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