https://github.com/CAPTAIN-WHU/iSAID_Devkit

[CVPR'W19-Oral] Official repository for "iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images"
https://github.com/CAPTAIN-WHU/iSAID_Devkit

aerial-imagery cvpr19 dataset devkit dota evaluation-code instance-segmentation object-detection pytorch pytorch-implementation

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[CVPR'W19-Oral] Official repository for "iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images"

• Host: GitHub
• URL: https://github.com/CAPTAIN-WHU/iSAID_Devkit
• Owner: CAPTAIN-WHU
• Created: 2019-07-13T14:58:33.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-04-03T20:29:36.000Z (over 3 years ago)
• Last Synced: 2024-03-06T03:35:04.191Z (4 months ago)
• Topics: aerial-imagery, cvpr19, dataset, devkit, dota, evaluation-code, instance-segmentation, object-detection, pytorch, pytorch-implementation
• Language: Jupyter Notebook
• Homepage: https://captain-whu.github.io/iSAID/
• Size: 27.7 MB
• Stars: 122
• Watchers: 7
• Forks: 18
• Open Issues: 10
• Metadata Files:
  • Readme: README.md

Lists

• awesome-satellite-imagery-datasets - devkit - whu.github.io/DOTA/), Paper: [Zamir et al. 2019](https://arxiv.org/abs/1905.12886) (1. Instance Segmentation)

README

        
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/isaid-a-large-scale-dataset-for-instance/object-detection-on-isaid)](https://paperswithcode.com/sota/object-detection-on-isaid?p=isaid-a-large-scale-dataset-for-instance)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/isaid-a-large-scale-dataset-for-instance/instance-segmentation-on-isaid)](https://paperswithcode.com/sota/instance-segmentation-on-isaid?p=isaid-a-large-scale-dataset-for-instance)

## iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images, CVPR workshops, 2019.

**Codes for Data Preparation and Evaluation**

1.  **Environment and dependencies installation**

    1. Create the conda environment

            ```conda env create -f environment.yml```

    2. Activate the current working environment

             ```source activate py_isaid```

    3. Setup pycocotols for the evalaution server

               - `cd cocoapi/PythonAPI`

               - `make`

               - `python setup.py install`

    4. Setup cityscapesScripts for the evalaution server

             - `cd preprocess/cityscapesScripts`

             - `python setup.py install`

    5. Setup detectron for the evalaution server

             - `cd preprocess/Detectron`

             - `make`

    6. Note: opencv version == 3.4.2

             

2.  **Data Preparation for Training, Validation and Testing**

    1. Please download iSAID dataset that contains image segmentation masks. Also, download original images from DOTA dataset. 

    Make sure that the final dataset must have this structure:

    ```

        iSAID

        ├── test

        │   └── images

        │       ├── P0006.png

        │       └── ...

        │       └── P0009.png

        ├── train

        │   └── images

        │       ├── P0002_instance_color_RGB.png

        │       ├── P0002_instance_id_RGB.png

        │       ├── P0002.png

        │       ├── ...

        │       ├── P0010_instance_color_RGB.png

        │       ├── P0010_instance_id_RGB.png

        │       └── P0010.png

        └── val

            └── images

                ├── P0003_instance_color_RGB.png

                ├── P0003_instance_id_RGB.png

                ├── P0003.png

                ├── ...

                ├── P0004_instance_color_RGB.png

                ├── P0004_instance_id_RGB.png

                └── P0004.png

    ```

    Note that the segmentation masks for the test images are withheld for the evaluation server.

    

    3. Change the current working directory to preprocess folder.

        ```cd preprocess```

    4. Create symlink for iSAID dataset as

        ```ln -s /path-of-iSAID-dataset ./dataset/```

    

    5. Split training and validation images into patches

        ```python split.py --set train,val```

    

    6. Split test images into patches

        ```python split.py --set test```

    

    7. Create coco-format json annotation files for train and val split images

        ```python preprocess.py --set train,val```

        

        Make sure that the final dataset after preprocesing must have this structure:

    ```

    iSAID_patches

    ├── test

    │   └── images

    │       ├── P0006_0_0_800_800.png

    │       └── ...

    │       └── P0009_0_0_800_800.png

    ├── train

    │   └── instance_only_filtered_train.json

    │   └── images

    │       ├── P0002_0_0_800_800_instance_color_RGB.png

    │       ├── P0002_0_0_800_800_instance_id_RGB.png

    │       ├── P0002_0_800_800.png

    │       ├── ...

    │       ├── P0010_0_0_800_800_instance_color_RGB.png

    │       ├── P0010_0_0_800_800_instance_id_RGB.png

    │       └── P0010_0_800_800.png

    └── val

        └── instance_only_filtered_val.json

        └── images

            ├── P0003_0_0_800_800_instance_color_RGB.png

            ├── P0003_0_0_800_800_instance_id_RGB.png

            ├── P0003_0_0_800_800.png

            ├── ...

            ├── P0004_0_0_800_800_instance_color_RGB.png

            ├── P0004_0_0_800_800_instance_id_RGB.png

            └── P0004_0_0_800_800.png

    ```

        

3. **Method**

    1. Run your instance segmentation method on patches and generate json file of predictions

4. **Evaluation**

    1. Change the current working directory to evaluate folder.

        ```cd ../evaluate```

    3. Given json of predictions and json of val set ground truth (obtained after preprocess.py), Compute Average Precision

        ```python evaluate.py ```