Merge prep_cmr with image_transform plus tests #123
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## main #123 +/- ##
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+ Coverage 22.60% 24.50% +1.89%
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Files 38 37 -1
Lines 3012 2975 -37
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+ Hits 681 729 +48
+ Misses 2331 2246 -85
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| with pytest.raises(Exception): | ||
| reg_img_stack(images, coords[1:, :]) | ||
| images_reg, max_dist = reg_img_stack(images, coords) | ||
| testing.assert_allclose(images_reg, images) |
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I did not get fully your logic here and can only guess. How did you make the registered images to be close to the original? I though you randomly perturbed the coordinates of the n-1 (9) images. After registration, they are close. Is it because the random noise is of small value compared to the coords? You can explain to me with a voice message in WeChat or Skype. Thanks.
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Sorry for the poor readability. Yes, the random noise to source coordinates are in (0, 1), which are small values compared to the image size. So the images after registration should be close to the original ones. The values in list max_dist are very small. Maybe we can test whether max_dist + 1 is close to a ones vector (+1 for avoiding inf relative difference). Because we do not have real images and landmark coordinates for testing here, this is probably the easiest way for me to test the function. I will add more comments to improve the readability.
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Thanks for the clarification. That's helpful! Ready to merge.
kale/prepdata/image_transform.py
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| if i == dst_id: | ||
| continue | ||
| else: | ||
| # epts = landmarks.iloc[i, 1:].values |
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Still useful? Otherwise, remove.
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| from kale.prepdata.image_transform import mask_img_stack, reg_img_stack, rescale_img_stack | ||
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| gait = loadmat("tests/test_data/gait_gallery_data.mat") |
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Smart to use gait to test image stacks. Good reuse.
haipinglu
changed the title
Fixes card.
Description
kale.prepdata.prep_cmrwithkale.prepdata.image_transform.Status
Ready
Types of changes
docsmanually updated for new API.