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avm_rot's Introduction

AVM

环境

最佳实践（推荐，已测试可行）

  1. 第一步 使用 MIM 安装 MMCV
pip install -U openmim
mim install mmcv-full
  1. 第二步 安装 MMRotate
pip install mmrotate
  1. 第三步下载配置文件(可选)
mim download mmrotate --config oriented_rcnn_r50_fpn_1x_dota_le90 --dest .

训练

在当前目录下建立软连接

ln -s AVM_DATA_ROOT avm

其中AVM原始数据集目录需调整为如下结构

avm
 │
 ├─ images
 │   ├─ b2_left
 │   │   └─ avm
 │   │       ├─ 0.jpg
 │   │       └─ 20.jpg
 │   │       ... 
 │   ├─ b2_right
 │   ├─ b2_to_b3
 │   └─ b3_to_b2
 │   
 └─ det_annotations
     ├─ b2_left
     │   ├─ 0.json
     │   └─ 20.json
     │   ... 
     ├─ b2_right
     ├─ b2_to_b3
     └─ b3_to_b2

将原数据集格式转换为DOTA格式

python create_data.py --avm-dir ./data/avm --output-dir ./data/avm_dota_format

转换过后文件结构如下：

avm_cs_format
 │
 └─ train
     ├─ annfiles
     │   ├─ b2_left_0.txt
     │   └─ b2_left_1000.txt
     │       ... 
     └─ images
         ├─ b2_left_0.png
         └─ b2_left_1000.png
         ...

推理

python inference.py --config=configs/rotated_retinanet_obb_r50_fpn_1x_avm_le90.py --checkpoint=checkpoints/epoch_3.pth --image_path=demo.png

结果将保存为bin文件，读取后reshape成(-1, 7)，则7个维度分别对应 (x, y, w, h, theta, score, label)。

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