I've looked all over the place through both the Python and C code, and I don't see any premultiplication of alphas at any stage before the pixels are passed off to agg, and neither can I find any place where the alphas are "unmultiplied" on the way back from agg to the backend for rendering.
It's very possible that I missed it, but I would have to miss it in two places (premultiply and the unmultiply). It looks to me like the output from agg ends up getting passed on directly to the renderer, which as far as I know, just uses straight alpha. The WxAgg renderer, for example, just creates a wx.Bitmap out of the pixels and blits it. Which means that any image going through agg's filters will not be correct if it has any pixels with alpha != 0 or != 255. [Using PIL images because they are simple to talk about...but the PIL image could alternatively be an image.py image] As far as I can tell, the image pixels current go through a pipeline like the following: [1] PIL Image -> _image image -> agg operations -> modified and/or resized _image image -> renderer If agg expects premultiplied alpha, the procedure should look something like: [2] PIL Image -> _image image -> premultiply alphas ->agg options -> unmultiply alphas -> modified and/or resized _image image -> renderer I personally don't like pipeline [2] because picture detail is lost in the "unmultiply alphas" stage. Better to use straight alpha all the way through. So long as matplotlib is using only a subset of agg algorithms that work no matter whether the alphas are premultiplied or not, I would think that the most reasonable route was the one that I took; to always pass straight alphas (sticking with pipeline [1]), and modify the agg source slightly to fit matplotlib's approach (i.e., remove the clipping there). I hope that I'm not way off base (I have a sneaking feeling that I am :O ), and hope this helps. I've verified on both Linux and Windows that removing the alpha-clip lines from agg_span_image_filter_rgba.h, rebuilding matplotlib, and replacing _image.so/_image.pyd and _backend_agg.so/_backend_agg.pyd does the trick (along with passing straight alphas). So far, I've seen no ill effects on any of my plots, but I'm also not in a position to run the pixel-by-pixel comparison matplotlib tests. On Wed, Oct 19, 2011 at 7:26 PM, Daniel Hyams <dhy...@gmail.com> wrote: > There has to be something else in play here. I'll try to keep this > short, but the summary is this: I can get the transparency to look > right, but only if 1) I put "straight" alpha in image, not > premultiplied, and 2) I hack agg to remove specificially every > instance of the lines of code that you refer to above. > > Why this is, I don't know. Hopefully I'm still misusing something. > However, it behaves as if the clipping of alpha in the agg library is > corrupting the alpha channel. I also submit that I could have broken > some other transparency capabilities of matplotlib, because I don't > know what other routines use what I hacked....I did check a few > transparent polygons and such though, and everything seemed to be > fine. > > I know that the agg library has been around for quite a long time, so > that also means that such a basic bug is unlikely. > > I've reattached the (slightly modified) script that reproduces the > problem, along with a sample image that it uses. The only change to > the script is right at the top, where a different image is read, a > quick statement is placed to add an alpha channel if there is not > already one, and I'm attempting to use premultiplied alphas. I've > also attached a screenshot of the output. Notice that in this case, > both "transparent" images look wrong. > > Now, if I 1) hack agg to remove the alpha clipping, and 2) modify the > one line in the attached python script so that I use straight alpha, > everything looks right. Specifically, I removed every instance of the > code below from xxxx, rebuilt all of the matplotlib .so's, and > specifically replaced _image.so and _backend_agg.so in my matplotlib > distribution. > > if(fg[order_type::A] > base_mask) fg[order_type::A] > = base_mask; > if(fg[order_type::R] > fg[order_type::A]) > fg[order_type::R] = fg[order_type::A]; > if(fg[order_type::G] > fg[order_type::A]) > fg[order_type::G] = fg[order_type::A]; > if(fg[order_type::B] > fg[order_type::A]) > fg[order_type::B] = fg[order_type::A]; > > > > > On Wed, Oct 19, 2011 at 2:34 PM, Daniel Hyams <dhy...@gmail.com> wrote: >> >> Ah, thanks so much Michael! That explanation helps a great deal; I >> was always considering things in "straight alpha" format, not even >> knowing that there was alternative. >> >> I'll play with this tonight; I don't see any problem getting the thing >> working, though, now that I know what agg expects to see... >> >> And yes, alpha support in the image class would be very helpful ;) >> >> On Wed, Oct 19, 2011 at 2:16 PM, Michael Droettboom <md...@stsci.edu> wrote: >> > You are right that Agg is doing the resizing here. Agg expects >> > premultiplied alpha. See [1] for information about what that means. >> > >> > [1] http://en.wikipedia.org/wiki/Alpha_compositing >> > >> > After Agg interpolates the pixel values, to prevent oversaturation it >> > truncates all values to be less than alpha (which makes sense if everything >> > is assumed to be premultiplied alpha). Arguably, the bug here is that >> > nearest neighbor (which doesn't have to do any blending) doesn't perform >> > the >> > truncation step -- then both would look "wrong". >> > >> > It happens in this code snippet in span_image_filter_rgba: (base_mask is >> > 255) >> > >> > if(fg[order_type::A] > base_mask) fg[order_type::A] >> > = base_mask; >> > if(fg[order_type::R] > fg[order_type::A]) fg[order_type::R] >> > = fg[order_type::A]; >> > if(fg[order_type::G] > fg[order_type::A]) fg[order_type::G] >> > = fg[order_type::A]; >> > if(fg[order_type::B] > fg[order_type::A]) fg[order_type::B] >> > = fg[order_type::A]; >> > >> > So, the solution to make a partially transparent image is to not do: >> > >> > pix[:,:,3] = 127 >> > >> > but instead, do >> > >> > pix *= 0.5 >> > >> > Of course, the real fix here is to support alpha blending properly in the >> > image class, then the user wouldn't have to deal with such details. A bug >> > should probably be filed in the matplotlib issue tracker for this. >> > >> > Mike >> > >> > On 10/19/2011 12:23 PM, Daniel Hyams wrote: >> > >> > [Sorry, I keep getting tripped up with HTML mail....resent in ascii, >> > and resaved one of the attachment png's to make it smaller.] >> > >> > >> > Example script attached (PIL required). Basically, if I impose a >> > specific value into an image's alpha channel and use any interpolation >> > scheme other than 'nearest', there appears gray all where the figure >> > didn't have any color to begin with. I've also attached a screenshot >> > of the output of the script on my machine. >> > >> > Hopefully I'm doing something wrongly? >> > >> > I chased the problem and managed to hack in a solution that fixes the >> > problem, but it's extremely inefficient...basically, in matplotlib's >> > image.py, routine BboxImage.make_image, you can create two images >> > there....one with no alpha channel (call it imRGB) and one with (call >> > it imRGBA). Go through all of the routine, doing exactly the same >> > things to both of the images *except* for the interpolation, which is >> > set to 'nearest' for imRGBA. Then, rip the colors out of imRGB, the >> > alpha channel off of imRGBA, and put them together....go through all >> > of the routine again with this composited image, and it works. I >> > know...I told you it was bad ;) >> > >> > The problem seems to be in the "resize" call in that routine...resize, >> > which calls into C code, does not appear to handle things correctly >> > when the alpha is anything other than 255's across the board. It >> > might be a problem in the agg routines, but hopefully it is just maybe >> > a misuse of the agg routines. >> > >> > The behavior seems to be backend independent as far as I could test (I >> > tried with wxagg and tk backends). I am using mpl 1.0.0 on Windows if >> > it matters. >> > >> > >> > -- >> > Daniel Hyams >> > dhy...@gmail.com >> > >> > ------------------------------------------------------------------------------ >> > All the data continuously generated in your IT infrastructure contains a >> > definitive record of customers, application performance, security >> > threats, fraudulent activity and more. Splunk takes this data and makes >> > sense of it. Business sense. IT sense. Common sense. >> > http://p.sf.net/sfu/splunk-d2d-oct >> > >> > _______________________________________________ >> > Matplotlib-users mailing list >> > Matplotlib-users@lists.sourceforge.net >> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >> > >> > >> > ------------------------------------------------------------------------------ >> > The demand for IT networking professionals continues to grow, and the >> > demand for specialized networking skills is growing even more rapidly. >> > Take a complimentary Learning@Ciosco Self-Assessment and learn >> > about Cisco certifications, training, and career opportunities. >> > http://p.sf.net/sfu/cisco-dev2dev >> > _______________________________________________ >> > Matplotlib-users mailing list >> > Matplotlib-users@lists.sourceforge.net >> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >> > >> > >> >> >> >> -- >> Daniel Hyams >> dhy...@gmail.com > > > > -- > Daniel Hyams > dhy...@gmail.com > -- Daniel Hyams dhy...@gmail.com ------------------------------------------------------------------------------ The demand for IT networking professionals continues to grow, and the demand for specialized networking skills is growing even more rapidly. 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