Question

[haesleinhuepf]

How can I use stackview to crop an image?

git-bob answer

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To crop an image using the stackview library, you can simply use the following code:

import stackview

# Assuming `image` is your image data
crop_widget = stackview.crop(image, continuous_update=True)

This will open an interactive widget that allows you to crop the image. You can add additional parameters such as zoom_factor, slider_text, and axis_names to customize the widget to your needs.

[nikofleischer]

Explain the axis and axis_names paramters for me
git-bob answer

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^{This message was generated by git-bob (version: 0.18.1, model: gpt-4o-2024-08-06, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

Hi @nikofleischer,

thanks for reaching out! Unfortunately, I'm not allowed to respond to you directly.
I need approval from a person who has access.

Best,
git-bob

[haesleinhuepf]

Explain the axis and axis_names paramters for me

git-bob answer

[github-actions]

^{This message was generated by git-bob (version: 0.18.1, model: gpt-4o-2024-08-06, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

Hi @haesleinhuepf,

The axis parameter in stackview’s cropping function specifies which axes of the image should be available for cropping. You can provide a list of axis indices that you want to include in the cropping interface.

The axis_names parameter lets you provide a list of names for each axis. These names are then displayed in the user interface, making it easier to understand what each axis represents. This is particularly useful when working with multi-dimensional data where each axis might represent something specific, like time, depth, or channel in an image stack.

Let me know if you need more information on this!

Best,
git-bob

[haesleinhuepf]

Correction: axis is obsolete.

stackview/stackview/_crop.py

Lines 23 to 24 in a5f2d36

	axis : int, optional
	This parameter is obsolete. If you want to show any other axis than the first, you need to transpose the image before, e.g. using np.swapaxes().