train = r"dataset/image/train2014"
dir1_val = r"dataset/image/val2014"
dir2_train = r"dataset/label/train2014"
dir2_val = r"dataset/label/val2014"
main_trainval = r"dataset/trainval.txt"
main_test = r"dataset/test.txt"
make_for_torch_yolov3(dir_image,
dir_label,
dir1_train,
dir1_val,
dir2_train,
dir2_val,
main_trainval,
main_test)最终数据集格式如下：
2. 构造训练所需要的数据集
根据以上数据集 需要单独构建一个datasets文件夹，存放标签和图像，具体格式如下：
可以参考该链接：https://github.com/ultralytics/yolov5/issues/7389
3. 构建数据配置文档，需要注意 YOLOv5目录需要和datasets目录同级。
命名为hongwai.yaml
# YOLOv3 🚀 by Ultralytics, AGPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: python train.py --data coco.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here (20.1 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco # dataset root dir
train: images/train2014 # train images (relative to 'path') 118287 images
val: images/val2014 # val images (relative to 'path') 5000 images
test: images/val2014 # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# Classes
nc: 1
names:
0: Target二、矩池云配置环境
1. 租用环境
2、 配置环境，缺啥配啥，耐心解决问题
参考命令：
pip install -r requirements.txt也许训练过程中还会报错找不到module，根据module名字，使用pip安装即可
三、训练
YOLOv5训练命令：
python train.py --data data/hongwai.yaml --weights '' --cfg yolov5s.yaml --img 640 --device 0YOLOv3训练命令：
python train.py --data data/hongwai.yaml --weights '' --cfg yolov3.yaml --img 640 --device 0训练结果部分展示：
四、文件夹检测
执行命令：
python detect.py --weights runs/train/exp10/weights/best.pt --source dataset/image/val2014结果保存位置：
【创造不易，需要指导做该项目的可以联系】
