Contour-finding also needs to be ported from opencv-python to scikit-image:

ModuleNotFoundError: `opencv-python` required for this functionality

def find_circles(dqplane, bitmask, min_area):
        # Using an input DQ plane this routine will find the groups of pixels with at least the minimum
        # area and return a list of the minimum enclosing circle parameters.
        pixels = np.bitwise_and(dqplane, bitmask)
        if ELLIPSE_PACKAGE == 'opencv-python':
            contours, hierarchy = cv.findContours(pixels, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
            bigcontours = [con for con in contours if cv.contourArea(con) >= min_area]
            circles = [cv.minEnclosingCircle(con) for con in bigcontours]
        else:
            # raise ModuleNotFoundError(ELLIPSE_PACKAGE_WARNING)
           raise ModuleNotFoundError('`opencv-python` required for this functionality')

Codecov Report

Attention: Patch coverage is 94.16667% with 14 lines in your changes missing coverage. Please review.

Project coverage is 85.35%. Comparing base (3b924fb) to head (cf527d6).
Report is 2 commits behind head on main.

@@            Coverage Diff             @@
##             main     #138      +/-   ##
==========================================
+ Coverage   84.74%   85.35%   +0.61%     
==========================================
  Files          44       46       +2     
  Lines        8527     8693     +166     
==========================================
+ Hits         7226     7420     +194     
+ Misses       1301     1273      -28     

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As described in #126 (comment), there are some differences in the methods used to construct ellipses between the two packages which result in different image masks. Perhaps a warning should make this more clear.

looking into replacing shape construction with scikit-image, it appears there is a crucial difference in the drawing method:

import cv2
import numpy
from matplotlib import pyplot
from skimage import draw

image_0 = numpy.full((10, 10), fill_value=0, dtype=numpy.uint8)

center = (4, 4)
radius = 4

disk = draw.disk(center, radius)
image_1 = image_0.copy()
image_1[disk] = 1

image_2 = cv2.circle(image_0.copy(), center, radius, 1, thickness=-1)

figure, axes = pyplot.subplots(nrows=1, ncols=2)

axes[0].imshow(image_1)
axes[0].set_title('scikit-image')

axes[1].imshow(image_2)
axes[1].set_title('opencv')

pyplot.show()

It looks like the contours found by scikit-image are consistently larger, by about half a unit, than those found by opencv:

>       assert circle[0][1] == pytest.approx(1.0, 1e-3)
E       assert 1.5 == 1.0 ± 1.0e-03

I assume this is a difference in methodology in thinking about pixels as blocks vs as points.

expanding the ellipse test case to a more oblong shape reveals a few edge cases in the scikit-image method; I'll convert this to draft for now and work on that

added some more complexity to the test shape and fixing issues in the ellipse fitting:

Currently, the .minAreaRect() is replaced by .minimum_rotated_rectangle from Shapely; this algorithm could be written without Shapely, like in circle.py, to remove the need for the additional dependency

we should also get input from INS on whether this alternative is acceptable

started regression test run against this branch at https://plwishmaster.stsci.edu:8081/blue/organizations/jenkins/RT%2FJWST/detail/JWST/2925/pipeline

Hm, I'm testing this on jw02123001001_09101_00001_nrs1 and something doesn't seem to be working right. I started out by merging the latest main branch in to make sure everything was synced, and then tried processing jw02123001001_09101_00001_nrs1

Runtime was significantly longer for the Scikit version; 743 seconds vs 314 seconds for the current opencv

More problematically though, the Scikit version doesn't seem to be flagging snowballs properly. It's flagging lots of little things that don't need attention, but missing the big snowballs. Perhaps I'm running the code the wrong way? In both cases I'm just using strun to execute detector1 pipeline, with the default expand_large_events=True

Thanks for testing! Where is jw02123001001_09101_00001_nrs1? I can run detector1 and debug that; it should have similar results to opencv, so if it doesn't then something is wrong

oh I can search that on MAST! Nevermind I'll use that, thanks anyway! 😅