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ImgTiler is a Python library for tiling and retiling images. It can be used for splitting large images into smaller tiles for other applications like Training Deep Learning models, inference on large images, etc.

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collinswakholi/ImgTiler

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Image Tiler

Image Tiler is a Python package that allows you to split an image into tiles based on a grid and overlap between tiles. It also provides functionality to combine the tiles back into the original image.

Installation

You can install Image Tiler using pip:

pip install image-tiller

Usage

SplitImage Class

The SplitImage class is used to split an image into tiles based on a grid and overlap between tiles. The class takes in an image path, the number of rows and columns in the grid, and the overlap between tiles.

CombineTiles Class

The CombineTiles class is used to combine tiles back into the original image. The class takes in a list of tiles, the number of rows and columns in the grid, and the overlap between tiles.

Example

from ImgTiler import SplitImage, CombineTiles
import cv2

img = cv2.imread('image.jpg')
grid = (3, 4) #3 rows, 4 columns
overlap = 30 #30 pixels
show_rects = True
show_image = True

splitter = SplitImage(img, grid, overlap) # Create SplitImage object
tiles = splitter.split_image(show_rect=show_rect, show_tiles=show_image) # Split image into tiles

combiner = CombineTiles(tiles, grid, overlap) # Create CombineTiles object
img2 = combiner.combine_tiles(show_image=show_image) # Combine tiles into original image

Original Image

Orignal Image

Split Result

Split Image without Rectangle Split Image with Rectangle

Combined Image

Combined Image without rectangle Combined Image with rectangle

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT license.

About

ImgTiler is a Python library for tiling and retiling images. It can be used for splitting large images into smaller tiles for other applications like Training Deep Learning models, inference on large images, etc.

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