def run(self, ips, snap, img, para = None): img[:] = snap>0 dist = distance_transform_edt(snap, output=np.uint16) pts = find_maximum(dist, para['tor'], True) buf = np.zeros(ips.shape, dtype=np.uint32) buf[pts[:,0], pts[:,1]] = img[pts[:,0], pts[:,1]] = 2 markers, n = ndimg.label(buf, np.ones((3,3))) line = watershed(dist, markers, line=True, conn=para['con']+1, up=False) msk = apply_hysteresis_threshold(img, 0, 1) img[:] = snap * ~((line==0) & msk)
def run(self, ips, snap, img, para=None):
dist = distance_transform_edt(snap, output=np.uint16)
markers, n = ndimg.label(snap == 0, np.ones((3, 3)))
line = watershed(dist, markers, line=True)
if para['type'] == 'segment with ori':
img[:] = np.where(line == 0, 0, snap)
if para['type'] == 'segment only':
img[:] = (line > 0) * 255
if para['type'] == 'white line':
img[:] = (line == 0) * 255
if para['type'] == 'gray line':
img[:] = np.where(line == 0, dist, 0)
def run(self, ips, snap, img, para=None):
return distance_transform_edt(snap)
def build(msk):
dis = distance_transform_edt(msk)
ske = skeletonize(msk > 128)
graph = sknw.build_sknw(ske, True)
rst = build_graph(graph, dis, True)
return rst
def run(self, ips, imgs, para=None):
imgs[:] = imgs > 0
dtype = imgs.dtype if imgs.dtype in (np.float32,
np.float64) else np.uint16
dismap = distance_transform_edt(imgs, output=dtype)
imgs[:] = np.clip(dismap, ips.range[0], ips.range[1])