How to update matplotlib's imshow() window interactively?

I'm working on some computer vision algorithm and I'd like to show how a numpy array changes in each step.

What works now is that if I have a simple imshow( array ) at the end of my code, the window displays and shows the final image.

However what I'd like to do is to update and display the imshow window as the image changes in each iteration.

So for example I'd like to do:

import numpy as np
import matplotlib.pyplot as plt
import time

array = np.zeros( (100, 100), np.uint8 )

for i in xrange( 0, 100 ):
    for j in xrange( 0, 50 ):
        array[j, i] = 1
        
        #_show_updated_window_briefly_
        plt.imshow( array )
        time.sleep(0.1)

The problem is that this way, the Matplotlib window doesn't get activated, only once the whole computation is finished.

I've tried both native matplotlib and pyplot, but the results are the same. For plotting commands I found an .ion() switch, but here it doesn't seem to work.

Q1. What is the best way to continuously display updates to a numpy array (actually a uint8 greyscale image)?

Q2. Is it possible to do this with an animation function, like in the dynamic image example? I'd like to call a function inside a loop, thus I don't know how to achieve this with an animation function.

You don't need to call imshow all the time. It is much faster to use the object's set_data method:

myobj = imshow(first_image)
for pixel in pixels:
    addpixel(pixel)
    myobj.set_data(segmentedimg)
    draw()

The draw() should make sure that the backend updates the image.

UPDATE: your question was significantly modified. In such cases it is better to ask another question. Here is a way to deal with your second question:

Matplotlib's animation only deals with one increasing dimension (time), so your double loop won't do. You need to convert your indices to a single index. Here is an example:

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation

nx = 150
ny = 50

fig = plt.figure()
data = np.zeros((nx, ny))
im = plt.imshow(data, cmap='gist_gray_r', vmin=0, vmax=1)

def init():
    im.set_data(np.zeros((nx, ny)))

def animate(i):
    xi = i // ny
    yi = i % ny
    data[xi, yi] = 1
    im.set_data(data)
    return im
    
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=nx * ny,
                               interval=50)

I struggled to make it work because many post talk about this problem, but no one seems to care about providing a working example. In this case however, the reasons were different :

• I couldn't use Tiago's or Bily's answers because they are not in the same paradigm as the question. In the question, the refresh is scheduled by the algorithm itself, while with funcanimation or videofig, we are in an event driven paradigm. Event driven programming is unavoidable for modern user interface programming, but when you start from a complex algorithm, it might be difficult to convert it to an event driven scheme - and I wanted to be able to do it in the classic procedural paradigm too.
• Bub Espinja reply suffered another problem : I didn't try it in the context of jupyter notebooks, but repeating imshow is wrong since it recreates new data structures each time which causes an important memory leak and slows down the whole display process.

Also Tiago mentioned calling draw(), but without specifying where to get it from - and by the way, you don't need it. the function you really need to call is flush_event(). sometime it works without, but it's because it has been triggered from somewhere else. You can't count on it. The real tricky point is that if you call imshow() on an empty table, you need to specify vmin and vmax or it will fail to initialize it's color map and set_data will fail too.

Here is a working solution :

IMAGE_SIZE = 500
import numpy as np
import matplotlib.pyplot as plt


plt.ion()

fig1, ax1 = plt.subplots()
fig2, ax2 = plt.subplots()
fig3, ax3 = plt.subplots()

# this example doesn't work because array only contains zeroes
array = np.zeros(shape=(IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8)
axim1 = ax1.imshow(array)

# In order to solve this, one needs to set the color scale with vmin/vman
# I found this, thanks to @jettero's comment.
array = np.zeros(shape=(IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8)
axim2 = ax2.imshow(array, vmin=0, vmax=99)

# alternatively this process can be automated from the data
array[0, 0] = 99 # this value allow imshow to initialise it's color scale
axim3 = ax3.imshow(array)

del array

for _ in range(50):
    print(".", end="")
    matrix = np.random.randint(0, 100, size=(IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8)
    
    axim1.set_data(matrix)
    fig1.canvas.flush_events()
    
    axim2.set_data(matrix)
    fig1.canvas.flush_events()
    
    axim3.set_data(matrix)
    fig1.canvas.flush_events()
print()

UPDATE : I added the vmin/vmax solution based on @Jettero's comment (I missed it at first).

If you are using Jupyter, maybe this answer interests you. I read in this site that the emmbebed function of clear_output can make the trick:

%matplotlib inline
from matplotlib import pyplot as plt
from IPython.display import clear_output

plt.figure()
for i in range(len(list_of_frames)):
    plt.imshow(list_of_frames[i])
    plt.title('Frame %d' % i)
    plt.show()
    clear_output(wait=True)

It is true that this method is quite slow, but it can be used for testing purposes.

I implemented a handy script that just suits your needs. Try it out here

An example that shows images in a custom directory is like this:

  import os
  import glob
  from scipy.misc import imread

  img_dir = 'YOUR-IMAGE-DIRECTORY'
  img_files = glob.glob(os.path.join(video_dir, '*.jpg'))

  def redraw_fn(f, axes):
    img_file = img_files[f]
    img = imread(img_file)
    if not redraw_fn.initialized:
      redraw_fn.im = axes.imshow(img, animated=True)
      redraw_fn.initialized = True
    else:
      redraw_fn.im.set_array(img)
  redraw_fn.initialized = False

  videofig(len(img_files), redraw_fn, play_fps=30)

I had a similar problem - want to update image, don't want to repeatedly replace the axes, but plt.imshow() (nor ax.imshow()) was not updating the figure displayed.

I finally discovered that some form of draw() was required. But fig.canvas.draw(), ax.draw() ... all did not work. I finally found the solution here:

%matplotlib notebook  #If using Jupyter Notebook
import matplotlib.pyplot as plt
import numpy as np

imData    = np.array([[1,3],[3,1]])

  # Setup and plot image
fig = plt.figure()
ax  = plt.subplot(111)
im  = ax.imshow(imData)

  # Change image contents
newImData = np.array([[2,2],[2,2]])
im.set_data( newImData )
im.draw()

import numpy as np
import matplotlib.pyplot as plt
k = 10
plt.ion()
array = np.zeros((k, k))
for i in range(k):
    for j in range(k):
        array[i, j] = 1
        plt.imshow(array)
        plt.show()
        plt.pause(0.001)
        plt.clf()