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Backport PR #28437 on branch v3.10.x (Respect array alpha with interpolation_stage='rgba' in _Imagebase::_make_image) #29656

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Merged
merged 1 commit into from
Feb 21, 2025
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Backport PR #28437 on branch v3.10.x (Respect array alpha with interpolation_stage='rgba' in _Imagebase::_make_image) #29656

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8 changes: 8 additions & 0 deletions doc/api/next_api_changes/behavior/28437-CH.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,8 @@
*alpha* parameter handling on images
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When passing and array to ``imshow(..., alpha=...)``, the parameter was silently ignored
if the image data was a RGB or RBGA image or if :rc:`interpolation_state`
resolved to "rbga".

This is now fixed, and the alpha array overwrites any previous transparency information.
32 changes: 21 additions & 11 deletions lib/matplotlib/image.py
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Expand Up @@ -501,17 +501,27 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
if A.ndim == 2: # interpolation_stage = 'rgba'
self.norm.autoscale_None(A)
A = self.to_rgba(A)
alpha = self._get_scalar_alpha()
if A.shape[2] == 3:
# No need to resample alpha or make a full array; NumPy will expand
# this out and cast to uint8 if necessary when it's assigned to the
# alpha channel below.
output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
else:
output_alpha = _resample( # resample alpha channel
self, A[..., 3], out_shape, t, alpha=alpha)
output = _resample( # resample rgb channels
self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)
alpha = self.get_alpha()
if alpha is None: # alpha parameter not specified
if A.shape[2] == 3: # image has no alpha channel
output_alpha = 255 if A.dtype == np.uint8 else 1.0
else:
output_alpha = _resample( # resample alpha channel
self, A[..., 3], out_shape, t)
output = _resample( # resample rgb channels
self, _rgb_to_rgba(A[..., :3]), out_shape, t)
elif np.ndim(alpha) > 0: # Array alpha
# user-specified array alpha overrides the existing alpha channel
output_alpha = _resample(self, alpha, out_shape, t)
output = _resample(
self, _rgb_to_rgba(A[..., :3]), out_shape, t)
else: # Scalar alpha
if A.shape[2] == 3: # broadcast scalar alpha
output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
else: # or apply scalar alpha to existing alpha channel
output_alpha = _resample(self, A[..., 3], out_shape, t) * alpha
output = _resample(
self, _rgb_to_rgba(A[..., :3]), out_shape, t)
output[..., 3] = output_alpha # recombine rgb and alpha

# output is now either a 2D array of normed (int or float) data
Expand Down
46 changes: 46 additions & 0 deletions lib/matplotlib/tests/test_image.py
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Expand Up @@ -1711,3 +1711,49 @@ def test_resample_dtypes(dtype, ndim):
axes_image = ax.imshow(data)
# Before fix the following raises ValueError for some dtypes.
axes_image.make_image(None)[0]


@pytest.mark.parametrize('intp_stage', ('data', 'rgba'))
@check_figures_equal()
def test_interpolation_stage_rgba_respects_alpha_param(fig_test, fig_ref, intp_stage):
axs_tst = fig_test.subplots(2, 3)
axs_ref = fig_ref.subplots(2, 3)
ny, nx = 3, 3
scalar_alpha = 0.5
array_alpha = np.random.rand(ny, nx)

# When the image does not have an alpha channel, alpha should be specified
# by the user or default to 1.0
im_rgb = np.random.rand(ny, nx, 3)
im_concat_default_a = np.ones((ny, nx, 1)) # alpha defaults to 1.0
im_rgba = np.concatenate( # combine rgb channels with array alpha
(im_rgb, array_alpha.reshape((ny, nx, 1))), axis=-1
)
axs_tst[0][0].imshow(im_rgb)
axs_ref[0][0].imshow(np.concatenate((im_rgb, im_concat_default_a), axis=-1))
axs_tst[0][1].imshow(im_rgb, interpolation_stage=intp_stage, alpha=scalar_alpha)
axs_ref[0][1].imshow(
np.concatenate( # combine rgb channels with broadcasted scalar alpha
(im_rgb, scalar_alpha * im_concat_default_a), axis=-1
), interpolation_stage=intp_stage
)
axs_tst[0][2].imshow(im_rgb, interpolation_stage=intp_stage, alpha=array_alpha)
axs_ref[0][2].imshow(im_rgba, interpolation_stage=intp_stage)

# When the image already has an alpha channel, multiply it by the
# scalar alpha param, or replace it by the array alpha param
axs_tst[1][0].imshow(im_rgba)
axs_ref[1][0].imshow(im_rgb, alpha=array_alpha)
axs_tst[1][1].imshow(im_rgba, interpolation_stage=intp_stage, alpha=scalar_alpha)
axs_ref[1][1].imshow(
np.concatenate( # combine rgb channels with scaled array alpha
(im_rgb, scalar_alpha * array_alpha.reshape((ny, nx, 1))), axis=-1
), interpolation_stage=intp_stage
)
new_array_alpha = np.random.rand(ny, nx)
axs_tst[1][2].imshow(im_rgba, interpolation_stage=intp_stage, alpha=new_array_alpha)
axs_ref[1][2].imshow(
np.concatenate( # combine rgb channels with new array alpha
(im_rgb, new_array_alpha.reshape((ny, nx, 1))), axis=-1
), interpolation_stage=intp_stage
)
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