There are a few ways to improve the speed, which, I guess, will give you factor of a few speed up. If you need more than that, I guess matplotlib is not suitable for your purpose.
* try a simple interpolation method, eg. imshow(arr, interpolation="nearest") * reduce the image size. Unless you have a monitor this large, you loose the image quality anyhow. In my machine (linux, xeon 2.6 GHz), it takes about ~0.8 s to display 2k x 2k image. 0.3 s for 1k x 1k. Regards, -JJ On Fri, Sep 4, 2009 at 5:18 AM, Auré Gourrier<aurelien.gourr...@yahoo.fr> wrote: > Dear all, > > I recently cried for help (see below) but sadly didn't get any answer... > This problem is really a bottleneck for me and if I must find a solution... > Can anyone PLEAAAAAAAAASE point me in the right direction ? > > I recently bumped into a problem while trying to display imshow images > (2048x2048 pix) in a GUI based on TkAgg (matplotib0.91.2, python 2..4.4, > numpy1.0.4). In brief, building the image with imshow is Ok, but the call to > show() or draw() takes about 1.2 s on my system (XP, 2.8Ghz, 2Go ram). After > reading a lot of posts and trying out a few things, I turned to the > Animation_blit_tk.py example and modified it accordingly (code attached > below). In the end, the display is still not much faster using this method > (still in the order of 1.2 s as indicated by the result below). > Could any one tel me whether I'm doing something wrong ? > > As far as I understand, at least in my Tk GUI, the limiting step using the > ImageTk.FigureCanvasTkAgg is the call to draw() or draw_artist(). I read > some comments from John regarding TkAgg being slow in some cases but > couldn't find a definite answer. > > Any hint would be much appreciated.... > > Cheers, > > Aure > > > -------- > # For detailed comments on animation and the techniqes used here, see > # the wiki entry http://www.scipy.org/Cookbook/Matplotlib/Animations > > import matplotlib > matplotlib.use('TkAgg') > > import sys > import pylab as p > #import matplotlib.numerix as nx > import time > > from FileUtils10 import fileHandling > > # for profiling > tstart = time.time() > tprevious = time.time() > > fnamelist = ['fname1','fname2','fname3'] > > ax = p.subplot(111) > canvas = ax.figure.canvas > > print 't1 ',time.time()-tprevious > tprevious = time.time() > > # create the initial line > dataarr = fileHandling(fnamelist[0]).readSpecial() > #print dataarr.dtype => numpy dtype uint16 > #dataarr = dataarr.astype('uint8') > print 't2 ',time.time()-tprevious > tprevious = time.time() > > image = p.imshow(dataarr, animated=True) > print 't3 ',time.time()-tprevious > tprevious = time.time() > > def run(*args): > tprevious = time.time() > background = canvas.copy_from_bbox(ax.bbox) > print 't4 ',time.time()-tprevious > tprevious = time.time() > while 1: > #print fnamelist[run.cnt] > # restore the clean slate background > canvas.restore_region(background) > print 't5 ',time.time()-tprevious > tprevious = time.time() > # update the data > dataarr = fileHandling(fnamelist[run.cnt]).readSpecial() > print 't6 ',time.time()-tprevious > tprevious = time.time() > image.set_data(dataarr) > print 't7 ',time.time()-tprevious > tprevious = time.time() > # just draw the animated artist > ax.draw_artist(image) > print 't8 ',time.time()-tprevious > tprevious = time.time() > # just redraw the axes rectangle > canvas.blit(ax.bbox) > print 't9 ',time.time()-tprevious > tprevious = time.time() > > if fnamelist[run.cnt] == fnamelist[-1]: > # print the timing info and quit > print 'total time:' , time.time()-tstart > print 'FPS:' , 1000./(time.time()-tstart) > p.close('all') > sys.exit() > > run.cnt += 1 > run.cnt = 0 > > > p.subplots_adjust(left=0.3, bottom=0.3) # check for flipy bugs > p.grid() # to ensure proper background restore > manager = p.get_current_fig_manager() > manager.window.after(100, run) > > p.show() > > > ==== > Results in: > > t1 0.858999967575 > t2 0.0320000648499 > t3 1.31299996376 > t4 0.0 > t5 0.0 > t6 0.0310001373291 > t7 0.0 > t8 1.18700003624 > t9 0.0160000324249 > t5 0.0 > t6 0.0469999313354 > t7 0..0 > t8 1.17200016975 > t9 0.0149998664856 > t5 0.0 > t6 0.047000169754 > t7 0.0 > t8 1.21899986267 > t9 0.0 > t5 0.0 > t6 0.0460000038147 > t7 0.0 > t8 1.17199993134 > t9 0.0 > t5 0.0 > t6 0.0469999313354 > t7 0.0 > t8 1.18700003624 > t9 0.0160000324249 > total time: 8.75 > FPS: 114.285714286 > > > > ------------------------------------------------------------------------------ > Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day > trial. Simplify your report design, integration and deployment - and focus > on > what you do best, core application coding. Discover what's new with > Crystal Reports now. http://p.sf.net/sfu/bobj-july > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > ------------------------------------------------------------------------------ Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day trial. Simplify your report design, integration and deployment - and focus on what you do best, core application coding. Discover what's new with Crystal Reports now. http://p.sf.net/sfu/bobj-july _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
