WIP: add a new segmentation module #1
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…emented. demo code for warping error added.
| diff = np.ma.array(np.abs(pred - out), mask=-simple) | ||
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| # -- finding the largest deviation from the difference map | ||
| max_idx = np.unravel_index(diff.argmax(), diff.shape) |
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Nice! I learned something here, I missed unravel_index from numpy >= 1.6.0 !
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Looks awesome! I think we are missing the following
Anything else? |
| image_size = np.float(image_true.size) | ||
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| # -- we transform the annotation image into an integer image | ||
| image_true = np.where(image_true > 0, 1, 0).astype(DTYPE_I) |
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image_true = (image_true > 0).astype(...)
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see if that fits you. I have some examples also in |
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Up ? |
| # -- parameters | ||
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| DIRNAME = './verenaImages' | ||
| CONNECTOMICS_DIRNAME = '/home/poilvert/Dropbox/0__Shared_with_Poilvert/' + \ |
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demo for ha (please discard)
python code
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@npinto,
First draft for a new segmentation module. So far I needed the
pad2dfunction fromsthor so I added it to the module, even though in the future we will remove that extra
code and depend on sthor.
I also made a proxy to the Adjusted Rand Index metric from sklearn that seems fast
enough. That metric though, is only usable on segmented images (on needs multiple
labels for each connected component).
I added a quick build script to build a small Cython module that I use in the warping error
code.
Finally here are the steps to make it (hopefully) work :
build_unique.shwarping_error_demo.pyCheese,
N.