一、Yolov8环境搭建
1.下载最新的Yolov8-obb代码：
https://github.com/ultralytics/ultralytics
2.安装配置环境
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple二、数据集制作
1.标注软件：roLabelImg
roLabelImg是基于labelImg改进的，是用来标注为VOC格式的数据，但是在labelImg的基础上增加了能够使标注的框进行旋转的功能。
2.数据格式转换
在ultralytics-main下新建一个文件夹myData设置如下结构:
其中，train_original和val_original为dota格式的标注文件，labels/train和labels/val为空，在执行完dota2obb步骤后，labels/train和labels/val则保存转换后可训练的obb格式的标注文件。
2.1 roxml2dota
# 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 = ['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)
2.2 dota2obb
import sys
sys.path.append('/home/code/wll/code/ultralytics-main-OBB/ultralytics')
from ultralytics.data.converter import convert_dota_to_yolo_obb
convert_dota_to_yolo_obb('/home/code/wll/code/ultralytics-main-OBB/myData')
#关于dataobb文件下的目录下面会详细说明三、配置文件设置
3.1 新建my-dota8-obb.yaml
3.2 修改yolov8-obb.yaml
只需将nc修改为自己的类别数量。
四、训练
4.1 下载预训练权重
4.2 训练
yolo obb train data=/home/code/wll/code/ultralytics-main-OBB/ultralytics/cfg/datasets/my-dota8-obb.yaml model=yolov8s-obb.pt epochs=200 imgsz=640 device=0,1,2,3五、验证
yolo obb val model=best-obb.pt data=/home/code/wll/code/ultralytics-main-OBB/ultralytics/cfg/datasets/my-dota8-obb.yaml 六、推理
yolo obb predict model=yolov8n-obb.pt source='https://ultralytics.com/images/bus.jpg'七、导出为onnx
yolo export model=yolov8n-obb.pt format=onnx
参考博客：
全网首发！Yolov8_obb旋转框训练、测试、推理手把手教学（DOTA1.0数据集map50已达80%）_yolov8 obb-CSDN博客