OpenCV Real-time Graph Plot using Matplotlib or Python-Drawnow
Hello Friends,
In this tutorial, we’ll see how we can use Matplotlib to generate live-opencv graphs. We’ll look at atleast three ways of doing this with their relevant source code.
Method #1 : A simple one
Here’s the first working version of the code (requires at least version Matplotlib 1.1.0 from 2011-11-14):
1 2 3 4 5 6 7 8 9 10 11 12 13 | import numpy as np import matplotlib.pyplot as plt plt.axis([0, 10, 0, 1]) plt.ion() for i in range(10): y = np.random.random() plt.scatter(i, y) plt.pause(0.05) while True: plt.pause(0.05) |
Note some of the changes:
Call plt.ion() in order to enable interactive plotting. plt.show(block=False) is no longer available.
Call plt.pause(0.05) to both draw the new data and it runs the GUI’s event loop (allowing for mouse interaction).
The while loop at the end is to keep the window up after all data is plotted.
Method #2: Real-time plot with Matplotlib Animation API
If you’re interested in realtime plotting, I’d recommend looking into matplotlib’s animation API.
In particular, using blit to avoid redrawing the background on every frame can give you substantial speed gains (~10x):
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | #!/usr/bin/env python import numpy as np import time import matplotlib matplotlib.use('GTKAgg') from matplotlib import pyplot as plt def randomwalk(dims=(256, 256), n=20, sigma=5, alpha=0.95, seed=1): """ A simple random walk with memory """ r, c = dims gen = np.random.RandomState(seed) pos = gen.rand(2, n) * ((r,), (c,)) old_delta = gen.randn(2, n) * sigma while True: delta = (1. - alpha) * gen.randn(2, n) * sigma + alpha * old_delta pos += delta for ii in xrange(n): if not (0. <= pos[0, ii] < r): pos[0, ii] = abs(pos[0, ii] % r) if not (0. <= pos[1, ii] < c): pos[1, ii] = abs(pos[1, ii] % c) old_delta = delta yield pos def run(niter=1000, doblit=True): """ Display the simulation using matplotlib, optionally using blit for speed """ fig, ax = plt.subplots(1, 1) ax.set_aspect('equal') ax.set_xlim(0, 255) ax.set_ylim(0, 255) ax.hold(True) rw = randomwalk() x, y = rw.next() plt.show(False) plt.draw() if doblit: # cache the background background = fig.canvas.copy_from_bbox(ax.bbox) points = ax.plot(x, y, 'o')[0] tic = time.time() for ii in xrange(niter): # update the xy data x, y = rw.next() points.set_data(x, y) if doblit: # restore background fig.canvas.restore_region(background) # redraw just the points ax.draw_artist(points) # fill in the axes rectangle fig.canvas.blit(ax.bbox) else: # redraw everything fig.canvas.draw() plt.close(fig) print "Blit = %s, average FPS: %.2f" % ( str(doblit), niter / (time.time() - tic)) if __name__ == '__main__': run(doblit=False) run(doblit=True) |
Output:
Blit = False, average FPS: 54.37
Blit = True, average FPS: 438.27
Method#3 : Without using MatplotLib
There is a package available called drawnow on GitHub as “python-drawnow”.
This provides an interface similar to MATLAB’s drawnow which allows you to easily update a figure.
python-drawnow is a thin wrapper around plt.draw but provides the ability to confirm (or debug) after figure display.
An example
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | import matplotlib.pyplot as plt from drawnow import drawnow def make_fig(): plt.scatter(x, y) # I think you meant this plt.ion() # enable interactivity fig = plt.figure() # make a figure x = list() y = list() for i in range(1000): temp_y = np.random.random() x.append(i) y.append(temp_y) # or any arbitrary update to your figure's data i += 1 drawnow(make_fig) |