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()