【保姆级教程】YOLOv8_OBB旋转目标检测：训练自己的数据集_obb数据集自动制作,图片自动旋转360度

一、YOLOV8环境准备

1.1 下载安装最新的YOLOv8代码

 仓库地址： https://github.com/ultralytics/ultralytics
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1.2 配置环境

  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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二、数据准备

2.1 安装roLabelImg标注软件（这里需要读者先预先安装好labelImg标注工具）

2.1.1 拉取roLabelImg源码

 仓库地址：https://github.com/cgvict/roLabelImg
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2.1.2 打开roLabelImg软件

步骤一：先使用Anaconda Prompt启动labeme标注工具

步骤二：然后，运行2.1.1节中roLabelImg文件下的roLabelImg.py文件

打开的roLabelImg标注软件界面如下：

2.2 标注自己的数据

2.3 数据转换

2.3.1 运行下面代码，将xml标签格式转为txt标签格式

# 文件名称   ：roxml_to_dota.py
# 功能描述   ：把rolabelimg标注的xml文件转换成dota能识别的xml文件，
#             再转换成dota格式的txt文件
#            把旋转框 cx,cy,w,h,angle，或者矩形框cx,cy,w,h,转换成四点坐标x1,y1,x2,y2,x3,y3,x4,y4
import os
import xml.etree.ElementTree as ET
import math
 
cls_list = ['zebracrossing']  #修改为自己的标签
 
 
def edit_xml(xml_file, dotaxml_file):
    """
    修改xml文件
    :param xml_file:xml文件的路径
    :return:
    """
 
    # dxml_file = open(xml_file,encoding='gbk')
    # tree = ET.parse(dxml_file).getroot()
 
    tree = ET.parse(xml_file)
    objs = tree.findall('object')
    for ix, obj in enumerate(objs):
        x0 = ET.Element("x0")  # 创建节点
        y0 = ET.Element("y0")
        x1 = ET.Element("x1")
        y1 = ET.Element("y1")
        x2 = ET.Element("x2")
        y2 = ET.Element("y2")
        x3 = ET.Element("x3")
        y3 = ET.Element("y3")
        # obj_type = obj.find('bndbox')
        # type = obj_type.text
        # print(xml_file)
 
        if (obj.find('robndbox') == None):
            obj_bnd = obj.find('bndbox')
            obj_xmin = obj_bnd.find('xmin')
            obj_ymin = obj_bnd.find('ymin')
            obj_xmax = obj_bnd.find('xmax')
            obj_ymax = obj_bnd.find('ymax')
            # 以防有负值坐标
            xmin = max(float(obj_xmin.text), 0)
            ymin = max(float(obj_ymin.text), 0)
            xmax = max(float(obj_xmax.text), 0)
            ymax = max(float(obj_ymax.text), 0)
            obj_bnd.remove(obj_xmin)  # 删除节点
            obj_bnd.remove(obj_ymin)
            obj_bnd.remove(obj_xmax)
            obj_bnd.remove(obj_ymax)
            x0.text = str(xmin)
            y0.text = str(ymax)
            x1.text = str(xmax)
            y1.text = str(ymax)
            x2.text = str(xmax)
            y2.text = str(ymin)
            x3.text = str(xmin)
            y3.text = str(ymin)
        else:
            obj_bnd = obj.find('robndbox')
            obj_bnd.tag = 'bndbox'  # 修改节点名
            obj_cx = obj_bnd.find('cx')
            obj_cy = obj_bnd.find('cy')
            obj_w = obj_bnd.find('w')
            obj_h = obj_bnd.find('h')
            obj_angle = obj_bnd.find('angle')
            cx = float(obj_cx.text)
            cy = float(obj_cy.text)
            w = float(obj_w.text)
            h = float(obj_h.text)
            angle = float(obj_angle.text)
            obj_bnd.remove(obj_cx)  # 删除节点
            obj_bnd.remove(obj_cy)
            obj_bnd.remove(obj_w)
            obj_bnd.remove(obj_h)
            obj_bnd.remove(obj_angle)
 
            x0.text, y0.text = rotatePoint(cx, cy, cx - w / 2, cy - h / 2, -angle)
            x1.text, y1.text = rotatePoint(cx, cy, cx + w / 2, cy - h / 2, -angle)
            x2.text, y2.text = rotatePoint(cx, cy, cx + w / 2, cy + h / 2, -angle)
            x3.text, y3.text = rotatePoint(cx, cy, cx - w / 2, cy + h / 2, -angle)
 
        # obj.remove(obj_type)  # 删除节点
        obj_bnd.append(x0)  # 新增节点
        obj_bnd.append(y0)
        obj_bnd.append(x1)
        obj_bnd.append(y1)
        obj_bnd.append(x2)
        obj_bnd.append(y2)
        obj_bnd.append(x3)
        obj_bnd.append(y3)
 
        tree.write(dotaxml_file, method='xml', encoding='utf-8')  # 更新xml文件
 
 
# 转换成四点坐标
def rotatePoint(xc, yc, xp, yp, theta):
    xoff = xp - xc;
    yoff = yp - yc;
    cosTheta = math.cos(theta)
    sinTheta = math.sin(theta)
    pResx = cosTheta * xoff + sinTheta * yoff
    pResy = - sinTheta * xoff + cosTheta * yoff
    return str(int(xc + pResx)), str(int(yc + pResy))
 
 
def totxt(xml_path, out_path):
    # 想要生成的txt文件保存的路径，这里可以自己修改
 
    files = os.listdir(xml_path)
    i = 0
    for file in files:
 
        tree = ET.parse(xml_path + os.sep + file)
        root = tree.getroot()
 
        name = file.split('.')[0]
 
        output = out_path + '\\' + name + '.txt'
        file = open(output, 'w')
        i = i + 1
        objs = tree.findall('object')
        for obj in objs:
            cls = obj.find('name').text
            box = obj.find('bndbox')
            x0 = int(float(box.find('x0').text))
            y0 = int(float(box.find('y0').text))
            x1 = int(float(box.find('x1').text))
            y1 = int(float(box.find('y1').text))
            x2 = int(float(box.find('x2').text))
            y2 = int(float(box.find('y2').text))
            x3 = int(float(box.find('x3').text))
            y3 = int(float(box.find('y3').text))
            if x0 < 0:
                x0 = 0
            if x1 < 0:
                x1 = 0
            if x2 < 0:
                x2 = 0
            if x3 < 0:
                x3 = 0
            if y0 < 0:
                y0 = 0
            if y1 < 0:
                y1 = 0
            if y2 < 0:
                y2 = 0
            if y3 < 0:
                y3 = 0
            for cls_index, cls_name in enumerate(cls_list):
                if cls == cls_name:
                    file.write("{} {} {} {} {} {} {} {} {} {}\n".format(x0, y0, x1, y1, x2, y2, x3, y3, cls, cls_index))
        file.close()
        # print(output)
        print(i)
 
 
if __name__ == '__main__':
    # -----**** 第一步：把xml文件统一转换成旋转框的xml文件 ****-----
    roxml_path = r'D:\data\yolov8_obb\xml'
    dotaxml_path = r'D:\data\yolov8_obb\xml'
    out_path = r'D:\data\yolov8_obb\xml'
    filelist = os.listdir(roxml_path)
    for file in filelist:
        edit_xml(os.path.join(roxml_path, file), os.path.join(dotaxml_path, file))
 
    # -----**** 第二步：把旋转框xml文件转换成txt格式 ****-----
    totxt(dotaxml_path, out_path)
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运行上面代码，就可以获得TXT格式标签文件

2.3.2 使用YOLOv8的标签转换工具

步骤一：在项目代码根目录下，创建下面的文件，并将图片和标签文件放到相应的文件夹中：

步骤二：由于官方源码的数据转换代码是用的VOC数据集，我们需要修改一下类别名，改成自己的数据集类别。修改ultralytics/ultralyticsdata/converter.py文件中的代码：

步骤三：运行下面的代码

import sys
 
sys.path.append('D:/study/cnn/yolo/ultralytics')
 
from ultralytics.data.converter import convert_dota_to_yolo_obb
convert_dota_to_yolo_obb('D:/study/cnn/yolo/ultralytics/data')
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运行后，会在data/labels/路径下，生成train,val两个文件夹，以及转换好的标签文件

三、配置文件设置

3.1 新建my-data8-obb.yaml

ultralytics\ultralytics\cfg\datasets路径下，创建my-data8-obb.yaml:

path: D:/study/cnn/yolo/ultralytics/data # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images

# Classes for DOTA 1.0
names:
  0: zebracrossing

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3.2 修改yolov8-obb.yaml

ultralytics\ultralytics\cfg\models\v8路径下，修改yolov8-obb.yaml：

四、训练

4.1 下载预训练权重

在YOLOv8 github上下载预训练权重：

4.2 训练

步骤一：修改ultralytics\ultralytics\cfg\default.yaml文件中的训练参数（根据自己的实际情况决定）
步骤二：执行下面代码：

from ultralytics import YOLO

# Load a model
model = YOLO('weights/yolov8n-obb.pt')  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data='D:/study/cnn/yolo/ultralytics/ultralytics/cfg/datasets/my-data8-obb.yaml', epochs=100, imgsz=640)
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五、验证

from ultralytics import YOLO
 
def main():
    model = YOLO(r'runs/obb/train/weights/best.pt')
    model.val(data='dota8-obb.yaml', imgsz=1024, batch=4, workers=4)
if __name__ == '__main__':
    main()
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六、推理

from ultralytics import YOLO

model = YOLO('D:/study/cnn/yolo/ultralytics/runs/obb/train5/weights/best.pt')
results = model('D:/study/cnn/yolo/ultralytics/data/images/train/1.png', save=True)
print(results[0].obb.xywhr[:,-1]*180/3.1415)
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