def segment_morphsnakes(img,
centers,
init_center=True,
smoothing=5,
lambdas=(3, 3),
bb_dist=INIT_MASK_BORDER):
""" segmentation using morphological snakes with some parameters
:param ndarray img: input image / segmentation
:param [[int, int]] centers: position of centres / seeds
:param bool init_center:
:param int smoothing:
:param [int, int] lambdas:
:param float bb_dist:
:return (ndarray, [[int, int]]): resulting segmentation, updated centres
"""
logging.debug('segment: morph-snakes...')
if img.ndim == 3:
img = img[:, :, 0]
if init_center:
_, mask, _ = create_circle_center(img.shape[:2], centers, radius=15)
else:
mask = np.zeros_like(img, dtype=int)
mask[bb_dist:-bb_dist, bb_dist:-bb_dist] = 1
# Morphological ACWE. Initialization of the level-set.
params = dict(smoothing=smoothing, lambda1=lambdas[0], lambda2=lambdas[1])
ms = multi_snakes.MultiMorphSnakes(img, mask, morphsnakes.MorphACWE,
params)
diag = np.sqrt(img.shape[0]**2 + img.shape[1]**2)
ms.run(int(diag / 2.))
segm = ms.levelset
return segm, centers, None
def test_multi_lakes():
logging.info('running: test_multi_lakes...')
# Load the image.
imgcolor = plt.imread(PATH_IMG_LAKES) / 255.
img = rgb2gray(imgcolor)
# MorphACWE does not need g(I)
# Morphological ACWE. Initialization of the level-set.
mask = np.zeros(img.shape, dtype=int)
mask[circle_levelset(img.shape, (50, 100), 25) == 1] = 1
mask[circle_levelset(img.shape, (150, 150), 25) == 1] = 2
mask[circle_levelset(img.shape, (120, 220), 15) == 1] = 3
mask[circle_levelset(img.shape, (80, 250), 15) == 1] = 4
ms = multi_ms.MultiMorphSnakes(img, mask, MorphACWE,
dict(smoothing=3, lambda1=1, lambda2=1))
# Visual evolution.
fig = plt.figure()
evolve_visual(ms, fig, num_iters=200, background=imgcolor)
assert os.path.exists(PATH_OUTPUT)
fig.savefig(os.path.join(PATH_OUTPUT, 'cmorphsnakes_multi_lakes.png'))
plt.close(fig)
