%matplotlib inline

Convert to grayscale using the apply method

This example shows how to use squidpy.ImageContainer.apply to convert an image layer to grayscale. This calls squidpy.im.process in the background.

We take the mean of the three channels(RGB) using the user-defined function `rgb2gray[ to convert the image to grayscale. The function ]{.title-ref}rgb2gray[ computes the mean of the three channels (RGB) across axis 3. The purpose of this example is to show how the ]{.title-ref}apply[ method {meth}`squidpy.ImageContainer.apply]{.title-ref} can be used.

See also

• Smooth an image

• Process a high-resolution image

• Convert to grayscale

import numpy as np

import matplotlib.pyplot as plt

import squidpy as sq

First, we load the H&E stained tissue image. Here, we only load a cropped dataset to speed things up. In general, squidpy.im.ImageContainer.apply() can also process very large images. (See Process a high-resolution image).

img = sq.datasets.visium_hne_image_crop()

The method squidpy.im.ImageContainer.apply() takes a function as an input. Such function, or a mapping of `{'{library_id}': function}[ takes a {class}`numpy.ndarray]{.title-ref} as input and returns an image-like output. Here, the function takes `x``, which is a numpy array as an input and returns the mean of three channels across axis 3 and produces image-like output.

def rgb2gray(x):
    """Return the mean of numpy array along axis 3"""
    return np.mean(x, axis=3)

The H&E stained tissue image is an image layer of squidpy.im.ImageContainer. So, we add .apply and pass the previously defined function as an input.

gray = img.apply(rgb2gray)

Then, we convert the image to grayscale and plot the result, using matplotlib.

fig, axes = plt.subplots(1, 2)
img.show(ax=axes[0])
_ = axes[0].set_title("Original")
gray.show(cmap="gray", ax=axes[1])
_ = axes[1].set_title("Grayscale")
plt.show()