Region Properties Performance Overhaul - Part 6: Public API (regionprops_table) #848
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The functions introduced here are not being added to the public API. They will be used behind the scenes from `regionprops_table` to enable orders of magnitude faster computation of region properties for all labels in an image. The basic approach here is to compute a property for all labels in an image from a single CUDA kernel call. This is in contrast to the approach from the `RegionProperties` class which first splits the full image into small sub-images corresponding to each region and then loops over these small sub-images, computing the requested property for each small region in turn. That approach is not amenable to good acceleration on the GPU as individual regions are typically small. Provides batch implementation that computes the following properties for all properties in a single kernel call: - bbox - label_filled (creates version of label_image with all holes filled) - num_pixels - num_pixels_filled - num_perimeter_pixels (number of pixels at perimeter of each labeled region) - num_boundary_pixels (number of pixels touching the image boundary for each region) The following properties are simple transformations of the properties above and have negligable additional cost to compute: - area - area_bbox - area_filled - equivalent_diameter_area - equivalent_spherical_perimeter (as in ITK) - extent - perimeter_on_border_ratio (as in ITK) - slice The following split the label image into a list of sub-images or subsets of coordinates where each element in the list corresponds to a label. The background of the label image has value 0 and is not represented in the sequences. Sequence entry `i` corresponds to label `i + 1`. In most cases, these will not be needed as properties are now computed for all regions at once from the labels image, but they are provided for completeness and to match the scikit-image API. - coords - coords_scaled - image (label mask subimages) - image_convex (convex label mask subimages) - image_intensity (intensity_image subimages) - image_filled (subimages of label mask but with holes filled) - label (sequence of integer label ids) Test cases are added that compare the results of these batch computations to results from scikit-image `regionprops_table`.
This function operates similarly to `regionprops_table`. In a future commit, once all properties have been supported, it will be used within the existing regionprops_table function so that it will provide much higher performance.
- intensity_mean - intensity_std - intensity_min - intensity_max Both single and multi-channel intensity images are supported
These properties are computed based on the image_convex subimages: - area_convex - feret_diameter_max - solidity
…moments_analytical.py
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Please review #843 first as that explains the general approach in more detail.
Overview
All of the available region properties were implemented for
regionprops_dictin parts 1-5.This MR's purpose is just to add ability for the existing
cucim.skimage.measure.regionprops_tablefunction to callregionprops_dictby default under the hood. This will allow existing code based onregionprops_tableto see an immediate performance benefit.The user can set kwarg
batch_processing=Falseto keep using the old implementation based on theRegionPropertiesclass. I am not sure if we should provide that option or just remove it. I think the only case it may be useful is that memory requirements could be lower for the non-batch approach as properties are computed on individual regions in isolation. Due to caching behavior ofRegionProperties, though, the memory usage might not actually be all that much lower.For compatibility with
regionprops_tableAPI, theregionprops_dictimplementation is updated to support user-provided callables forextra_properties. For such "extra" properties there will not be a performance benefit toregionprops_dictvs. legacyregionprops_tableas it will also loop over individual regions and apply this function on each.