[WIP] Image analysis workflow #801
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,30 @@ | ||
| import dask | ||
| import dask.array as da | ||
| import numpy as np | ||
| from dask_image import ndfilters, ndmeasure, ndmorph | ||
|
|
||
|
|
||
| def test_BBBC039(small_client): | ||
| images = da.from_zarr( | ||
| "s3://coiled-datasets/BBBC039", storage_options={"anon": True} | ||
| ) | ||
|
Comment on lines
+9
to
+11
There was a problem hiding this comment. I converted the original |
||
| smoothed = ndfilters.gaussian_filter(images, sigma=[0, 1, 1]) | ||
| thresh = ndfilters.threshold_local(smoothed, block_size=images.chunksize) | ||
| threshold_images = smoothed > thresh | ||
| structuring_element = np.array( | ||
|
There was a problem hiding this comment. Suggest adding a short comment: to this line about why we're not using the default structuring element. Eg: # Since this image stack appears to be 3-dimensional,
# we sandwich a 2d structuring element in between zeros
# so that each 2d image slice has the binary closing applied independentlyApparently I only ever said that verbally during the talk, may as well write it down. |
||
| [ | ||
| [[0, 0, 0], [0, 0, 0], [0, 0, 0]], | ||
| [[0, 1, 0], [1, 1, 1], [0, 1, 0]], | ||
| [[0, 0, 0], [0, 0, 0], [0, 0, 0]], | ||
| ] | ||
| ) | ||
| binary_images = ndmorph.binary_closing( | ||
| threshold_images, structure=structuring_element | ||
| ) | ||
| label_images, num_features = ndmeasure.label(binary_images) | ||
| index = np.arange(num_features) | ||
| # FIXME: Only selecting the first few images due to cluster idle timeout. | ||
| # Maybe sending large graph? Need to investigate a bit. | ||
|
There was a problem hiding this comment. I have no useful suggestions, but it's great* that this has possibly already identified some kind of problem your users might occasionally run into. *You know, great for your users, but admittedly not so great for the person who now needs to investigate it 😆 Have fun with that, James! |
||
| area = ndmeasure.area(images[:3], label_images[:3], index) | ||
| mean_intensity = ndmeasure.mean(images[:3], label_images[:3], index) | ||
| dask.compute(mean_intensity, area) | ||
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Suggest adding a link to those talk slides and/or the repo. That way if anyone needs to work on this benchmark later, they can look there for context/answers there before quizzing James about it.