I have a test script for each method and some example images to torture them. smile.png

I will post each set of test images for cv2 matchTemplate() and scikit blob_dog().  There is quite a bit of tuning that can be done to each method that can affect the results.  You can increase sensitivity in one case but increase the false positive rate in another.  The settings I have found seem to work well across each type of image I have.

cv2 settings:

stars = cv2.matchTemplate(image, star_template, cv2.TM_CCOEFF_NORMED)
stars_filtered = numpy.where(result >= 0.55)

stars = blob_dog(image, max_sigma=5, min_sigma=1, threshold=.1, overlap=0.1)