Based on DOTA_devkit.
Add some modules to trans DOTA annotation format to YOLO annotation format.
Add some files for every demo.

• DOTA.py Load image, and show the bounding oriented box.

• ImgSplit.py Split image and the label.

• ResultMerge.py Merge the detection result annotation txt.

• dota_×_evaluation_task×.py Evaluate the detection result annotation txt.

• YOLO_Transformer.py Trans DOTA format to YOLO(OBB or HBB) format.

• Draw_DOTA_YOLO.py Picture the YOLO_OBB labels(after augmented).

Same as DOTA_devkit. Then:

$  pip install -r requirements.txt

想要了解这几个函数实现的细节和原理可以看我的知乎文章;
DOTA遥感数据集以及相关工具DOTA_devkit的整理(踩坑记录);
DOTA数据格式转YOLO数据格式工具(cv2.minAreaRect踩坑记录);

• DOTA.py

$  python DOTA.py

• ImgSplit.py

$  python ImgSplit_multi_process.py

• ResultMerge.py

$  python ResultMerge.py

• dota_v1.5_evaluation_task1.py

change the path with yours.

detpath = r'/.../evaluation_example/result_classname/Task1_{:s}.txt'
annopath = r'/.../evaluation_example/row_DOTA_labels/{:s}.txt'
imagesetfile = r'/.../evaluation_example/imgnamefile.txt'

$  python dota_v1.5_evaluation_task1.py

• YOLO_Transform.py

$  python YOLO_Transform.py

DOTA format:    poly classname diffcult
    To
YOLO HBB format: classid x_c y_c width height   ——   def dota2Darknet()
longside format： classid x_c y_c longside shortside Θ  Θ∈[0, 180)  ——  def dota2LongSideFormat()

• Draw_DOTA_YOLO.py

1.Run YOLO_Transformer.py to get the YOLO_OBB_labels first.

2.then augment YOLO_OBB_labels and visualize it:

$  Draw_DOTA_YOLO.py

在使用中有任何问题，欢迎反馈给我，可以用以下联系方式跟我交流

• 知乎（@略略略）
• 代码问题提issues,其他问题请知乎上联系

感谢以下的项目,排名不分先后

• DOTA_devkit

  Name  : "胡凯旋"
  describe myself："咸鱼一枚"