NotificationsYou must be signed in to change notification settings
Fork8.1k
Star22.1k

Commit146e9d0

committed

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

File tree

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`

0 commit comments

Comments

(0)

Movatterモバイル変換

Navigation Menu

Search code, repositories, users, issues, pull requests...

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Uh oh!

Commit146e9d0

File tree

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`

0 commit comments