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