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Commit 44770ae

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Merge pull request #28437 from chaoyihu/imshow-alpha-not-respected
Respect array alpha with interpolation_stage='rgba' in _Imagebase::_make_image
2 parents 177f28c + 8a032c1 commit 44770ae
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‎doc/api/next_api_changes/behavior/28437-CH.rst

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+8Lines changed: 8 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,8 @@
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*alpha* parameter handling on images
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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When passing and array to ``imshow(..., alpha=...)``, the parameter was silently ignored
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if the image data was a RGB or RBGA image or if :rc:`interpolation_state`
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resolved to "rbga".
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This is now fixed, and the alpha array overwrites any previous transparency information.

‎lib/matplotlib/image.py

Copy file name to clipboardExpand all lines: lib/matplotlib/image.py
+21-11Lines changed: 21 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -501,17 +501,27 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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if A.ndim == 2: # interpolation_stage = 'rgba'
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self.norm.autoscale_None(A)
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A = self.to_rgba(A)
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alpha = self._get_scalar_alpha()
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if A.shape[2] == 3:
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# No need to resample alpha or make a full array; NumPy will expand
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# this out and cast to uint8 if necessary when it's assigned to the
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# alpha channel below.
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output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
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else:
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output_alpha = _resample( # resample alpha channel
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self, A[..., 3], out_shape, t, alpha=alpha)
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output = _resample( # resample rgb channels
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self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)
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alpha = self.get_alpha()
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if alpha is None: # alpha parameter not specified
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if A.shape[2] == 3: # image has no alpha channel
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output_alpha = 255 if A.dtype == np.uint8 else 1.0
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else:
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output_alpha = _resample( # resample alpha channel
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self, A[..., 3], out_shape, t)
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output = _resample( # resample rgb channels
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self, _rgb_to_rgba(A[..., :3]), out_shape, t)
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elif np.ndim(alpha) > 0: # Array alpha
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# user-specified array alpha overrides the existing alpha channel
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output_alpha = _resample(self, alpha, out_shape, t)
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output = _resample(
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self, _rgb_to_rgba(A[..., :3]), out_shape, t)
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else: # Scalar alpha
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if A.shape[2] == 3: # broadcast scalar alpha
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output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
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else: # or apply scalar alpha to existing alpha channel
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output_alpha = _resample(self, A[..., 3], out_shape, t) * alpha
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output = _resample(
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self, _rgb_to_rgba(A[..., :3]), out_shape, t)
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output[..., 3] = output_alpha # recombine rgb and alpha
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# output is now either a 2D array of normed (int or float) data

‎lib/matplotlib/tests/test_image.py

Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_image.py
+46Lines changed: 46 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1711,3 +1711,49 @@ def test_resample_dtypes(dtype, ndim):
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axes_image = ax.imshow(data)
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# Before fix the following raises ValueError for some dtypes.
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axes_image.make_image(None)[0]
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@pytest.mark.parametrize('intp_stage', ('data', 'rgba'))
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@check_figures_equal()
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def test_interpolation_stage_rgba_respects_alpha_param(fig_test, fig_ref, intp_stage):
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axs_tst = fig_test.subplots(2, 3)
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axs_ref = fig_ref.subplots(2, 3)
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ny, nx = 3, 3
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scalar_alpha = 0.5
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array_alpha = np.random.rand(ny, nx)
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# When the image does not have an alpha channel, alpha should be specified
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# by the user or default to 1.0
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im_rgb = np.random.rand(ny, nx, 3)
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im_concat_default_a = np.ones((ny, nx, 1)) # alpha defaults to 1.0
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im_rgba = np.concatenate( # combine rgb channels with array alpha
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(im_rgb, array_alpha.reshape((ny, nx, 1))), axis=-1
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)
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axs_tst[0][0].imshow(im_rgb)
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axs_ref[0][0].imshow(np.concatenate((im_rgb, im_concat_default_a), axis=-1))
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axs_tst[0][1].imshow(im_rgb, interpolation_stage=intp_stage, alpha=scalar_alpha)
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axs_ref[0][1].imshow(
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np.concatenate( # combine rgb channels with broadcasted scalar alpha
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(im_rgb, scalar_alpha * im_concat_default_a), axis=-1
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), interpolation_stage=intp_stage
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)
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axs_tst[0][2].imshow(im_rgb, interpolation_stage=intp_stage, alpha=array_alpha)
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axs_ref[0][2].imshow(im_rgba, interpolation_stage=intp_stage)
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# When the image already has an alpha channel, multiply it by the
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# scalar alpha param, or replace it by the array alpha param
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axs_tst[1][0].imshow(im_rgba)
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axs_ref[1][0].imshow(im_rgb, alpha=array_alpha)
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axs_tst[1][1].imshow(im_rgba, interpolation_stage=intp_stage, alpha=scalar_alpha)
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axs_ref[1][1].imshow(
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np.concatenate( # combine rgb channels with scaled array alpha
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(im_rgb, scalar_alpha * array_alpha.reshape((ny, nx, 1))), axis=-1
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), interpolation_stage=intp_stage
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)
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new_array_alpha = np.random.rand(ny, nx)
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axs_tst[1][2].imshow(im_rgba, interpolation_stage=intp_stage, alpha=new_array_alpha)
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axs_ref[1][2].imshow(
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np.concatenate( # combine rgb channels with new array alpha
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(im_rgb, new_array_alpha.reshape((ny, nx, 1))), axis=-1
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), interpolation_stage=intp_stage
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)

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