Fixed an off-by-half-pixel bug in image resampling when using a nonaffine transform #30054
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Indeed, hunting down this bug was an adventure! For SunPy, we do some fun stuff with remapping data to a different view, e.g.: |
Heh, I was hesitant to access There are a bunch of layers of private API between (Edit: As realized down below, I'll point out that this PR implicitly already has a test using public API, given that baseline images had to be updated. The plotting of an image using a log scale uses a nonaffine transform, and hence was affected by the bug. |
lib/matplotlib/tests/test_image.py
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| [(np.array([[0.1, 0.3, 0.2]]), mpl._image.NEAREST, | ||
| np.array([[0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2]])), | ||
| (np.array([[0.1, 0.3, 0.2]]), mpl._image.BILINEAR, |
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| [(np.array([[0.1, 0.3, 0.2]]), mpl._image.NEAREST, | |
| np.array([[0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2]])), | |
| (np.array([[0.1, 0.3, 0.2]]), mpl._image.BILINEAR, | |
| [(np.array([[0.1, 0.3, 0.2]]), mimage.NEAREST, | |
| np.array([[0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2]])), | |
| (np.array([[0.1, 0.3, 0.2]]), mimage.BILINEAR, |
These values are implemented in a private namespace, but are available in a public namespace (which is already imported directly)
This should also resolve the type hint problems
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Fair enough. As I said, it is preferred but not strictly required. More that is was worth asking, especially with the mypy errors (for which my solution did not work btw, but the one provided above should). |
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@ksunden Thanks for pointing out that |
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I can mostly understand the line plots, but do you have an example of how this changes in an image itself? Possibly one that is rather low resolution so it is clear how the changes affect it. The test image that did change is a bit too high resolution to see clearly, I think. |
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As I mentioned above, it's a little challenging to show the behavior in an obvious manner because it is typically only at the display-pixel level. That means you can't see the discrepancies better through zooming in an interactive plot or plotting at a larger DPI. Perhaps there is some way I am not aware of, but I take a different approach below. Here's a comparison script to generate images for comparison (without the fix in this PR): import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import Transform
data = np.arange(12321).reshape((111, 111)) % 4
class NonAffineIdentityTransform(Transform):
input_dims = 2
output_dims = 2
def inverted(self):
return self
fig = plt.figure()
ax = fig.add_subplot()
ax.imshow(data, extent=[0, 111, 0, 111], transform=ax.transData)
ax.set_title('affine')
fig.savefig('affine.png')
fig = plt.figure()
ax = fig.add_subplot()
ax.imshow(data, extent=[0, 111, 0, 111], transform=NonAffineIdentityTransform() + ax.transData)
ax.set_title('nonaffine')
fig.savefig('nonaffine.png')At normal display, it's difficult to discern any difference between the two images: If I then use an image editor to zoom in to the origin: affine: The data array was specifically chosen so that 3 data pixels map to 10 display pixels, just as with the line plots above. You can see that between the affine image and the nonaffine image, the 3-4-3 pattern of display pixels has shifted. (That is, a data pixel that used to span 3 display pixels now spans 4 display pixels, and the data pixel right after it has changed from 4 display pixels to 3 display pixels.) It is difficult to know which one is "right" from these images, given all of the additional stuff that is done to render the image, but that's why the test in this PR checks the output of the low-level |
PR summary
This PR fixes a off-by-half-pixel bug in
matplotlib._image.resample()when using a nonaffine transform. (This function is used internally when drawing an image artist.) The mesh for nonaffine transforms is mistakenly computed at the lower corners of pixels instead of the centers of pixels, which results in a half-pixel shift in the output. Here are illustrative plots and the generating script.Before this PR:
After this PR:
Script:
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