splitraster 0.3.7
Provide good support for deep learning and computer vision tasks by creating a tiled output from an input raster dataset.
License: MIT
Requires Python: <3.14,>=3.7
Keywords: split raster tiling

# Split Raster

Provide good support for deep learning and computer vision tasks by creating a tiled output from an input raster dataset.

Find more details:

- Document: [split_raster](https://cuicaihao.github.io/split_raster/)
- Pypi: [splitraster](https://pypi.org/project/splitraster/)


## Use the packages

```bash
pip install splitraster
```

## Try Sample code

The sample image can be found in the GitHub repo.

```python

from splitraster import io

input_image_path = "./data/raw/RGB.png"
gt_image_path = "./data/raw/GT.png"

save_path = "../data/processed/RGB"
crop_size = 256
repetition_rate = 0.5
overwrite = False

n = io.split_image(input_image_path, save_path, crop_size,
                   repetition_rate=repetition_rate, overwrite=overwrite)
print(f"{n} tiles sample of {input_image_path} are added at {save_path}")

save_path_gt = "./data/processed/GT"
n = io.split_image(gt_image_path, save_path_gt, crop_size,
                   repetition_rate=repetition_rate, overwrite=overwrite)
print(f"{n} tiles sample of {gt_image_path} are added at {save_path_gt}")


```

Possible results:

```bash
Successfully installed splitraster-0.1.0
❯ python test.py
Input Image File Shape (H, W, D):(1000, 1000, 3)
crop_size=256, stride=128
Padding Image File Shape (H, W, D):(1024, 1024, 3)
There are 49 files in the ./data/processed/RGB
New image name will start with 50
Generating: 100%|█████████████████████████████████████████████████████████████| 49/49 [00:00<00:00, 50.65img/s]
49 tiles sample of ./data/raw/RGB.png are added at ./data/processed/RGB
Input Image File Shape (H, W, D):(1000, 1000)
crop_size=256, stride=128
Padding Image File Shape (H, W, D):(1024, 1024)
There are 49 files in the ./data/processed/GT
New image name will start with 50
Generating: 100%|████████████████████████████████████████████████████████████| 49/49 [00:00<00:00, 139.72img/s]
49 tiles sample of ./data/raw/GT.png are added at ./data/processed/GT
```

You can also work with Remote Sensing (GeoTIFF) Satellite images such as Multispectral Images which have more bands or channels. All the codes will be the same, but with a small difference. Replace the `io` with the `geo` module.

This feature also needs you to install the `gdal` package with the following command in your python environment.
This package is not in the required packages due to many users may not use this function.

```bash
conda install -c conda-forge gdal
```

Sample Code:

```Python
from splitraster import geo
input_image_path = "./data/raw/Input.tif"
gt_image_path = "./data/raw/GT.tif"

save_path = "../data/processed/Input"
crop_size = 256
repetition_rate = 0.5
overwrite = False

n = geo.split_image(input_image_path, save_path, crop_size,
                   repetition_rate=repetition_rate, overwrite=overwrite)
print(f"{n} tiles sample of {input_image_path} are added at {save_path}")
```

## Random Sampling Code

The basic implementation is still the same as the above. Just replace the 'split_image' method to 'rand_crop_image'.

```python
from splitraster import io
input_image_path = "./data/raw/RGB.png"
gt_image_path = "./data/raw/GT.png"

input_save_path = "./data/processed/Rand/RGB"
gt_save_path = "./data/processed/Rand/GT"

n = io.random_crop_image(input_image_path, input_save_path,  gt_image_path, gt_save_path, crop_size=256, crop_number=20, img_ext='.png', label_ext='.png', overwrite=True)

print(f"{n} sample paris of {input_image_path, gt_image_path} are added at {input_save_path, gt_save_path}.")

```

```python
from splitraster import geo
input_tif_image_path = "./data/raw/TIF/RGB5k.tif"
gt_tif_image_path = "./data/raw/TIF/GT5k.tif"

input_save_image_path = "./data/processed/Rand/RGB_TIF"
gt_save_image_path = "./data/processed/Rand/GT_TIF"

n = geo.random_crop_image(input_tif_image_path, input_save_image_path,  gt_tif_image_path, gt_save_image_path, crop_size=500, crop_number=20, overwrite=True)

print(f"{n} sample paris of {input_tif_image_path, gt_tif_image_path} are added at {input_save_image_path, gt_save_image_path}.")

```

Future Update:

- [x] Add Random Sampling feature.
- [ ] Create a GUI with Qt and generate an executable file
- [ ] Add Sample Balancing feature.