Re: [scikit-image] Viewing 3D images/stacks of images

19 May 2017

      from matplotlib.widgets import Slider
import matplotlib.pyplot as plt
import numpy as np

def ddd(images):
    def _update_image(num):
        num = int(num)
        image = np.squeeze(images[num:num+1])
        img_ax.set_data(image)
        fig.canvas.draw_idle()

    if images.ndim is not 3:
        raise ValueError("Not a 3D image.")

    Z, _, _ = images.shape

    fig, ax = plt.subplots()
    img_ax = ax.imshow(np.squeeze(images[0]), cmap="gray")
    ax.axis("off")

    sliderax = plt.axes([0.19, 0.05, 0.65, 0.03],
                        facecolor="lightgoldenrodyellow")
    img_slider = Slider(sliderax, "Z", 0, Z-1,
                        valfmt='%d', valinit=0)
    img_slider.on_changed(_update_image)
    plt.show()

I'd forgotten to include the import statements. Just in case you'd like to
test the code. Input image of shape (Z, N, M).

On 19 May 2017 at 22:10, Kesavan Subburam <pskeshu@gmail.com> wrote:
...
Hi all,
I'd used CollectionViewer from skimage.viewer before for viewing
microscopy Z stacks. However, I personally felt it was a bit of a pain to
use. For instance, before passing in an array of images, the image has to
be stretched, otherwise, most of the time, one would end up with a "black"
series of images.
So, I wrote a simple function using matplotlib to view image collections,
which works quite well, and I think it can be extended to display nD images
with multiple sliders.
def ddd(images):
    def _update_image(num):
        num = int(num)
        image = np.squeeze(images[num:num+1])
        img_ax.set_data(image)
        fig.canvas.draw_idle()
if images.ndim is not 3:
        raise ValueError("Not a 3D image.")
Z, _, _ = images.shape
fig, ax = plt.subplots()
    img_ax = ax.imshow(np.squeeze(images[0]), cmap="gray")
    ax.axis("off")
sliderax = plt.axes([0.19, 0.05, 0.65, 0.03],
                        facecolor="lightgoldenrodyellow")
    img_slider = Slider(sliderax, "Z", 0, Z-1,
                        valfmt='%d', valinit=0)
    img_slider.on_changed(_update_image)
    plt.show()
This might be a bit redundant, but I would like your thoughts on whether
this might be useful for scikit-image. Some of the plugins of
CollectionViewer can be rewritten for this matplotlib based approach in a
much more readable fashion -- for instance, the line profile plugin.
Best,
--
Kesavan Subburam
-- 
Kesavan