def test_eagle():
""" Test that "eagle" image can be loaded. """
# Fetching the data through the testing module will
# cause the test to skip if pooch isn't installed.
fetch('data/eagle.png')
eagle = data.eagle()
assert_equal(eagle.ndim, 2)
assert_equal(eagle.dtype, np.dtype('uint8'))
See Wikipedia_ for more details on the algorithm. .. _Wikipedia: https://en.wikipedia.org/wiki/Watershed_(image_processing) """ from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage.morphology import disk from skimage.segmentation import watershed from skimage import data from skimage.filters import rank from skimage.util import img_as_ubyte image = img_as_ubyte(data.eagle()) # denoise image denoised = rank.median(image, disk(2)) # find continuous region (low gradient - # where less than 10 for this image) --> markers # disk(5) is used here to get a more smooth image markers = rank.gradient(denoised, disk(5)) < 10 markers = ndi.label(markers)[0] # local gradient (disk(2) is used to keep edges thin) gradient = rank.gradient(denoised, disk(2)) # process the watershed labels = watershed(gradient, markers)
def test_eagle():
""" Test that "eagle" image can be loaded. """
eagle = data.eagle()
assert_equal(eagle.ndim, 2)
assert_equal(eagle.dtype, np.dtype('uint8'))
def setup(self):
try:
self.image = data.eagle()
except ValueError:
raise NotImplementedError("eagle data unavailable")
self.image_float32 = self.image.astype(np.float32)
