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Filter images in premultiplied alpha mode.

Test e.g. with```pythonimport matplotlib.pyplot as pltimport numpy as npfig = plt.figure(figsize=(6, 6))img = np.zeros((5, 5, 4))img[..., 1] = 1.img[..., 3] = np.tril(np.ones((5, 5)))fig.add_subplot(221).imshow(img, interpolation="bilinear")fig.add_subplot(222).imshow((img * 0xff).astype("u1"), interpolation="bilinear")fig.add_subplot(212).imshow( np.where( np.mgrid[:100, :200][0] > np.random.randint(100, size=200), np.arange(100)[:, None], np.nan), cmap="Greens", interpolation="antialiased", interpolation_stage="rgba")plt.show()```

1 parent9f7b3dd commit146e9d0Copy full SHA for 146e9d0

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4 files changed

+36

-19

lines changed

lib/matplotlib
- image.py
- tests
  - baseline_images/test_image
    - downsampling_speckle.png
    - rgba_antialias.png
  - test_image.py

4 files changed

+36

-19

lines changed

`‎lib/matplotlib/image.py`

Lines changed: 35 additions & 18 deletions

Original file line number	Diff line number	Diff line change
`@@ -496,37 +496,54 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,`
`496`	`496`	`out_alpha*=_resample(self,alpha,out_shape,t,resample=True)`
`497`	`497`	`# mask and run through the norm`
`498`	`498`	`resampled_masked=np.ma.masked_array(A_resampled,out_mask)`
`499`		`-output=self.norm(resampled_masked)`
	`499`	`+res=self.norm(resampled_masked)`
`500`	`500`	`else:`
`501`	`501`	`ifA.ndim==2:# interpolation_stage = 'rgba'`
`502`	`502`	`self.norm.autoscale_None(A)`
`503`	`503`	`A=self.to_rgba(A)`
`504`	`504`	`alpha=self.get_alpha()`
	`505`	`+post_apply_alpha=False`
`505`	`506`	`ifalphaisNone:# alpha parameter not specified`
`506`	`507`	`ifA.shape[2]==3:# image has no alpha channel`
`507`		`-output_alpha=255ifA.dtype==np.uint8else1.0`
`508`		`-else:`
`509`		`-output_alpha=_resample(# resample alpha channel`
`510`		`-self,A[...,3],out_shape,t)`
`511`		`-output=_resample(# resample rgb channels`
`512`		`-self,_rgb_to_rgba(A[..., :3]),out_shape,t)`
	`508`	`+alpha=np.uint8(0xff)ifA.dtype==np.uint8else1.0`
	`509`	`+A=np.dstack([A,np.full(A.shape[:2],alpha)])`
`513`	`510`	`elifnp.ndim(alpha)>0:# Array alpha`
`514`	`511`	`# user-specified array alpha overrides the existing alpha channel`
`515`		`-output_alpha=_resample(self,alpha,out_shape,t)`
`516`		`-output=_resample(`
`517`		`-self,_rgb_to_rgba(A[..., :3]),out_shape,t)`
	`512`	`+A=np.dstack([A[..., :3],alpha])`
`518`	`513`	`else:# Scalar alpha`
`519`	`514`	`ifA.shape[2]==3:# broadcast scalar alpha`
`520`		`-output_alpha= (255*alpha)ifA.dtype==np.uint8elsealpha`
	`515`	`+ifA.dtype==np.uint8:`
	`516`	`+alpha=np.uint8(0xff*alpha)`
	`517`	`+A=np.dstack([A,np.full(A.shape[:2],alpha)])`
`521`	`518`	`else:# or apply scalar alpha to existing alpha channel`
`522`		`-output_alpha=_resample(self,A[...,3],out_shape,t)*alpha`
`523`		`-output=_resample(`
`524`		`-self,_rgb_to_rgba(A[..., :3]),out_shape,t)`
`525`		`-output[...,3]=output_alpha# recombine rgb and alpha`
`526`		`-`
`527`		`-# output is now either a 2D array of normed (int or float) data`
	`519`	`+post_apply_alpha=True`
	`520`	`+# Resample in premultiplied alpha space. (TODO: Consider`
	`521`	`+# implementing premultiplied-space resampling in`
	`522`	`+# span_image_resample_rgba_affine::generate?)`
	`523`	`+ifA.dtype==np.uint8:`
	`524`	`+# Temporarily increase bitdepth to avoid precision loss.`
	`525`	`+A=A.astype(np.uint16)`
	`526`	`+A[..., :3]*=A[...,3:]`
	`527`	`+A[...,3]*=np.uint16(0x100)`
	`528`	`+res=_resample(self,A,out_shape,t)`
	`529`	`+out_alpha=res[...,3:]/0x100`
	`530`	`+rgb_pre=res[..., :3]`
	`531`	`+rgb=rgb_pre/np.where(out_alpha,out_alpha,1)`
	`532`	`+res=np.dstack([rgb,out_alpha])`
	`533`	`+ifpost_apply_alpha:`
	`534`	`+res[...,3]*=alpha`
	`535`	`+res=np.minimum(res.round(),0xff).astype(np.uint8)`
	`536`	`+else:`
	`537`	`+A[..., :3]*=A[...,3:]`
	`538`	`+res=_resample(self,A,out_shape,t)`
	`539`	`+np.divide(res[..., :3],res[...,3:],out=res[..., :3],`
	`540`	`+where=res[...,3:]!=0)`
	`541`	`+ifpost_apply_alpha:`
	`542`	`+res[...,3]*=alpha`
	`543`	`+`
	`544`	`+# res is now either a 2D array of normed (int or float) data`
`528`	`545`	`# or an RGBA array of re-sampled input`
`529`		`-output=self.to_rgba(output,bytes=True,norm=False)`
	`546`	`+output=self.to_rgba(res,bytes=True,norm=False)`
`530`	`547`	`# output is now a correctly sized RGBA array of uint8`
`531`	`548`
`532`	`549`	`# Apply alpha after if the input was greyscale without a mask`

Original file line number	Diff line number	Diff line change
`@@ -514,7 +514,7 @@ def test_image_composite_background():`
`514`	`514`	`ax.set_xlim([0,12])`
`515`	`515`
`516`	`516`
`517`		`-@image_comparison(['image_composite_alpha'],remove_text=True)`
	`517`	`+@image_comparison(['image_composite_alpha'],remove_text=True,tol=0.07)`
`518`	`518`	`deftest_image_composite_alpha():`
`519`	`519`	`"""`
`520`	`520`	`Tests that the alpha value is recognized and correctly applied in the`

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Commit146e9d0

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4 files changed

4 files changed

`‎lib/matplotlib/image.py`

`‎lib/matplotlib/tests/baseline_images/test_image/downsampling_speckle.png`

`‎lib/matplotlib/tests/baseline_images/test_image/rgba_antialias.png`

`‎lib/matplotlib/tests/test_image.py`

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