Usage Guide

# only use this next line if in a notebook
%matplotlib ipympl

import matplotlib.pyplot as plt
import numpy as np
from mpl_image_segmenter import ImageSegmenter
from mpl_image_segmenter.example_images import color_image_stack, gray_image_stack

# load a sample image

gray_image_stack = gray_image_stack()
color_image_stack = color_image_stack()
example_color_image = color_image_stack[0]
example_gray_image = gray_image_stack[0]

Single class - Single Gray Image

A single grayscale image (shape (Y, X)) and only class in the segmentation mask is the simplest use case.

You can click and drag to select regions. Scroll to zoom and middle click to pan are provided by the mpl-pan-zoom library.

segmenter = ImageSegmenter(
    example_gray_image,
    figsize=(7, 7),
)
display(segmenter)

Erasing

To start erasing instead of selecting set segmenter.erasing = True. Run the following cell then try to lasso an area you have already selected

segmenter.erasing = True

Looking at the mask.

The mask is accessible via ImageSegmenter.mask. 0 is used as the value of unselected regions.

segmenter.mask

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])

Single class - Single Color Image

If you have a RGB(A) image, i.e. with shape (Y, X, 3) or (Y, X, 4) then you must pass color_image=True to the ImageSegmenter constructor in order to have the shapes behave approriately.

ImageSegmenter(example_color_image, color_image=True)

Multiple classes

You can use multiple classes in the mask with the classes argument. It accepts either an integer, or an iterable class names as string.

You can change which class is currently being drawn on the mask using the .current_class attribute. This can always be set to an integer, or if you are using named classes you can also set it to the name of the class you want to draw.

Warning

The class index is NOT zero indexed. This is because class 0 is always the background.

colors

By default the tab10 color set will be used, and if that is not enough the xkcd color named colors will be used. Or you can pass a list of colors via the mask_colors argument.

segmenter = ImageSegmenter(example_gray_image, classes = ["class1", "class2", "class3"])
# segmenter = ImageSegmenter(gray_image, classes = 3)

# set the class 
segmenter.current_class = 1 # same as setting to `class1`
segmenter.current_class = "class2" # same as setting to 2

Image Stacks

For both gray and color images you can pass in an array-like stack of images (i.e. shape (N, Y, X, [3]) and then switch which image is currently active using the image_index property.

segmenter = ImageSegmenter(gray_image_stack)
segmenter

# change which image is displayed
segmenter.image_index = 2

# The mask will now be multidimensional as well

segmenter.mask.shape

(5, 512, 512)

pre-seedng a mask

You can also load an existing mask. You will need to ensure that it does not have values greater than nclasses and that it has the same shape as the image. There are currently no safegaurds for this and when there are exceptions in a matplotlib callback they can be hard to see in the notebook - so be careful!

If you run the below cell the image should show up with the premade mask already applied!

from mpl_image_segmenter.example_images import example_mask_stack
mask = example_mask_stack()
preloaded = ImageSegmenter(gray_image_stack, classes=3, mask=mask)
display(preloaded)

Styling

imshow parameters

You can modify the display of the image using any kwargs accepted the imshow command. For example set the colormap with the cmap argument.

fig, axs = plt.subplots(1,2)
gray = ImageSegmenter(example_gray_image, cmap="gray", ax=axs[0])
plasma = ImageSegmenter(example_gray_image, cmap="plasma",ax=axs[1])

LassoSelector line

You can change the appearance of the LassoSelector line using the props kwarg. So to make the line very thick and red:

props = {"color": "red", "linewidth": 10}
seg = ImageSegmenter(
    example_gray_image, figsize=(5, 5), cmap="gray", props=props
)
display(seg)