代码说明

数据分析：data_analysis.ipynb
获取test的json：get_testjson.py
Retinex与加速测试代码：Retinex.py

已集成在mmdet的transform里，此处仅为测试用
WBF: Weighted-Boxes-Fusion/ensemble.ipynb
泊松融合：poisson-image-editing和poisson_blending.ipynb
画标注框到训练集图上：draw_bbox.ipynb
画预测框到测试集图上：draw_pred_bbox.ipynb
实例平衡增强：instance_balanced_augmentation.ipynb
动态模糊可视化样本图代码：Motion_Blurring.ipynb
动态模糊，Mixup和Retinex加入mmdetection后的代码：mmdet/datasets/pipelines/transforms.py，使用时请自行在__init__.py下加入它们的名称。

配置文件按照需求修改以下内容：

train_pipeline = [
  dict(type='LoadImageFromFile'),
  dict(type='LoadAnnotations', with_bbox=True),
  dict(type='Mixup', prob=0.5, lambd=0.8, mixup=True,
       json_path='data/seacoco/train_waterweeds.json',
       img_path='data/seacoco/train/'),
  dict(type='MotionBlur', p=0.3),
  dict(type='Resize', img_scale=[(4096, 600), (4096, 1000)],
       multiscale_mode='range', keep_ratio=True),
  dict(type='Retinex', model='MSR', sigma=[30, 150, 300],
       restore_factor=2.0, color_gain=6.0, gain=128.0, offset=128.0),
  dict(type='RandomFlip', flip_ratio=0.5),
  dict(type='Pad', size_divisor=32),
  dict(type='Albu',
       transforms=albu_train_transforms,
       bbox_params=dict(type='BboxParams',
                        format='pascal_voc',
                        label_fields=['gt_labels'],
                        min_visibility=0.0,
                        filter_lost_elements=True),
       keymap={
           'img': 'image',
           'gt_bboxes': 'bboxes'
       },
       update_pad_shape=False,
       skip_img_without_anno=True),
  dict(type='Normalize', **img_norm_cfg),
  dict(type='DefaultFormatBundle'),
  dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]

标签平滑：mmdet/losses/cross_entropy_loss.py加入了标签平滑的方法。在配置文件需要修改（rpn_head下的CEloss的平滑指数为0.0，bbox_head下的CEloss的平滑指数>0.0即可）如下：
```
oss_cls=dict( type='CrossEntropyLoss',
             use_sigmoid=False,
             loss_weight=1.0，
             smoothing=0.001)
```

BIGWangYuDong / Underwater_detection

代码说明

About

Languages