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Brief Introduction

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

Fuction

  • 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).

Installation

Same as DOTA_devkit. Then:

$  pip install -r requirements.txt

More detailed explanation

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);

Usage Example

  • DOTA.py
$  python DOTA.py

DOTA_HBB_label DOTA_OBB_label

  • ImgSplit.py
$  python ImgSplit_multi_process.py

Img_before_split Img_after_split

  • ResultMerge.py
$  python ResultMerge.py

visualize_detection_result1 visualize_detection_result2 visualize_merged_result

  • 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

visualize_augmented_labels

有问题反馈

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

感激

感谢以下的项目,排名不分先后

关于作者

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