Hello, switching to 64-bit Python and OS might help. I can display 8bit images up to 8459x8459 with imshow on Windows 7 64-bit with 8GB RAM. Python then uses about 5.5 GB RAM according to task manager. A 8460x8460 or larger 8bit images crash Python (definitely a bug). The 32-bit interpreter starts throwing MemoryErrors around 4350x4350 pixels. The exact limits will depend on the application, OS, and free RAM.
Christoph On 12/15/2009 9:15 AM, Wellenreuther, Gerd wrote: > Well, I am trying to create an overlay, *one* picture showing all 34 > images. So I am only trying to create a single figure. > > I just attached an example so you can get an idea (it was downsampled > for mailing, the original picture has ca. 5500 x 6500 pixels). In the > end, I just want to save the image to the disk, so I am using 'Agg' as > the backend - hope this also saves me some memory. > > And about "old" images: I am always starting a completely new > python-process for each stitching (one at a time). > > Cheers, Gerd > > Perry Greenfield wrote: >> Are you clearing the figure after each image display? The figure >> retains references to the image if you don't do a clf() and thus you >> will eventually run out of memory, even if you delete the images (they >> don't go away while matplotlib is using them). >> >> Perry >> >> On Dec 15, 2009, at 10:32 AM, Wellenreuther, Gerd wrote: >> >>> Dear all, >>> >>> I am trying to write a script to be used with our microscope, stitching >>> images of various magnifications together to yield a big picture of a >>> sample. The preprocessing involves operations like rotating the picture >>> etc., and finally those pictures are being plotted using imshow. >>> >>> Unfortunately, I am running into memory problems, e.g.: >>> >>>> C:\Python26\lib\site-packages\PIL\Image.py:1264: DeprecationWarning: >>>> integer argument expected, got float >>>> im = self.im.stretch(size, resample) >>>> Traceback (most recent call last): >>>> File "F:\Procs\Find_dendrites.py", line 1093, in <module> >>>> file_type="PNG",do_stitching=do_stitching,do_dendrite_finding=do_dendrite_finding,down_sizing_factor=48,dpi=600) >>>> >>>> File "F:\Procs\Find_dendrites.py", line 1052, in process_images >>>> scale,aspect_ratio,dpi,left,right,bottom,top) >>>> File "F:\Procs\Find_dendrites.py", line 145, in stitch_images >>>> pylab.draw() >>>> File "C:\Python26\lib\site-packages\matplotlib\pyplot.py", line 352, >>>> in draw >>>> get_current_fig_manager().canvas.draw() >>>> File >>>> "C:\Python26\lib\site-packages\matplotlib\backends\backend_agg.py", >>>> line 313, in draw >>>> self.renderer = self.get_renderer() >>>> File >>>> "C:\Python26\lib\site-packages\matplotlib\backends\backend_agg.py", >>>> line 324, in get_renderer >>>> self.renderer = RendererAgg(w, h, self.figure.dpi) >>>> File >>>> "C:\Python26\lib\site-packages\matplotlib\backends\backend_agg.py", >>>> line 59, in __init__ >>>> self._renderer = _RendererAgg(int(width), int(height), dpi, >>>> debug=False) >>>> RuntimeError: Could not allocate memory for image >>> >>> or >>> >>>> Traceback (most recent call last): >>>> File "F:\Procs\Find_dendrites.py", line 1093, in <module> >>>> file_type="PNG",do_stitching=do_stitching,do_dendrite_finding=do_dendrite_finding,down_sizing_factor=48,dpi=75) >>>> >>>> File "F:\Procs\Find_dendrites.py", line 1052, in process_images >>>> scale,aspect_ratio,dpi,left,right,bottom,top) >>>> File "F:\Procs\Find_dendrites.py", line 142, in stitch_images >>>> pylab.imshow(rotated_images[i],aspect='auto') >>>> File "C:\Python26\lib\site-packages\matplotlib\pyplot.py", line >>>> 2046, in imshow >>>> ret = ax.imshow(X, cmap, norm, aspect, interpolation, alpha, vmin, >>>> vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, >>>> url, **kwargs) >>>> File "C:\Python26\lib\site-packages\matplotlib\axes.py", line 6275, >>>> in imshow >>>> im.set_data(X) >>>> File "C:\Python26\lib\site-packages\matplotlib\image.py", line 291, >>>> in set_data >>>> self._A = pil_to_array(A) >>>> File "C:\Python26\lib\site-packages\matplotlib\image.py", line 856, >>>> in pil_to_array >>>> x = toarray(im) >>>> File "C:\Python26\lib\site-packages\matplotlib\image.py", line 831, >>>> in toarray >>>> x = np.fromstring(x_str,np.uint8) >>>> MemoryError >>> >>> >>> I already implemented some downscaling of the original images (ca. 3200 >>> x 2400 pixels), to roughly match the figures dpi-setting. But this does >>> not seem to be the only issue. The script does work for dpi of 600 or >>> 150 for 11 individual images, yielding e.g. a 23 MB file with 600 dpi >>> and 36 Megapixels. But it fails for e.g. 35 images even for 75 dpi. >>> >>> I was trying to throw away any unneccessary data using del + triggering >>> the garbage collection, but this did not help beyond a certain point. >>> Maybe somebody could tell me what kind of limitations there are using >>> imshow to plot a lot of images together, and how to improve? >>> >>> Some more info: I am using Windows. Just by judging from the >>> task-manager, the preprocessing is not the problem. But *plotting* the >>> images using imshow seems to cause an increase of memory consumption of >>> the task of 32-33 MB *each* time. Somewhere around a total of 1.3 - 1.5 >>> Gigs the process dies ... >>> >>> Thanks in advance, >>> >>> Gerd >>> -- >>> Dr. Gerd Wellenreuther >>> beamline scientist P06 "Hard X-Ray Micro/Nano-Probe" >>> Petra III project >>> HASYLAB at DESY >>> Notkestr. 85 >>> 22603 Hamburg >>> >>> Tel.: + 49 40 8998 5701 >>> >>> ------------------------------------------------------------------------------ >>> >>> Return on Information: >>> Google Enterprise Search pays you back >>> Get the facts. >>> http://p.sf.net/sfu/google-dev2dev >>> _______________________________________________ >>> Matplotlib-users mailing list >>> Matplotlib-users@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users >> > > > > ------------------------------------------------------------------------------ > This SF.Net email is sponsored by the Verizon Developer Community > Take advantage of Verizon's best-in-class app development support > A streamlined, 14 day to market process makes app distribution fast and easy > Join now and get one step closer to millions of Verizon customers > http://p.sf.net/sfu/verizon-dev2dev > > > > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ------------------------------------------------------------------------------ This SF.Net email is sponsored by the Verizon Developer Community Take advantage of Verizon's best-in-class app development support A streamlined, 14 day to market process makes app distribution fast and easy Join now and get one step closer to millions of Verizon customers http://p.sf.net/sfu/verizon-dev2dev _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
