%matplotlib inline

Convert to grayscale

This example shows how to use squidpy.im.process to convert an image layer to grayscale.

You can convert any layer of squidpy.im.ImageContainer() to grayscale. We use the argument method = 'gray' to convert the image. This calls skimage.color.rgb2gray in the background.

See also

• examples_image_compute_smooth

• examples_image_compute_process_hires

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.process can also process very large images (see examples_image_compute_process_hires).

img = sq.datasets.visium_hne_image_crop()

Then, we convert the image to grayscale and plot the result. With the argument layer we can select the image layer that should be processed. When converting to grayscale, the channel dimensions change from 3 to 1. By default, the name of the resulting channel dimension will be '{{original_channel_name}}_gray'. Use the argument channel_dim to set a new channel name explicitly. By default, the resulting image is saved in the layer image_gray. This behavior can be changed with the arguments copy and layer_added.

sq.im.process(img, layer="image", method="gray")

fig, axes = plt.subplots(1, 2)
img.show("image", ax=axes[0])
_ = axes[0].set_title("original")
img.show("image_gray", cmap="gray", ax=axes[1])
_ = axes[1].set_title("grayscale")