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Filter images in premultiplied alpha mode.
Test e.g. with ```python import matplotlib.pyplot as plt import numpy as np fig = 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() ```
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‎lib/matplotlib/image.py

Lines changed: 35 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -496,37 +496,54 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
496496
out_alpha *= _resample(self, alpha, out_shape, t, resample=True)
497497
# mask and run through the norm
498498
resampled_masked = np.ma.masked_array(A_resampled, out_mask)
499-
output = self.norm(resampled_masked)
499+
res = self.norm(resampled_masked)
500500
else:
501501
if A.ndim == 2: # interpolation_stage = 'rgba'
502502
self.norm.autoscale_None(A)
503503
A = self.to_rgba(A)
504504
alpha = self.get_alpha()
505+
post_apply_alpha = False
505506
if alpha is None: # alpha parameter not specified
506507
if A.shape[2] == 3: # image has no alpha channel
507-
output_alpha = 255 if A.dtype == np.uint8 else 1.0
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) if A.dtype == np.uint8 else 1.0
509+
A = np.dstack([A, np.full(A.shape[:2], alpha)])
513510
elif np.ndim(alpha) > 0: # Array alpha
514511
# 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])
518513
else: # Scalar alpha
519514
if A.shape[2] == 3: # broadcast scalar alpha
520-
output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
515+
if A.dtype == np.uint8:
516+
alpha = np.uint8(0xff * alpha)
517+
A = np.dstack([A, np.full(A.shape[:2], alpha)])
521518
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+
if A.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+
if post_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+
if post_apply_alpha:
542+
res[..., 3] *= alpha
543+
544+
# res is now either a 2D array of normed (int or float) data
528545
# 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)
530547
# output is now a correctly sized RGBA array of uint8
531548

532549
# Apply alpha *after* if the input was greyscale without a mask

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

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

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‎lib/matplotlib/tests/test_image.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -514,7 +514,7 @@ def test_image_composite_background():
514514
ax.set_xlim([0, 12])
515515

516516

517-
@image_comparison(['image_composite_alpha'], remove_text=True)
517+
@image_comparison(['image_composite_alpha'], remove_text=True, tol=0.07)
518518
def test_image_composite_alpha():
519519
"""
520520
Tests that the alpha value is recognized and correctly applied in the

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