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), 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.