https://github.com/vfdev-5/imagetilingutils

Minimalistic set of image reader agnostic tools to easily iterate over large images
https://github.com/vfdev-5/imagetilingutils

image python3 tile tiling

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Minimalistic set of image reader agnostic tools to easily iterate over large images

• Host: GitHub
• URL: https://github.com/vfdev-5/imagetilingutils
• Owner: vfdev-5
• License: mit
• Created: 2018-02-12T10:43:53.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-04-07T00:30:42.000Z (over 4 years ago)
• Last Synced: 2024-10-04T15:49:03.481Z (about 1 month ago)
• Topics: image, python3, tile, tiling
• Language: Python
• Homepage: http://imagetilingutils.readthedocs.io/en/latest/
• Size: 24.7 MB
• Stars: 14
• Watchers: 4
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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# Image Tiling Utils

Minimalistic set of image reader agnostic tools to easily iterate over large images

**Example 1**

Let's iterate over a large image with overlapping tiles of the 

same size tiles in pixels. At boundaries we add "no-data" pixels. 

Let's assume the data access is provided with an example function

```python

def read_data(x, y, width, height, out_width=None, out_height=None):

    out_width = width if out_width is None else out_width

    out_height = height if out_height is None else out_height    

    img.read(x, y, width, height, out_width, out_height)

``` 

Thus, overlapping tiles can be extracted as  

```python

from tiling import ConstStrideTiles

tiles = ConstStrideTiles(image_size=(500, 500), tile_size=(256, 256), stride=(100, 100), 

                         origin=(-100, -100),

                         scale=1.0,

                         include_nodata=True)

                       

print("Number of tiles: %i" % len(tiles))

for extent, out_size in tiles:

    x, y, width, height = extent

    data = read_data(x, y, width, height, 

                     out_width=out_size[0], 

                     out_height=out_size[1])

    print("data.shape: {}".format(data.shape))

    

# Access a tile:

i = len(tiles) // 2 

extent, out_size  = tiles[i]

```

![example 1 tiles](assets/example_const_stride_tiles.png)

**Example 2**

Let's iterate over a large image with overlapping tiles of the same size in pixels. 

In this case we prefer to not going outside the input image and fill tiles with `nodata`.

In this situation, overlapping is not constant. 

Let's assume the data access is provided with an example function

```python

def read_data(x, y, width, height, out_width=None, out_height=None):

    out_width = width if out_width is None else out_width

    out_height = height if out_height is None else out_height    

    img.read(x, y, width, height, out_width, out_height)

``` 

Thus, overlapping tiles can be extracted as  

```python

from tiling import ConstSizeTiles

tiles = ConstSizeTiles(image_size=(500, 500), tile_size=(256, 256), min_overlapping=15, scale=1.0)

                       

print("Number of tiles: %i" % len(tiles))

for extent, out_size in tiles:

    assert out_size[0] == tiles.tile_size[0]

    assert out_size[1] == tiles.tile_size[1]

    x, y, width, height = extent

    data = read_data(x, y, width, height, 

                     out_width=out_size[0], 

                     out_height=out_size[1])

    print("data.shape: {}".format(data.shape))

# Access a tile:

i = len(tiles) // 2 

extent  = tiles[i]

```

![example 2 tiles](assets/example_const_size_tiles.png)

## Installation:

#### from pip

```bash

pip install tiling

```

#### from sources

```bash

pip install git+https://github.com/vfdev-5/ImageTilingUtils.git

```

## Examples 

For more practical examples, see [notebooks](examples)