def test_recobundles_flow(): with TemporaryDirectory() as out_dir: data_path = get_fnames('fornix') fornix = load_tractogram(data_path, 'same', bbox_valid_check=False).streamlines f = Streamlines(fornix) f1 = f.copy() f2 = f1[:15].copy() f2._data += np.array([40, 0, 0]) f.extend(f2) f2_path = pjoin(out_dir, "f2.trk") sft = StatefulTractogram(f2, data_path, Space.RASMM) save_tractogram(sft, f2_path, bbox_valid_check=False) f1_path = pjoin(out_dir, "f1.trk") sft = StatefulTractogram(f, data_path, Space.RASMM) save_tractogram(sft, f1_path, bbox_valid_check=False) rb_flow = RecoBundlesFlow(force=True) rb_flow.run(f1_path, f2_path, greater_than=0, clust_thr=10, model_clust_thr=5., reduction_thr=10, out_dir=out_dir) labels = rb_flow.last_generated_outputs['out_recognized_labels'] recog_trk = rb_flow.last_generated_outputs['out_recognized_transf'] rec_bundle = load_tractogram(recog_trk, 'same', bbox_valid_check=False).streamlines npt.assert_equal(len(rec_bundle) == len(f2), True) label_flow = LabelsBundlesFlow(force=True) label_flow.run(f1_path, labels) recog_bundle = label_flow.last_generated_outputs['out_bundle'] rec_bundle_org = load_tractogram(recog_bundle, 'same', bbox_valid_check=False).streamlines BMD = BundleMinDistanceMetric() nb_pts = 20 static = set_number_of_points(f2, nb_pts) moving = set_number_of_points(rec_bundle_org, nb_pts) BMD.setup(static, moving) x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine bmd_value = BMD.distance(x0.tolist()) npt.assert_equal(bmd_value < 1, True)
def test_min_vs_min_fast_precision(): static = fornix_streamlines()[:20] moving = fornix_streamlines()[:20] static = [s.astype('f8') for s in static] moving = [m.astype('f8') for m in moving] bmd = BundleMinDistanceMatrixMetric() bmd.setup(static, moving) bmdf = BundleMinDistanceMetric() bmdf.setup(static, moving) x_test = [0.01, 0, 0, 0, 0, 0] print(bmd.distance(x_test)) print(bmdf.distance(x_test)) assert_equal(bmd.distance(x_test), bmdf.distance(x_test))
def test_min_vs_min_fast_precision(): static = fornix_streamlines()[:20] moving = fornix_streamlines()[:20] static = [s.astype('f8') for s in static] moving = [m.astype('f8') for m in moving] bmd = BundleMinDistanceMatrixMetric() bmd.setup(static, moving) bmdf = BundleMinDistanceMetric() bmdf.setup(static, moving) x_test = [0.01, 0, 0, 0, 0, 0] print(bmd.distance(x_test)) print(bmdf.distance(x_test)) assert_equal(bmd.distance(x_test), bmdf.distance(x_test))
def test_recobundles_flow(): with TemporaryDirectory() as out_dir: data_path = get_fnames('fornix') streams, hdr = nib.trackvis.read(data_path) fornix = [s[0] for s in streams] f = Streamlines(fornix) f1 = f.copy() f2 = f1[:15].copy() f2._data += np.array([40, 0, 0]) f.extend(f2) f2_path = pjoin(out_dir, "f2.trk") save_trk(f2_path, f2, affine=np.eye(4)) f1_path = pjoin(out_dir, "f1.trk") save_trk(f1_path, f, affine=np.eye(4)) rb_flow = RecoBundlesFlow(force=True) rb_flow.run(f1_path, f2_path, greater_than=0, clust_thr=10, model_clust_thr=5., reduction_thr=10, out_dir=out_dir) labels = rb_flow.last_generated_outputs['out_recognized_labels'] recog_trk = rb_flow.last_generated_outputs['out_recognized_transf'] rec_bundle, _ = load_trk(recog_trk) npt.assert_equal(len(rec_bundle) == len(f2), True) label_flow = LabelsBundlesFlow(force=True) label_flow.run(f1_path, labels) recog_bundle = label_flow.last_generated_outputs['out_bundle'] rec_bundle_org, _ = load_trk(recog_bundle) BMD = BundleMinDistanceMetric() nb_pts = 20 static = set_number_of_points(f2, nb_pts) moving = set_number_of_points(rec_bundle_org, nb_pts) BMD.setup(static, moving) x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine bmd_value = BMD.distance(x0.tolist()) npt.assert_equal(bmd_value < 1, True)
def evaluate_results(self, model_bundle, pruned_streamlines, slr_select): """ Compare the similiarity between two given bundles, model bundle, and extracted bundle. Parameters ---------- model_bundle : Streamlines pruned_streamlines : Streamlines slr_select : tuple Select the number of streamlines from model to neirborhood of model to perform the local SLR. Returns ------- ba_value : float bundle adjacency value between model bundle and pruned bundle bmd_value : float bundle minimum distance value between model bundle and pruned bundle """ spruned_streamlines = Streamlines(pruned_streamlines) recog_centroids = self._cluster_model_bundle( spruned_streamlines, model_clust_thr=1.25) mod_centroids = self._cluster_model_bundle( model_bundle, model_clust_thr=1.25) recog_centroids = Streamlines(recog_centroids) model_centroids = Streamlines(mod_centroids) ba_value = bundle_adjacency(set_number_of_points(recog_centroids, 20), set_number_of_points(model_centroids, 20), threshold=10) BMD = BundleMinDistanceMetric() static = select_random_set_of_streamlines(model_bundle, slr_select[0]) moving = select_random_set_of_streamlines(pruned_streamlines, slr_select[1]) nb_pts = 20 static = set_number_of_points(static, nb_pts) moving = set_number_of_points(moving, nb_pts) BMD.setup(static, moving) x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine bmd_value = BMD.distance(x0.tolist()) return ba_value, bmd_value
def evaluate_results(self, model_bundle, pruned_streamlines, slr_select): """ Comapare the similiarity between two given bundles, model bundle, and extracted bundle. Parameters ---------- model_bundle : Streamlines pruned_streamlines : Streamlines slr_select : tuple Select the number of streamlines from model to neirborhood of model to perform the local SLR. Returns ------- ba_value : float bundle analytics value between model bundle and pruned bundle bmd_value : float bundle minimum distance value between model bundle and pruned bundle """ spruned_streamlines = Streamlines(pruned_streamlines) recog_centroids = self._cluster_model_bundle( spruned_streamlines, model_clust_thr=1.25) mod_centroids = self._cluster_model_bundle( model_bundle, model_clust_thr=1.25) recog_centroids = Streamlines(recog_centroids) model_centroids = Streamlines(mod_centroids) ba_value = ba_analysis(recog_centroids, model_centroids, threshold=10) BMD = BundleMinDistanceMetric() static = select_random_set_of_streamlines(model_bundle, slr_select[0]) moving = select_random_set_of_streamlines(pruned_streamlines, slr_select[1]) nb_pts = 20 static = set_number_of_points(static, nb_pts) moving = set_number_of_points(moving, nb_pts) BMD.setup(static, moving) x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine bmd_value = BMD.distance(x0.tolist()) return ba_value, bmd_value
def test_recobundles_flow(): with TemporaryDirectory() as out_dir: data_path = get_fnames('fornix') streams, hdr = nib.trackvis.read(data_path) fornix = [s[0] for s in streams] f = Streamlines(fornix) f1 = f.copy() f2 = f1[:15].copy() f2._data += np.array([40, 0, 0]) f.extend(f2) f2_path = pjoin(out_dir, "f2.trk") save_trk(f2_path, f2, affine=np.eye(4)) f1_path = pjoin(out_dir, "f1.trk") save_trk(f1_path, f, affine=np.eye(4)) rb_flow = RecoBundlesFlow(force=True) rb_flow.run(f1_path, f2_path, greater_than=0, clust_thr=10, model_clust_thr=5., reduction_thr=10, out_dir=out_dir) labels = rb_flow.last_generated_outputs['out_recognized_labels'] recog_trk = rb_flow.last_generated_outputs['out_recognized_transf'] rec_bundle, _ = load_trk(recog_trk) npt.assert_equal(len(rec_bundle) == len(f2), True) label_flow = LabelsBundlesFlow(force=True) label_flow.run(f1_path, labels) recog_bundle = label_flow.last_generated_outputs['out_bundle'] rec_bundle_org, _ = load_trk(recog_bundle) BMD = BundleMinDistanceMetric() nb_pts = 20 static = set_number_of_points(f2, nb_pts) moving = set_number_of_points(rec_bundle_org, nb_pts) BMD.setup(static, moving) x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine bmd_value = BMD.distance(x0.tolist()) npt.assert_equal(bmd_value < 1, True)