Matplotlib Image labeller for classifying images

Last update: Sep 24, 2022

Overview

mpl-image-labeller

Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui!

For more see the documentation.

Install

pip install mpl-image-labeller

Key features

Simple interface
Uses keys instead of mouse
Only depends on Matplotlib
- Works anywhere - from inside Jupyter to any supported GUI framework
Displays images with correct aspect ratio
Easily configurable keymap
Smart interactions with default Matplotlib keymap
Callback System (see examples/callbacks.py)

single class per image

multiple classes per image

Usage

import matplotlib.pyplot as plt
import numpy as np

from mpl_image_labeller import image_labeller

images = np.random.randn(5, 10, 10)
labeller = image_labeller(
    images, classes=["good", "bad", "meh"], label_keymap=["a", "s", "d"]
)
plt.show()

accessing the axis You can further modify the image (e.g. add masks over them) by using the plotting methods on axis object accessible by labeller.ax.

Lazy Loading Images If you want to lazy load your images you can provide a function to give the images. This function should take the integer idx as an argument and return the image that corresponds to that index. If you do this then you must also provide N_images in the constructor to let the object know how many images it should expect. See examples/lazy_loading.py for an example.

Controls

<- move one image back
-> move one image forward

To label images use the keys defined in the label_keymap argument - default 0, 1, 2...

Get the labels by accessing the labels property.

Overwriting default keymap

Matplotlib has default keybindings that it applied to all figures via rcparams.keymap that allow for actions such as s to save or q to quit. If you inlcude one of these keys as a shortcut for labelling as a class then that default keymap will be disabled for that figure.

Related Projects

This is not the first project to implement easy image labelling but seems to be the first to do so entirely in Matplotlib. The below projects implement varying degrees of complexity and/or additional features in different frameworks.

Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.

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Comments

BUG: Zooming in on one image messes up display of other images
will need something like:

# make sure the home button will work if hasattr(fig.canvas, "toolbar") and fig.canvas.toolbar is not None: fig.canvas.toolbar.push_current()

but that may also be wrong - may need to pop everytime an image is switched (right before switching) and then push the new one?
opened by ianhi 1
[pre-commit.ci] pre-commit autoupdate
updates:

github.com/asottile/setup-cfg-fmt: v1.17.0 → v1.19.0

github.com/PyCQA/flake8: 3.9.2 → 4.0.1

github.com/PyCQA/isort: 5.8.0 → 5.9.3

github.com/psf/black: 21.5b2 → 21.10b0

github.com/asottile/pyupgrade: v2.19.0 → v2.29.0

github.com/pre-commit/mirrors-mypy: v0.812 → v0.910-1
opened by pre-commit-ci[bot] 1

Releases(1.1.2)

1.1.2(Nov 18, 2022)

Fix a bug wiht one hot encodings

Full Changelog: https://github.com/ianhi/mpl-image-labeller/compare/1.1.1...1.1.2
Source code(tar.gz)
Source code(zip)
1.1.1(Nov 12, 2021)
What's Changed

add github actions test by @ianhi in https://github.com/ianhi/mpl-image-labeller/pull/20

Autoscale cmaps + add tests by @ianhi in https://github.com/ianhi/mpl-image-labeller/pull/21

Updated callbacks example to show how to adjust overlay extent

Full Changelog: https://github.com/ianhi/mpl-image-labeller/compare/1.1.0...1.1.1
Source code(tar.gz)
Source code(zip)
1.1.0(Nov 1, 2021)
What's Changed

Added ability for user to set the title https://github.com/ianhi/mpl-image-labeller/pull/15

Updated text positioning for single class labeller

Full Changelog: https://github.com/ianhi/mpl-image-labeller/compare/1.0.0...1.1.0
Source code(tar.gz)
Source code(zip)
1.0.0(Oct 30, 2021)
Created a docs readthedocs page

Added a multiclass labeller

Ability to use onehot encodings

Source code(tar.gz)
Source code(zip)
0.5.0(Oct 29, 2021)
Fixed xlims getting messed up when zooming in https://github.com/ianhi/mpl-image-labeller/pull/9

Allow passing imshow_kwargs https://github.com/ianhi/mpl-image-labeller/commit/27afa0bf9633c5f59e2d3089f9fef789147e2b3c

Source code(tar.gz)
Source code(zip)
0.4.0(Oct 29, 2021)

Callbacks!
Source code(tar.gz)
Source code(zip)
0.3.0(Oct 27, 2021)

Automatically adjust image extent to preserve the aspect ratio of the image
Source code(tar.gz)
Source code(zip)
0.2.0(Oct 27, 2021)
Full Changelog: https://github.com/ianhi/mpl-image-labeller/compare/0.1.1...0.2.0

Fixes:

init_labels is respected

new features:

ax is not accesible through the .ax attribute

images can now be a callable

Thanks to @jrussell25 for suggesting these improvements
Source code(tar.gz)
Source code(zip)
0.1.1(Oct 27, 2021)

Fix the qwerty keymap - previously it would break #2
Source code(tar.gz)
Source code(zip)

Owner

Ian Hunt-Isaak

The embodiment of entropy - He/Him

GitHub Repository http://mpl-image-labeller.rtfd.io/

Matplotlib Image labeller for classifying images

Related tags

Overview

mpl-image-labeller

Install

Key features

Usage

Controls

Overwriting default keymap

Related Projects

You might also like...

This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models are Pix2Pix, Pix2PixHD, CycleGAN and PointWise.

Official implement of Paper：A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.

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Rename Images with Auto Generated Neural Image Captions

A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

Image-to-Image Translation with Conditional Adversarial Networks (Pix2pix) implementation in keras

Learning Continuous Image Representation with Local Implicit Image Function

Comments

BUG: Zooming in on one image messes up display of other images

[pre-commit.ci] pre-commit autoupdate

Releases(1.1.2)

1.1.2(Nov 18, 2022)

1.1.1(Nov 12, 2021)

What's Changed

1.1.0(Nov 1, 2021)

What's Changed

1.0.0(Oct 30, 2021)

0.5.0(Oct 29, 2021)

0.4.0(Oct 29, 2021)

0.3.0(Oct 27, 2021)

0.2.0(Oct 27, 2021)

0.1.1(Oct 27, 2021)

Owner

Ian Hunt-Isaak

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