mirror of https://github.com/open-mmlab/mmyolo.git
56 lines
2.2 KiB
Markdown
56 lines
2.2 KiB
Markdown
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# Dataset Conversion
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The folder `tools/data_converters` currently contains `ballon2coco.py`, `yolo2coco.py`, and `labelme2coco.py` - three dataset conversion tools.
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- `ballon2coco.py` converts the `balloon` dataset (this small dataset is for starters only) to COCO format.
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```shell
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python tools/dataset_converters/balloon2coco.py
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```
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- `yolo2coco.py` converts a dataset from `yolo-style` **.txt** format to COCO format, please use it as follows:
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```shell
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python tools/dataset_converters/yolo2coco.py /path/to/the/root/dir/of/your_dataset
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```
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Instructions:
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1. `image_dir` is the root directory of the yolo-style dataset you need to pass to the script, which should contain `images`, `labels`, and `classes.txt`. `classes.txt` is the class declaration corresponding to the current dataset. One class a line. The structure of the root directory should be formatted as this example shows:
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```bash
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.
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└── $ROOT_PATH
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├── classes.txt
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├── labels
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│ ├── a.txt
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│ ├── b.txt
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│ └── ...
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├── images
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│ ├── a.jpg
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│ ├── b.png
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│ └── ...
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└── ...
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```
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2. The script will automatically check if `train.txt`, `val.txt`, and `test.txt` have already existed under `image_dir`. If these files are located, the script will organize the dataset accordingly. Otherwise, the script will convert the dataset into one file. The image paths in these files must be **ABSOLUTE** paths.
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3. By default, the script will create a folder called `annotations` in the `image_dir` directory which stores the converted JSON file. If `train.txt`, `val.txt`, and `test.txt` are not found, the output file is `result.json`. Otherwise, the corresponding JSON file will be generated, named as `train.json`, `val.json`, and `test.json`. The `annotations` folder may look similar to this:
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```bash
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.
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└── $ROOT_PATH
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├── annotations
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│ ├── result.json
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│ └── ...
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├── classes.txt
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├── labels
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│ ├── a.txt
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│ ├── b.txt
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│ └── ...
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├── images
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│ ├── a.jpg
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│ ├── b.png
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│ └── ...
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└── ...
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```
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