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Rewrite of image infrastructure#5718

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Merged
tacaswell merged 65 commits intomatplotlib:masterfrommdboom:image-interpolation
Feb 18, 2016

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mdboom
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This began as an attempt tofix#5490, but it turned out to be impossible to fix that without addressing a number of other issues.

There's a number of things this does. Let's start with fixing#5490.

The order of operations has changed from:

normalize -> colormap -> resize

to

normalize -> resize -> colormap

I had experimented with doing the normalizationafter the resize, but Agg is designed for data in range (0, 1). It might actually be more mathematically/perceptually correct to do the normalization before the resizing (which does linear interpolation modulo some filter) so that if a non-linear norm is being applied, the interpolation takes that into account.

BEFORE:

test

AFTER:

good

You can see the images no longer have the "red" that doesn't belong to the color map.

In addition:

  • image.py was almost completely rewritten. There was a lot of copy-and-pasted code in there that has now been factored out by having all the image classes inherit from the same base class.
  • Fewer data type conversions are necessary in the image pipeline. In particular, the resampler can resample RGBA data in floating-point directly, so there's no need to convert it to 8-bit ints and back.
  • Thesvg.image_noscale rcParam is not longer supported. One can get the same behavior by settinginterpolation=None on images orimage.interpolation: None rcParam globally.
  • The experimental feature where one could "skew" an image by setting the private member_image_skew_coordinate has been removed. One can set the transform on the image object or its parent Axes to get the same behavior, and this will be understood by other artists as well so I think is much preferred.
  • Non-linear Axis types (log, symlog etc.) are now applied to the images. It should theoretically be possible to support projections as well, but our projections are currently broken with images for other reasons. I think I'll implement that as a follow-on to this (since it's not a regression that we don't support arbitrary projections with images).
  • I changed the default ofimage.resample toTrue. The difference there isresample: Falseonly applies interpolation when upsampling.resample: True applies it in both directions. And it has really odd behavior when one dimension is upsampled and the other is downsampled. I believe the history of this is thatresample: False is the old behavior before our Agg backend had full-on resampling in it, and the flag was maintained to allow for backward-compatible performance. I think it's high-time to use the more modern approach by default, especially wrt the upcoming 2.0 release.
  • Formerly, there was image resampling code in both the_image extension and the agg backend, apparently so that the Agg backend could apply an affine transform to the image like some of our vector backends. To reduce the number of complex code paths, the Agg backend has been reverted to being a "dumb" backend that can't resize an image and all resampling happens before it hits the backend. This doesn't break theinterpolation='none' feature where an exact copy of the original data is stored in a vector file.
  • This revealed a bug in Agg itself when resampling greyscale images, that I plan to report upstream.
  • Thedraw_image backend API has been simplified. It now simply takes an optional affine transform if the backend can handle transforming the image itself (this is true of all vector backends, and none of the others).
  • A whole slew of C++ code to manage Image buffers has been removed. This code pre-dated the requirement of Numpy in matplotlib, so it needed to be there just to store the data. All of this has been replaced with direct usage of Numpy, and a single functional interface to resample images.

@tacaswelltacaswell added this to thenext major release (2.0) milestoneDec 22, 2015
@tacaswell
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close#3931 as well.

Might be related to#3057#1441 and#4584

@WeatherGod
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Away from my computer at the moment. Will pushing all of this stuff down
into AGG break things for non-agg backends such as macosx and Cairo?
On Dec 22, 2015 5:19 PM, "Thomas A Caswell"notifications@github.com
wrote:

close#3931#3931 as
well.

Might be related to#3057
#3057#1441
#1441 and#4584
#4584


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#5718 (comment)
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@mdboom
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Away from my computer at the moment. Will pushing all of this stuff down into AGG break things for non-agg backends such as macosx and Cairo?

Our image interpolation is already done using the_image extension, which has always used Agg (but notbackend_agg) for the heavy lifting, for all backends.

The change here (and I admit it's confusing and I wasn't very clear) is that thebackend_agg used to do interpolation as well under certain circumstances (basically when interpolation='none' or there was a skew/rotation) rather than the_image extension. This was quite almost exactly the same code in_image and_backend_agg, except the latter supported rotation/skew. By moving rotation/skew into_image and then removing all image resampling from_backend_agg, the other raster backends (Cairo, OSX) now have the ability to rotate and skew images as well now and are on par with the features in the Agg backend in that regard for the first time.

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Ah, that is much clearer! Sometimes I forget the distinction between the
use of agg and backend_agg.
On Dec 23, 2015 11:31 AM, "Michael Droettboom"notifications@github.com
wrote:

Away from my computer at the moment. Will pushing all of this stuff down
into AGG break things for non-agg backends such as macosx and Cairo?

Our image interpolation is already done using the _image extension, which
has always used Agg (but not backend_agg) for the heavy lifting, for all
backends.

The change here (and I admit it's confusing and I wasn't very clear) is
that the backend_agg used to do interpolation as well under certain
circumstances (basically when interpolation='none' of there was a
skew/rotation) rather than the _image extension. This was quite almost
exactly the same code in _image and _backend_agg, except the latter
supported rotation/skew. By moving rotation/skew into _image and then
removing all image resampling from _backend_agg, the other raster
backends (Cairo, OSX) now have the ability to rotate and skew images as
well now and are on par with the features in the Agg backend in that regard
for the first time.


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#5718 (comment)
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@mdboommdboom mentioned this pull requestDec 29, 2015
20 tasks
@mdboommdboomforce-pushed theimage-interpolation branch 6 times, most recently fromf677ac8 to615c0dcCompareDecember 30, 2015 20:10
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@tacaswell

Might be related to#3057#1441 and#4584

An extra commit has been added here to fix#3057.

This PR fixes#1441 (though by happy accident)

#4584 doesn't hit the image code at all, so this is orthogonal to fixing that.

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This is now passing on Travis, has some "What's New" entries, and is ready for final review.

@tacaswell: Thanks to the pointers to related issues. That was helpful for verifying some things outside of the existing test suite.

@WeatherGod
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I'll give the C++ code a thorough read through tomorrow.
On Dec 30, 2015 5:40 PM, "Michael Droettboom"notifications@github.com
wrote:

This is now passing on Travis, has some "What's New" entries, and is ready
for final review.

@tacaswellhttps://github.com/tacaswell: Thanks to the pointers to
related issues. That was helpful for verifying some things outside of the
existing test suite.


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#5718 (comment)
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@tacaswell
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master...matplotlib:6659dd704336b7575d8234b83a789531abd721ff

0335602...9097d0a

are links to the diffs excluding the svg files

@tacaswell
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examples/api/demo_affine_image.py picked up the execute bit for all, was that intentional?

@tacaswell
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in backend_svg it looks like the alpha value is only applied if the transform is notNone, is that correct?

@tacaswell
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docstring of class ScalarMappable.to_rgba needs some updating.

@tacaswell
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L1251 isfigure.py needs to be updated to match the same logic inAxes re if images can be compositied.

@tacaswell
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Would it be better to usedtype.kind (https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.dtype.kind.html#numpy.dtype.kind) instead ofissubclass(A.dtype.type, np.floating) ?

@tacaswell
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I have a some what pathological example where colors not from the color map end up in the final image.

importnumpyasnpimportmatplotlib.pyplotaspltX,Y=np.ogrid[-1000:1000,-1000:100000]R=np.sin((X**2+Y**2)/1000)fig,ax=plt.subplots()ax.imshow(R,cmap='viridis',interpolation='nearest')ax.set_aspect('auto')ax.set_xlim((28860.457192461989,31279.898576044176))ax.set_ylim((1044.2529963498005,906.46842593439703))ax.set_xscale('log')plt.show()

so2

To be fair, this is a couple layers of stacked Moire patterns as out here the grid isway under sampling the underlying function to begin with and then it is being way down sampled.

I also found that the initial draw is faster under the new code, but panning is snappier under the old code.

@mdboom
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examples/api/demo_affine_image.py picked up the execute bit for all, was that intentional?

Nope. Will fix. My emacs is set up to helpfully apply the executable bit if there's is#! in the file. One could argue a file with a hashbang without the executable bit is degenerate. Probably best to just remove the hangbang to be consistent with most of the other examples.

in backend_svg it looks like the alpha value is only applied if the transform is not None, is that correct?

Yes. If there is a transform passed, it gets exactly the original data, so the alpha needs to be applied by the renderer. If there is no transform passed, the alpha has already been applied byimage.py. The reason for this is to properly support alpha-blending when images are pre-composited together. This did, however, illuminate one bug in the application of alpha inimage.py and the fact that PDF isn't currently doing this correctly. PS simply doesn't support alpha blending so not much we can do there.

docstring of class ScalarMappable.to_rgba needs some updating.

done.

L1251 is figure.py needs to be updated to match the same logic in Axes re if images can be compositied.

Not sure I follow. Isn't that already the case? Compositing happens if there's

  • more than one image
  • the backend supports it
  • none of the images haveinterpolation == 'none'

It looks like it's the same in both places. (Could be refactored into a shared function, I suppose...)

Would it be better to use dtype.kind (https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.dtype.kind.html#numpy.dtype.kind) instead of issubclass(A.dtype.type, np.floating) ?

I forgot about that... That's certainly more readable.

@tacaswell
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an update on my pathological case, if you zoom way in in the png it looks correct, the red is coming from something outside of mpl doing blending in RGB space.

Re L1251 Sorry, I was reading the first half of the diff and that was changed in the second half.

@mdboom
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an update on my pathological case, if you zoom way in in the png it looks correct, the red is coming from something outside of mpl doing blending in RGB space.

What do you think that would be? -- "we control all the pixels" all the way down (at least for PNG). As far as I can tell, the "pinky" color is some sort of perceptual trick of the eyes when you have two very contrasting colors of only one pixel right next to each other (and personally, my display is 220dpi, so the pixels are quite small). If I zoom way in on the screenshot in the GIMP and use the eyedropper tool, however, I don't actually see any colors from outside the colormap. All this makes a very good stress test, though.

@tacaswell
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An optical illusions makes more sense. The display software (web browser / gimp) might be down-sampling to display, but Qt should not be.

If I take a screen shot of this page and zoom in on the (downsized) png gimp does show some red.

@mdboom
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If I take a screen shot of this page and zoom in on the (downsized) png gimp does show some red.

I see. As you imply, I don't think there's anything we can do about that. As long as that doesn't happen fromsavefig or a screen shot directly taken from a GUI backend, we're fine.

@jankatins
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You can download the image failures from the artifact tab of each not passing appveyor run. They include a nice html file which has the failing tests at the top.

->https://ci.appveyor.com/project/mdboom/matplotlib/build/1.0.832/job/cnc0brawn35inqpw/artifacts

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I think this is working again, though Appveyor seems stopped up. Feel free to merge once passing.

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Time for#6004...

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This "is the test running too slow" test seems a bit finicky. AppVeyor is passing otherwise.

tacaswell added a commit that referenced this pull requestFeb 18, 2016
@tacaswelltacaswell merged commit5b74696 intomatplotlib:masterFeb 18, 2016
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Merged despite the too-slow failure.

tacaswell added a commit that referenced this pull requestFeb 18, 2016
Rewrite of image infrastructureConflicts:lib/matplotlib/tests/test_axes.py    - do not back-port appveyor spceific limitslib/matplotlib/tests/test_image.py    - do not backport the jpeg_alpha testsetupext.py   - do not include windows/appveyor related changes
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backported to v2.x as6595139

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\o/

@tacaswelltacaswell mentioned this pull requestJun 19, 2017
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Don't interpolate images in RGB space
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