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This easy-to-use library splits images and its bounding box annotations into tiles, both into specific sizes and into any arbitrary number of equal parts. It can also resize them, both by specific sizes and by a resizing/scaling factor.
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setup.py ready for v0.2 release Apr 23, 2019

README.md

PyPI version

image_bbox_slicer

This easy-to-use library is a data transformer sometimes useful in Object Detection tasks. It splits images and their bounding box annotations into tiles, both into specific sizes and into any arbitrary number of equal parts. It can also resize them, both by specific sizes and by a resizing/scaling factor. Read the docs here.

Partial Labels Example

Currently, this library only supports bounding box annotations in PASCAL VOC format. And as of now, there is no command line execution support. Please raise an issue if needed.

Installation

$ pip install image_bbox_slicer

Works with Python 3.4 and higher versions and requires:

Pillow==5.4.1
numpy==1.16.2
pascal-voc-writer==0.1.4
matplotlib==3.0.3

Usage - A Quick Demo

Note: This usage demo can be found in demo.ipynb in the repo.

import image_bbox_slicer as ibs

Create And Configure Slicer Object

Setting Paths To Source And Destination Directories.

You must configure paths to source and destination directories like the following.

im_src = './src/images'
an_src = './src/annotations'
im_dst = './dst/images'
an_dst = './dst/annotations'

slicer = ibs.Slicer()
slicer.config_dirs(img_src=im_src, ann_src=an_src, 
                   img_dst=im_dst, ann_dst=an_dst)

Dealing With Partial Labels

Partial Labels Example

The above images show the difference in slicing with and without partial labels. In the image on the left, all the box annotations masked in green are called Partial Labels.

Configure your slicer to either ignore or consider them by setting Slicer object's keep_partial_labels instance variable to True or False respectively. By default it is set to False.

slicer.keep_partial_labels = True

Dealing With Empty Tiles

Empty Tiles Example

An empty tile is a tile with no "labels" in it. The definition of "labels" here is tightly coupled with the user's preference of partial labels. If you choose to keep the partial labels (i.e. keep_partial_labels = True), a tile with a partial label is not treated as empty. If you choose to not keep the partial labels (i.e. keep_partial_labels = False), a tile with one or more partial labels is considered empty.

Configure your slicer to either ignore or consider empty tiles by setting Slicer object's ignore_empty_tiles instance variable to True or False respectively. By default it is set to True.

slicer.ignore_empty_tiles = False

Before-After Mapping

You can choose to store the mapping between file names of the images before and after slicing by setting the Slicer object's save_before_after_map instance variable to True. By default it is set to False.

Typically, mapper.csv looks like the following:

| old_name   | new_names                       |
|------------|---------------------------------|
| 2102       | 000001, 000002, 000003, 000004  |
| 3931       | 000005, 000005, 000007, 000008  |
| test_image | 000009, 000010, 000011, 000012  |
| ...        | ...                             |
slicer.save_before_after_map = True

Slicing

Images and Bounding Box Annotations Simultaneously

By Number Of Tiles

slicer.slice_by_number(number_tiles=4)
slicer.visualize_sliced_random()
Output1 Output2

By Specific Size

slicer.slice_by_size(tile_size=(418,279), tile_overlap=0)
slicer.visualize_sliced_random()
Output3 Output4

Note: visualize_sliced_random() randomly picks a recently sliced image from the directory for plotting.

Other Slicing Functions

By Number Of Tiles

slicer.slice_images_by_number(number_tiles=4)

By Specific Size

slicer.slice_images_by_size(tile_size=(418,279), tile_overlap=0)

Slicing Only Bounding Box Annotations

By Number Of Tiles

slicer.slice_bboxes_by_number(number_tiles=4)

By Specifc Size

slicer.slice_bboxes_by_size(tile_size=(418,279), tile_overlap=0)

Resizing

png

Images and Bounding Box Annotations Simultaneously

By Specific Size

slicer.resize_by_size(new_size=(500,200))
slicer.visualize_resized_random()

png

png

By A Resize Factor

slicer.resize_by_factor(resize_factor=0.05)
slicer.visualize_resized_random()

png

png

Note:

visualize_resized_random() randomly picks a recently resized image from the destination directory for plotting.

Other Resizing Functions

Resizing Separately

Only Images

  • By Specific Size

slicer.resize_images_by_size(new_size=(500,200))
  • By Resize Factor

slicer.resize_images_by_factor(resize_factor=0.05)

Only Bounding Box Annotations

  • By Specific Size

slicer.resize_bboxes_by_size(new_size=(500,200))
  • By Resize Factor

slicer.resize_bboxes_by_factor(resize_factor=0.05)
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