Re: [matplotlib-devel] Affine Transformations on images
Dear List, dear Michael, > Looks good. Does matplotlib still pass all regression tests with this > change? It does pass all regression tests that were passed with the git version I started with. (There were 10 failures which are still there). In the meantime, I also wrote a class that already uses my extension. With it, you can plot rotated or sheared images with all backends. (There were some quirks to get sheard images on some backends, see examples/api/demo_affine_image.py, but it worked on some backends only). While writing it, I found some inconsistencies in matplotlib: - bounding boxes are not correctly transformed. BboxBase.tranformed only transformes the outer points of a bounding box, if you rotate it, this will give wrong results for several angles. I wrote a function that should be correct for all affine transformations: def transform_bbox(bbox, trans): x0, y0, x1, y1 = bbox.extents tx0, ty0 = trans.transform([x0, y0]) tx1, ty1 = trans.transform([x1, y1]) tx2, ty2 = trans.transform([x1, y0]) tx3, ty3 = trans.transform([x0, y1]) return Bbox.from_extents(min(tx0, tx1, tx2, tx3), min(ty0, ty1, ty2, ty3), max(tx0, tx1, tx2, tx3), max(ty0, ty1, ty2, ty3)) - The other inconsistency is that within matplotlib, extents are defined different: in imshow, the parameter extent expects the order (left, right, bottom, top), while BboxBase.extents is (left, bottom, right, top). This should be changed in the future, maybe the move to python 3 is a good time for that? But now to my code to draw images. It's a new class inheriting AxesImage, but is supposed to once replace AxesImage, as it is compatible. I'm re-writing _draw_unsampled_image, to actually draw a sampled image. Thats only because make_image, the method to be rewritten for a sampled image, is not flexible enough (the caller draws the image, but there is no way for make_image to tell where that image is to be put). In the future, the methods should be renamed (it's a private method, so that's no problem). class ShearImage(AxesImage): def _check_unsampled_image(self, _): return True def _draw_unsampled_image(self, renderer, gc): """ actually, draw sampled image. This method is more flexible than make_image """ mag = renderer.get_image_magnification() trans = Affine2D().scale(mag, mag) + self.get_transform() + \ self.axes.transData.get_affine() bbox = self.axes.bbox viewLim = transform_bbox(bbox, trans.inverted()) im, xmin, ymin, dxintv, dyintv, sx, sy = \ self._get_unsampled_image(self._A, self.get_extent(), viewLim) if im is None: return # I'm not if this check is required. -JJL im.set_interpolation(self._interpd[self._interpolation]) im.set_resample(self._resample) fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) im.set_bg( *bg) # uncomment the following line to see the extent to which the image # is drawn # im.set_bg(0, 0, 0, 100) numrows, numcols = im.get_size() ex = self.get_extent() tex = Bbox.from_extents([ex[0], ex[2], ex[1], ex[3]]) tex = transform_bbox(tex, trans) if tex.xmin < bbox.xmin: left = bbox.xmin tx = 0 else: left = tex.xmin tx = tex.xmin - bbox.xmin if tex.ymin < bbox.ymin: bottom = bbox.ymin ty = 0 else: bottom = tex.ymin ty = tex.ymin - bbox.ymin trans = Affine2D().scale(dxintv / numcols, dyintv / numrows).translate(xmin, ymin) + \ trans + \ Affine2D().translate(-bbox.xmin - tx, -bbox.ymin - ty) im.set_matrix(*trans.get_matrix()[:2, :].T.ravel()) width = min(tex.xmax, bbox.xmax) - left height = min(tex.ymax, bbox.ymax) - bottom if width <= 0 or height <= 0: return im.resize(width * mag, height * mag, norm=self._filternorm, radius=self._filterrad) im._url = self.get_url() renderer.draw_image(gc, left, bottom, im) Last but not least, a little script to test the above. It shows a rotated image. You can scale and move the image nicely. If you uncomment the line mentioned above in ShearImage code, you can see where the image is actually drawn, and you will see that only the necessary parts are drawn if the image is smaller than the entire axes. The test script follows: from pylab import * ax = axes() im = ShearImage(ax) im.set_data(fromfunction(lambda x, y: sin(x + y ** 2), (100, 100))) im.set_extent(im.get_extent()) transform = Affine2D().rotate_deg(30) im.set_transform(transform) ax.images.append(im) show() Greetings Martin -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator o
Re: [matplotlib-devel] Affine Transformations on images
On Tue, Jul 12, 2011 at 7:01 PM, Martin Teichmann wrote: > (There were some quirks to get sheard images on some backends, > see examples/api/demo_affine_image.py, but it worked on some backends > only). I didn't have time to go through your code carefully, but my understanding is that you rely on Agg to get a transformed image (resampled image) and provide that image to backends, right? Note that when interpolation mode is "none", resampling should be prohibited. There is a good reason why we implemented the "none" interpolation mode even though this is not supported for some backends. Overriding this behavior (which I guess is the case of you current ShearImage implementation. Please correct me if I'm wrong) won't be acceptable. Please makes sure that resampling of images only happens when the interpolation mode is not "none". Regards, -JJ -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev ___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Affine Transformations on images
I'm starting to get a better sense of the code now. One of the features of the current implementation is that images are resampled before going into the output of the vector backends, so that we can a) control file size and b) control the interpolation algorithm used. It looks like that separation is maintained with this approach, only difference is that images can be both prerotated as well as prescaled now. Is that correct? (I must admit, I've work on bits and pieces of the image code at the edges, but I don't understand it in great detail). It looks like overriding "draw_unsampled_image" is the wrong thing to do here, though. The purpose of that function is to draw an image in a vector backend without any resampling at all -- and here you seem to be adding that. It also only gets called when interpolation is "none" or "nearest", so your helpful changes currently don't work with other forms of interpolation. I think we need to either add a function "_draw_sampled_image", or modify make_image so it returns an offset with the new location to draw the image (because rotation may cause the bounds of the image to be shifted). This is great work, and is looking to be fairly significant. Would you be able to set up a personal git fork and branch for you work? It would make it easier for the rest of us to evaluate what you have done and kick the tires a bit. The outline of how to do this is here: http://matplotlib.sourceforge.net/devel/gitwash/index.html Cheers, Mike On 07/12/2011 06:01 AM, Martin Teichmann wrote: > Dear List, > dear Michael, > >> Looks good. Does matplotlib still pass all regression tests with this >> change? > It does pass all regression tests that were passed with the git version > I started with. (There were 10 failures which are still there). > > In the meantime, I also wrote a class that already uses my extension. > With it, you can plot rotated or sheared images with all backends. > (There were some quirks to get sheard images on some backends, > see examples/api/demo_affine_image.py, but it worked on some backends > only). > > While writing it, I found some inconsistencies in matplotlib: > > - bounding boxes are not correctly transformed. BboxBase.tranformed only > transformes the outer points of a bounding box, if you rotate it, this will > give > wrong results for several angles. I wrote a function that should be correct > for > all affine transformations: > > def transform_bbox(bbox, trans): > x0, y0, x1, y1 = bbox.extents > tx0, ty0 = trans.transform([x0, y0]) > tx1, ty1 = trans.transform([x1, y1]) > tx2, ty2 = trans.transform([x1, y0]) > tx3, ty3 = trans.transform([x0, y1]) > return Bbox.from_extents(min(tx0, tx1, tx2, tx3), min(ty0, ty1, ty2, > ty3), > max(tx0, tx1, tx2, tx3), max(ty0, ty1, ty2, ty3)) > > - The other inconsistency is that within matplotlib, extents are > defined different: > in imshow, the parameter extent expects the order (left, right, bottom, top), > while BboxBase.extents is (left, bottom, right, top). This should be changed > in > the future, maybe the move to python 3 is a good time for that? > > But now to my code to draw images. It's a new class inheriting AxesImage, > but is supposed to once replace AxesImage, as it is compatible. > > I'm re-writing _draw_unsampled_image, to actually draw a sampled image. > Thats only because make_image, the method to be rewritten for a sampled > image, is not flexible enough (the caller draws the image, but there is no way > for make_image to tell where that image is to be put). In the future, > the methods > should be renamed (it's a private method, so that's no problem). > > class ShearImage(AxesImage): > def _check_unsampled_image(self, _): > return True > > def _draw_unsampled_image(self, renderer, gc): > """ > actually, draw sampled image. This method is more flexible than > make_image > """ > mag = renderer.get_image_magnification() > trans = Affine2D().scale(mag, mag) + self.get_transform() + \ > self.axes.transData.get_affine() > bbox = self.axes.bbox > viewLim = transform_bbox(bbox, trans.inverted()) > > im, xmin, ymin, dxintv, dyintv, sx, sy = \ > self._get_unsampled_image(self._A, self.get_extent(), viewLim) > > if im is None: return # I'm not if this check is required. -JJL > im.set_interpolation(self._interpd[self._interpolation]) > im.set_resample(self._resample) > > fc = self.axes.patch.get_facecolor() > bg = mcolors.colorConverter.to_rgba(fc, 0) > im.set_bg( *bg) > # uncomment the following line to see the extent to which the image > # is drawn > # im.set_bg(0, 0, 0, 100) > numrows, numcols = im.get_size() > > ex = self.get_extent() > tex = Bbox.from_extents([ex[0], ex[2], ex[1], ex[3]]) > tex = transf
[matplotlib-devel] Plan for cutting v1.1.0?
Just wondering if there is any sort of agreement/target for cutting a v1.1.0 release? I think we are very close. Most of what I see that needs to be done are documentation-related (and I want to add a can_pan() function to mirror the can_zoom() function). Most documentation work that seems to remain are Animation module (and I would love to find out if there is some way to embed a video using sphinx) and the release-related documentation such as "what's new". Also, do we plan to now host the documentation and downloads on github, or are we still sticking with sourceforge for that? This looks to be a very nice release! Ben Root -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Plan for cutting v1.1.0?
On 07/13/2011 08:58 AM, Benjamin Root wrote: > Just wondering if there is any sort of agreement/target for cutting a > v1.1.0 release? I think we are very close. Most of what I see that > needs to be done are documentation-related (and I want to add a > can_pan() function to mirror the can_zoom() function). I think the last chunk of discussion is here: http://www.mail-archive.com/[email protected]/msg08276.html As I recall, John had time to help in June, but less after that. I don't think we ever reached an agreement as to schedule. It simply went into slide mode. > > Most documentation work that seems to remain are Animation module (and I > would love to find out if there is some way to embed a video using > sphinx) and the release-related documentation such as "what's new". > Also, do we plan to now host the documentation and downloads on github, > or are we still sticking with sourceforge for that? ipython has moved to github for their homepage and documentation, but as far as I can see, github has no facility for hosting downloads; the ipython tarballs are on scipy.org. > > This looks to be a very nice release! Question: simply abandon v1.0.x-maint without a release? Eric > Ben Root > -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev ___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Plan for cutting v1.1.0?
On Wed, Jul 13, 2011 at 2:54 PM, Eric Firing wrote: > As I recall, John had time to help in June, but less after that. I > don't think we ever reached an agreement as to schedule. It simply went > into slide mode. I am back from my vacation so I have time again. I figured since I was leaving and people were making fantastic progress on pull requests and tickets that it was best to wait. But I'm available now to help with the release. >> Most documentation work that seems to remain are Animation module (and I >> would love to find out if there is some way to embed a video using >> sphinx) and the release-related documentation such as "what's new". >> Also, do we plan to now host the documentation and downloads on github, >> or are we still sticking with sourceforge for that? > > ipython has moved to github for their homepage and documentation, but as > far as I can see, github has no facility for hosting downloads; the > ipython tarballs are on scipy.org. No strong opinions here. It probably makes sense to move as much stuff to github as possible now that the issue tracker has been moved, but the sf download stuff is pretty good now and I haven't investigated whether github offers equivalents. Also, our donations are processed through sf and they are non-negligible. I found this old post on github about accepting donations https://github.com/blog/57-getting-paid-the-open-source-way; not sure if this is still current. >> This looks to be a very nice release! > > Question: simply abandon v1.0.x-maint without a release? I would say yes here -- we can leave the branch around for a while in case there is a reason to cut a release from it, but I don't think it makes sense to do a release of the branch otherwise. -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev ___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Plan for cutting v1.1.0?
Eric Firing, on 2011-07-13 09:54, wrote: > ipython has moved to github for their homepage and documentation, but as > far as I can see, github has no facility for hosting downloads; the > ipython tarballs are on scipy.org. They are on scipy, github, and PyPI. Here are the ones for matplotlib: https://github.com/matplotlib/matplotlib/downloads > Question: simply abandon v1.0.x-maint without a release? this makes sense to me. let's just make sure there's nothing there that never made it into trunk, but should have. best, -- Paul Ivanov 314 address only used for lists, off-list direct email at: http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 signature.asc Description: Digital signature -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Matplotlib-devel Digest, Vol 62, Issue 6
Hi, I will be on vacation with limited email from July 14 to August 7, 2011. Bonjour, Je serai en conge du 14 juillet jusqu'au 7 aout, 2011. -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev ___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Plan for cutting v1.1.0?
On 07/13/2011 10:20 AM, Paul Ivanov wrote: > Eric Firing, on 2011-07-13 09:54, wrote: >> ipython has moved to github for their homepage and documentation, but as >> far as I can see, github has no facility for hosting downloads; the >> ipython tarballs are on scipy.org. > > They are on scipy, github, and PyPI. Here are the ones for > matplotlib: > > https://github.com/matplotlib/matplotlib/downloads I don't know how I missed the "Downloads" button on the RHS of https://github.com/matplotlib/matplotlib. It looks like we would need a much larger space allocation to include all the binary distributions. > > >> Question: simply abandon v1.0.x-maint without a release? > > this makes sense to me. let's just make sure there's nothing > there that never made it into trunk, but should have. Fine with me, also. Eric -- AppSumo Presents a FREE Video for the SourceForge Community by Eric Ries, the creator of the Lean Startup Methodology on "Lean Startup Secrets Revealed." This video shows you how to validate your ideas, optimize your ideas and identify your business strategy. http://p.sf.net/sfu/appsumosfdev2dev ___ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
