def run(self, ips, imgs, para=None):
dist = -ndimg.distance_transform_edt(imgs)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(imgs.shape, dtype=np.uint16)
buf[pts[:, 0], pts[:, 1], pts[:, 2]] = 1
markers, n = ndimg.label(buf, np.ones((3, 3, 3)))
line = watershed(dist, markers, line=True, conn=para['con'] + 1)
imgs[line == 0] = 0
def run(self, ips, snap, img, para=None):
img[:] = snap
dist = -ndimg.distance_transform_edt(snap)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(ips.size, dtype=np.uint16)
buf[pts[:, 0], pts[:, 1]] = 1
markers, n = ndimg.label(buf, np.ones((3, 3)))
line = watershed(dist, markers)
img[line == 0] = 0
def run(self, ips, snap, img, para=None):
img[:] = snap > 0
dist = -ndimg.distance_transform_edt(snap)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(ips.size, 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)
msk = apply_hysteresis_threshold(img, 0, 1)
img[:] = snap * ~((line == 0) & msk)
def run(self, ips, imgs, para=None):
imgs[:] = imgs > 0
dist = -ndimg.distance_transform_edt(imgs)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(imgs.shape, dtype=np.uint32)
buf[pts[:, 0], pts[:, 1], pts[:, 2]] = 2
imgs[pts[:, 0], pts[:, 1], pts[:, 2]] = 2
markers, n = ndimg.label(buf, np.ones((3, 3, 3)))
line = watershed(dist, markers, line=True, conn=para['con'] + 1)
msk = apply_hysteresis_threshold(imgs, 0, 1)
imgs[:] = imgs > 0
imgs *= 255
imgs *= ~((line == 0) & msk)
def run(self, ips, snap, img, para=None):
pts = find_maximum(self.ips.img, para['tol'], False)
self.ips.roi = PointRoi([tuple(i) for i in pts[:, ::-1]])
self.ips.update = True
def run(self, ips, snap, img, para=None):
pts = find_maximum(self.ips.img, para['tol'], False)
ips.roi = ROI([Points(pts[:, ::-1])])
ips.update()
