YOLOv8旋转目标检测Yolov8n-obb详细实例+rolabelimg

一、Yolov8环境搭建

首先创建虚拟环境下载安装（其实就是yolov8的环境）再大概写一下步骤，没有想详细的看本人另外一篇：YOLOv8环境搭建_yolov8环境配置-CSDN博客

1、下载安装anaconda

2、创建虚拟环境

conda create -n my_yolov8 python=3.8.8

3、激活刚创建的虚拟环境

activate my_yolov8

4、 到官方网站下载yolo模型 ，下载好后解压

 https://github.com/ultralytics/ultralytics 

4、文件requirements.txt ，安装配置环境

pip install -r 自己的requirements路径\ultralytics-main\requirements.txt

requirements.txt内容：自己可以在下载的ultralytics-main下面新建一个txt

# Ultralytics requirements
# Usage: pip install -r requirements.txt
 
# Base ----------------------------------------
hydra-core>=1.2.0
matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.1.1
Pillow>=7.1.2
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.64.0
 
# Logging -------------------------------------
tensorboard>=2.4.1
# clearml
# comet
 
# Plotting ------------------------------------
pandas>=1.1.4
seaborn>=0.11.0
 
# Export --------------------------------------
# coremltools>=6.0  # CoreML export
# onnx>=1.12.0  # ONNX export
# onnx-simplifier>=0.4.1  # ONNX simplifier
# nvidia-pyindex  # TensorRT export
# nvidia-tensorrt  # TensorRT export
# scikit-learn==0.19.2  # CoreML quantization
# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
# tensorflowjs>=3.9.0  # TF.js export
# openvino-dev  # OpenVINO export
 
# Extras --------------------------------------
ipython  # interactive notebook
psutil  # system utilization
thop>=0.1.1  # FLOPs computation
# albumentations>=1.0.3
# pycocotools>=2.0.6  # COCO mAP
# roboflow
 
# HUB -----------------------------------------
GitPython>=3.1.24

二、数据集制作

1、安装制作标签软件

激活自己的虚拟环境先下载安装labelimg，要装rolabelimg要先装labelimg

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple  labelImg==1.8.6

在lib-site-packages-下有这两个了。

2.安装rolabelimg

去github下载项目rolabelimg，

链接：https://pan.baidu.com/s/1fthJMm6E0r2zEVaZOiF3Gw 
提取码：xikk，

解压后，进入项目中运行一下命令即可驱动，roLabelImg-master也可以放lib-site-packages

python 路径\roLabelImg.py

 用按键zxcv进行旋转调整角度

 

3、标签可训练格式转换

# 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

3.1 roxml2dotaxml2txt

x1,y1,x2,y2,x3,y3,x4,y4
import os
import xml.etree.ElementTree as ET
import math
 
cls_list = ['1', 'gj', 'ladder']  #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'E:\CodeProject\ultralytics-main-OBB\data_transfor\org_xml'
    dotaxml_path = r'E:\CodeProject\ultralytics-main-OBB\data_transfor\dota_xml'
    out_path = r'E:\CodeProject\ultralytics-main-OBB\data_transfor\dota_txt'
    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)

三、配置文件设置

Yolov8_OBB斜框训练自己的数据集手把手教学_yolov8 obb-CSDN博客

四、训练

下载模型预训练权重：

训练：

yolo obb train data=路径\datasets\my-dota8-obb.yaml model=yolov8s-obb.pt epochs=20 imgsz=640 device=0,1,2,3 

参考：

windows下python3安装rolabelimg或者labelimg2标注斜框-CSDN博客

Yolov8_obb（prob loss) 基于anchor_free的旋转框目标检测，剪枝，跟踪（ByteTracker）_yolov8 obb-CSDN博客