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From: Rein van den Boomgaard (UvA) <rein@sc...>  20040707 20:45:49

What is the prefered way to read in images in numarray/matplotlib? I know of PIL but it seems like a lot of overlap with numarray just to read an image from file. I really need to read only one type of file (say ppm) and then i can use imagemagick to convert from and to that format. Regards Rein 
From: John Hunter <jdhunter@ac...>  20040707 16:17:46

>>>>> "Nino" == Nino Cucchiara <cucchiar@...> writes: Nino> I want to write some greek symbols in axes legend, for Nino> exemple nu letters or lambda. Is it possible? Thank you. You can use mathtext anywhere you can use text. Just use the standard way of specifying the string s = r'$\lambda = 5$' See http://matplotlib.sf.net/matplotlib.mathtext.html for more information on mathtext. You may be happy to hear that mathtext for postscript should be ready soon! Here is an example that uses mathtext in a legend from matplotlib.matlab import * t = arange(0.0, 2.0, 0.1) plot(t, sin(2*pi*t), 'go', t, log(1+t), '.') legend((r'$\nu$', r'$\lambda$'), 'upper right') show() 
From: Nino Cucchiara <cucchiar@me...>  20040707 16:07:27

I want to write some greek symbols in axes legend, for exemple nu letters or lambda. Is it possible? Thank you. 
From: Eduardo GonzalezSolares <eglez@as...>  20040707 16:03:02

Hi, I note that in the in the examples which display some images (e.g, image_origin.py) the displayed images hide one of the xaxis (top one if origin="upper" and bottom one otherwise). It seems like it may be necessary to apply an offset before displaying the images (I looked throughout the imshow.py code but didn't find the exact place to do it, it may be a problem of offsetting and scaling). Cheers, Eddie. 
From: John Hunter <jdhunter@ac...>  20040707 14:35:57

>>>>> "Leon" == Leon Brits <ljbrits@...> writes: Leon> Hi, I wanted to compile a old matplot program with the new Leon> matplotlib v0.54.2 but I get the error: Leon> NameError: name 'get_current_fig_manager' is not Leon> defined Leon> the line of code is: Leon> g_canvas = get_current_fig_manager().canvas Leon> This use to work with matplotlib v0.53. I use pyGtk v2.0. Leon> Start code: import pygtk pygtk.require('2.0') import gtk get_current_fig_manager is definitely defined in matplotlib.matlab in version 0.54.2. You need to make sure you do from matplotlib.matlab import get_current_fig_manager before the call to g_canvas = get_current_fig_manager().canvas and you should have no trouble. If you are still having trouble, you'll need to post a complete code example. JDH 
From: Leon Brits <ljbrits@fa...>  20040707 14:27:00

Hi, I wanted to compile a old matplot program with the new matplotlib v0.54.2 but I get the error: NameError: name 'get_current_fig_manager' is not defined the line of code is: g_canvas = get_current_fig_manager().canvas This use to work with matplotlib v0.53. I use pyGtk v2.0. Start code: import pygtk pygtk.require('2.0') import gtk Please help! Leon 
From: John Hunter <jdhunter@ac...>  20040707 12:37:18

>>>>> "Rein" == Rein van den Boomgaard (UvA) <rein@...> writes: Rein> Dear All, as i'm new to this list let me first introduce Rein> myself before posting a question. Rein> I'm working in the field of image processing and computer Rein> vision for a long time now and i am a long time matlab Rein> user. Up to now i didn't like any of the matlab clones but Rein> it seems that numarray+matplotlib might be a winner here. I've worked some with octave in the past and found it unsatisfying (though a very impressive one man feat!), in part because I wasn't happy with gnuplot, which it used for graphics at the time (and perhaps still does), and in part because it really aimed at being a matlab clone but very few of my mfiles ran w/o alteration. matplotlib tries to solve the first problem by producing better graphics than matlab and the second by not trying to be a drop in replacement for matlab. I'm curious about your experiences, what you tried, and what you found lacking. Rein> I started using numarray+matplotlib by coding the Gaussian Rein> derivative convolutions. Calculating a derivative (by Rein> convolving with the derivative of the Gaussian function) Rein> will lead to an image(array) with both positive and negative Rein> values. Although imshow should deal with that (as far as i Rein> understand the code: beware i am a Python beginner) the Rein> display turns black for all derivatives (except for the Rein> 'zero order' derivative of course). Rein> What is going on? Is it me (probably) or is it imshow? In matplotlib0.54.2, the images must be normalized to the unit interval before color mapping or plotting. For example, in examples/image_demo2.py, notice that I do A *= 1.0/max(A) Your images are black, I think, because you haven't normalized them. I've done a lot of work on matplotlib images since 0.54.2, with new fixes and features. Normalization is handled by default in the 0.60 release candidate. When I run your (very nice) example with my development version of matplotlib, it produces this image for figure 2 ( I added "show" at the end of your script, but it was otherwise unaltered) http://nitace.bsd.uchicago.edu:8080/files/share/fig2.png which looks right to me. I'm always interested in nice screenshots for the web site, so please consider donating this example! I've uploaded the 0.60d release candidate to http://nitace.bsd.uchicago.edu:8080/files/share/matplotlib0.60d.tar.gz Can you compile matplotlib yourself or are you using a binary distribution? If you can try the development version linked above, that would solve two problems: you'll get enhanced image support and I'll get a tester for my image changes. If you do so, see the updated help for the image related commands: imshow, figimage, clim, jet, gray and the new matplotlibrc parameters image.* In the figure 2 image from your script, I notice that there the titles overlap the images above. Here is how you can control this (requires matplotlib 0.60d) # pass in these keywor args to title. The y location is the y # text coordinate in axes coords (0,0 is lower left, 1,1 is # upper right). You can use **somedict in place of keyword # args in python offsets = {'y':1.0, 'fontsize':10} _gDplot(1,0,0) title(r'$G$', **offsets) _gDplot(5,1,0) title(r'$G_x$', **offsets) _gDplot(6,0,1) title(r'$G_y$', **offsets) _gDplot(9,2,0) title(r'$G_{xx}$', **offsets) _gDplot(10,1,1) title(r'$G_{xy}$', **offsets) _gDplot(11,0,2) title(r'$G_{yy}$', **offsets) _gDplot(13,3,0) title(r'$G_{xxx}$', **offsets) _gDplot(14,2,1) title(r'$G_{xxy}$', **offsets) _gDplot(15,1,2) title(r'$G_{xyy}$', **offsets) _gDplot(16,0,3) title(r'$G_{yyy}$', **offsets) Cheers, JDH 
From: Rein van den Boomgaard (UvA) <rein@sc...>  20040707 07:45:08

Dear All, as i'm new to this list let me first introduce myself before posting a question. I'm working in the field of image processing and computer vision for a long time now and i am a long time matlab user. Up to now i didn't like any of the matlab clones but it seems that numarray+matplotlib might be a winner here. I started using numarray+matplotlib by coding the Gaussian derivative convolutions. Calculating a derivative (by convolving with the derivative of the Gaussian function) will lead to an image(array) with both positive and negative values. Although imshow should deal with that (as far as i understand the code: beware i am a Python beginner) the display turns black for all derivatives (except for the 'zero order' derivative of course). What is going on? Is it me (probably) or is it imshow? Attached you find the code. Regards Rein van den Boomgaard University of Amsterdam The Netherlands # ========================================================== # Gaussian Derivatives # # Copyright (c) Rein van den Boomgaard # Vision Consultancy & Training # rein@... # # ========================================================== import types from numarray import * from numarray.nd_image import * def gD( image, scales, orders, nscales=4, mode='reflect' ): scales = _normalize_sequence(scales,image) orders = _normalize_sequence(orders,image) result = image.copy() ndims = len( image.shape ) for i in range(0,ndims): N = ceil(scales[i]*nscales) x = arange( N, N ) w = GaussianDerivativeFunction( x, scales[i], orders[i] ) result = convolve1d( result, w, i, mode = mode ) return( result ) def HermiteH( n, x ): """ Hermite Poynomial """ if n==0: return 0*x + 1 elif n==1: return 2*x else: return 2*x*HermiteH( n1, x )  2*(n1)*HermiteH(n2,x) def GaussianDerivativeFunction( x, scale, order ): scale = float(scale) g = 1.0 / ( scale * sqrt(2*pi) ) * exp(  x**2 / (2*scale**2) ) return (1.0 / (scale*sqrt(2.0)))**order * \ HermiteH( order, x / (scale * sqrt(2)) ) * g # the _normalize_sequence function is copied from numarray # because i cant import it...??? def _normalize_sequence(input, array): """If input is a scalar, create a sequence of length equal to the rank of array by duplicating the input. If input is a sequence, check if its length is equalt to the lenght of array. """ if isinstance(array, ArrayType): rank = len(array.shape) else: rank = 1 if (isinstance(input, (types.IntType, types.LongType, types.FloatType))): normalized = [input] * rank else: if isinstance(input, numarray.numarraycore.NumArray): normalized = list(input) else: normalized = input if len(normalized) != rank: error = "sequence argument must have length equal to input rank" raise RuntimeError, error return normalized if __name__ == "__main__": from matplotlib import use, interactive from matplotlib.matlab import * PLOT_1D_GAUSS = 1 PLOT_GAUSSIAN_DERIVATIVES = 1 PLOT_TIMING = 1 if PLOT_1D_GAUSS: # testing the Gaussiand Derivatives x=arange(5,5,.01) figure(1) clf() plot(x,GaussianDerivativeFunction(x,1,0)) plot(x,GaussianDerivativeFunction(x,1,1)) plot(x,GaussianDerivativeFunction(x,1,2)) plot(x,GaussianDerivativeFunction(x,1,3)) plot(x,GaussianDerivativeFunction(x,1,4)) xlabel(r'$x$') ylabel(r'$G_n(x)$') title('Gaussian Derivatives') if PLOT_GAUSSIAN_DERIVATIVES: scale = 9 def _gDplot(n,ox,oy): subplot(4,4,n) axis('off') imshow( gD(a,scale,(ox,oy)) ) a = zeros( (64,64) ) * 1.0 a[32,32] = 1.0 figure(2) axis('off') title('Gaussian Derivatives') _gDplot(1,0,0) title(r'$G$') _gDplot(5,1,0) title(r'$G_x$') _gDplot(6,0,1) title(r'$G_y$') _gDplot(9,2,0) title(r'$G_{xx}$') _gDplot(10,1,1) title(r'$G_{xy}$') _gDplot(11,0,2) title(r'$G_{yy}$') _gDplot(13,3,0) title(r'$G_{xxx}$') _gDplot(14,2,1) title(r'$G_{xxy}$') _gDplot(15,1,2) title(r'$G_{xyy}$') _gDplot(16,0,3) title(r'$G_{yyy}$') if PLOT_TIMING: from timeit import Timer niter = 5 def time_gD(scale): t = Timer( "b=gD(a,%d, 0)" % scale, "from __main__ import gD,a" ) timing = t.timeit(number=niter ) print( timing/niter ) return( timing/niter ) a = zeros( (256,256) ) * 1.0 a[128,128] = 1.0 figure(3) scales = array([1,3,5,7,9,11,15,19, 25,31,41,51]) print(type(scales)) plot( scales, map( time_gD, scales ), 'x' ) xlabel('Scale (px)') ylabel('Time (s)') title('Gaussian Convolution Timings') 