def test_renderer(): ren = window.Renderer() # background color for renderer (1, 0.5, 0) # 0.001 added here to remove numerical errors when moving from float # to int values bg_float = (1, 0.501, 0) # that will come in the image in the 0-255 uint scale bg_color = tuple((np.round(255 * np.array(bg_float))).astype('uint8')) ren.background(bg_float) # window.show(ren) arr = window.snapshot(ren) report = window.analyze_snapshot(arr, bg_color=bg_color, colors=[bg_color, (0, 127, 0)]) npt.assert_equal(report.objects, 0) npt.assert_equal(report.colors_found, [True, False]) axes = actor.axes() ren.add(axes) # window.show(ren) arr = window.snapshot(ren) report = window.analyze_snapshot(arr, bg_color) npt.assert_equal(report.objects, 1) ren.rm(axes) arr = window.snapshot(ren) report = window.analyze_snapshot(arr, bg_color) npt.assert_equal(report.objects, 0) window.add(ren, axes) arr = window.snapshot(ren) report = window.analyze_snapshot(arr, bg_color) npt.assert_equal(report.objects, 1) ren.rm_all() arr = window.snapshot(ren) report = window.analyze_snapshot(arr, bg_color) npt.assert_equal(report.objects, 0) ren2 = window.renderer(bg_float) ren2.background((0, 0, 0.)) report = window.analyze_renderer(ren2) npt.assert_equal(report.bg_color, (0, 0, 0)) ren2.add(axes) report = window.analyze_renderer(ren2) npt.assert_equal(report.actors, 3) window.rm(ren2, axes) report = window.analyze_renderer(ren2) npt.assert_equal(report.actors, 0)
def test_streamtube_and_line_actors(): renderer = window.renderer() line1 = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2.]]) line2 = line1 + np.array([0.5, 0., 0.]) lines = [line1, line2] colors = np.array([[1, 0, 0], [0, 0, 1.]]) c = actor.line(lines, colors, linewidth=3) window.add(renderer, c) c = actor.line(lines, colors, spline_subdiv=5, linewidth=3) window.add(renderer, c) # create streamtubes of the same lines and shift them a bit c2 = actor.streamtube(lines, colors, linewidth=.1) c2.SetPosition(2, 0, 0) window.add(renderer, c2) arr = window.snapshot(renderer) report = window.analyze_snapshot(arr, colors=[(255, 0, 0), (0, 0, 255)], find_objects=True) npt.assert_equal(report.objects, 4) npt.assert_equal(report.colors_found, [True, True]) # as before with splines c2 = actor.streamtube(lines, colors, spline_subdiv=5, linewidth=.1) c2.SetPosition(2, 0, 0) window.add(renderer, c2) arr = window.snapshot(renderer) report = window.analyze_snapshot(arr, colors=[(255, 0, 0), (0, 0, 255)], find_objects=True) npt.assert_equal(report.objects, 4) npt.assert_equal(report.colors_found, [True, True])
def test_slicer(): renderer = window.renderer() data = (255 * np.random.rand(50, 50, 50)) affine = np.eye(4) slicer = actor.slicer(data, affine) slicer.display(None, None, 25) window.add(renderer, slicer) renderer.reset_camera() renderer.reset_clipping_range() # window.show(renderer) # copy pixels in numpy array directly arr = window.snapshot(renderer, 'test_slicer.png') import scipy print(scipy.__version__) print(scipy.__file__) print(arr.sum()) print(np.sum(arr == 0)) print(np.sum(arr > 0)) print(arr.shape) print(arr.dtype) report = window.analyze_snapshot(arr, find_objects=True) print(report) npt.assert_equal(report.objects, 1) # print(arr[..., 0]) # The slicer can cut directly a smaller part of the image slicer.display_extent(10, 30, 10, 30, 35, 35) renderer.ResetCamera() window.add(renderer, slicer) # save pixels in png file not a numpy array with TemporaryDirectory() as tmpdir: fname = os.path.join(tmpdir, 'slice.png') # window.show(renderer) arr = window.snapshot(renderer, fname) report = window.analyze_snapshot(fname, find_objects=True) npt.assert_equal(report.objects, 1) npt.assert_raises(ValueError, actor.slicer, np.ones(10)) renderer.clear() rgb = np.zeros((30, 30, 30, 3)) rgb[..., 0] = 1. rgb_actor = actor.slicer(rgb) renderer.add(rgb_actor) renderer.reset_camera() renderer.reset_clipping_range() arr = window.snapshot(renderer) report = window.analyze_snapshot(arr, colors=[(255, 0, 0)]) npt.assert_equal(report.objects, 1) npt.assert_equal(report.colors_found, [True]) lut = actor.colormap_lookup_table(scale_range=(0, 255), hue_range=(0.4, 1.), saturation_range=(1, 1.), value_range=(0., 1.)) renderer.clear() slicer_lut = actor.slicer(data, lookup_colormap=lut) slicer_lut.display(10, None, None) slicer_lut.display(None, 10, None) slicer_lut.display(None, None, 10) slicer_lut2 = slicer_lut.copy() slicer_lut2.display(None, None, 10) renderer.add(slicer_lut2) renderer.reset_clipping_range() arr = window.snapshot(renderer) report = window.analyze_snapshot(arr, find_objects=True) npt.assert_equal(report.objects, 1)
def test_bundle_maps(): renderer = window.renderer() bundle = fornix_streamlines() bundle, shift = center_streamlines(bundle) mat = np.array([[1, 0, 0, 100], [0, 1, 0, 100], [0, 0, 1, 100], [0, 0, 0, 1.]]) bundle = transform_streamlines(bundle, mat) # metric = np.random.rand(*(200, 200, 200)) metric = 100 * np.ones((200, 200, 200)) # add lower values metric[100, :, :] = 100 * 0.5 # create a nice orange-red colormap lut = actor.colormap_lookup_table(scale_range=(0., 100.), hue_range=(0., 0.1), saturation_range=(1, 1), value_range=(1., 1)) line = actor.line(bundle, metric, linewidth=0.1, lookup_colormap=lut) window.add(renderer, line) window.add(renderer, actor.scalar_bar(lut, ' ')) report = window.analyze_renderer(renderer) npt.assert_almost_equal(report.actors, 1) # window.show(renderer) renderer.clear() nb_points = np.sum([len(b) for b in bundle]) values = 100 * np.random.rand(nb_points) # values[:nb_points/2] = 0 line = actor.streamtube(bundle, values, linewidth=0.1, lookup_colormap=lut) renderer.add(line) # window.show(renderer) report = window.analyze_renderer(renderer) npt.assert_equal(report.actors_classnames[0], 'vtkLODActor') renderer.clear() colors = np.random.rand(nb_points, 3) # values[:nb_points/2] = 0 line = actor.line(bundle, colors, linewidth=2) renderer.add(line) # window.show(renderer) report = window.analyze_renderer(renderer) npt.assert_equal(report.actors_classnames[0], 'vtkLODActor') # window.show(renderer) arr = window.snapshot(renderer) report2 = window.analyze_snapshot(arr) npt.assert_equal(report2.objects, 1) # try other input options for colors renderer.clear() actor.line(bundle, (1., 0.5, 0)) actor.line(bundle, np.arange(len(bundle))) actor.line(bundle) colors = [np.random.rand(*b.shape) for b in bundle] actor.line(bundle, colors=colors)
def test_contour_from_roi(): # Render volume renderer = window.renderer() data = np.zeros((50, 50, 50)) data[20:30, 25, 25] = 1. data[25, 20:30, 25] = 1. affine = np.eye(4) surface = actor.contour_from_roi(data, affine, color=np.array([1, 0, 1]), opacity=.5) renderer.add(surface) renderer.reset_camera() renderer.reset_clipping_range() # window.show(renderer) # Test binarization renderer2 = window.renderer() data2 = np.zeros((50, 50, 50)) data2[20:30, 25, 25] = 1. data2[35:40, 25, 25] = 1. affine = np.eye(4) surface2 = actor.contour_from_roi(data2, affine, color=np.array([0, 1, 1]), opacity=.5) renderer2.add(surface2) renderer2.reset_camera() renderer2.reset_clipping_range() # window.show(renderer2) arr = window.snapshot(renderer, 'test_surface.png', offscreen=True) arr2 = window.snapshot(renderer2, 'test_surface2.png', offscreen=True) report = window.analyze_snapshot(arr, find_objects=True) report2 = window.analyze_snapshot(arr2, find_objects=True) npt.assert_equal(report.objects, 1) npt.assert_equal(report2.objects, 2) # test on real streamlines using tracking example from dipy.data import read_stanford_labels from dipy.reconst.shm import CsaOdfModel from dipy.data import default_sphere from dipy.direction import peaks_from_model from dipy.tracking.local import ThresholdTissueClassifier from dipy.tracking import utils from dipy.tracking.local import LocalTracking from dipy.viz.colormap import line_colors hardi_img, gtab, labels_img = read_stanford_labels() data = hardi_img.get_data() labels = labels_img.get_data() affine = hardi_img.get_affine() white_matter = (labels == 1) | (labels == 2) csa_model = CsaOdfModel(gtab, sh_order=6) csa_peaks = peaks_from_model(csa_model, data, default_sphere, relative_peak_threshold=.8, min_separation_angle=45, mask=white_matter) classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25) seed_mask = labels == 2 seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine) # Initialization of LocalTracking. # The computation happens in the next step. streamlines = LocalTracking(csa_peaks, classifier, seeds, affine, step_size=2) # Compute streamlines and store as a list. streamlines = list(streamlines) # Prepare the display objects. streamlines_actor = actor.line(streamlines, line_colors(streamlines)) seedroi_actor = actor.contour_from_roi(seed_mask, affine, [0, 1, 1], 0.5) # Create the 3d display. r = window.ren() r2 = window.ren() r.add(streamlines_actor) arr3 = window.snapshot(r, 'test_surface3.png', offscreen=True) report3 = window.analyze_snapshot(arr3, find_objects=True) r2.add(streamlines_actor) r2.add(seedroi_actor) arr4 = window.snapshot(r2, 'test_surface4.png', offscreen=True) report4 = window.analyze_snapshot(arr4, find_objects=True) # assert that the seed ROI rendering is not far # away from the streamlines (affine error) npt.assert_equal(report3.objects, report4.objects)
def test_slicer(): renderer = window.renderer() data = (255 * np.random.rand(50, 50, 50)) affine = np.eye(4) slicer = actor.slicer(data, affine) slicer.display(None, None, 25) renderer.add(slicer) renderer.reset_camera() renderer.reset_clipping_range() # window.show(renderer) # copy pixels in numpy array directly arr = window.snapshot(renderer, 'test_slicer.png', offscreen=True) import scipy print(scipy.__version__) print(scipy.__file__) print(arr.sum()) print(np.sum(arr == 0)) print(np.sum(arr > 0)) print(arr.shape) print(arr.dtype) report = window.analyze_snapshot(arr, find_objects=True) npt.assert_equal(report.objects, 1) # print(arr[..., 0]) # The slicer can cut directly a smaller part of the image slicer.display_extent(10, 30, 10, 30, 35, 35) renderer.ResetCamera() renderer.add(slicer) # save pixels in png file not a numpy array with TemporaryDirectory() as tmpdir: fname = os.path.join(tmpdir, 'slice.png') # window.show(renderer) window.snapshot(renderer, fname, offscreen=True) report = window.analyze_snapshot(fname, find_objects=True) npt.assert_equal(report.objects, 1) npt.assert_raises(ValueError, actor.slicer, np.ones(10)) renderer.clear() rgb = np.zeros((30, 30, 30, 3)) rgb[..., 0] = 1. rgb_actor = actor.slicer(rgb) renderer.add(rgb_actor) renderer.reset_camera() renderer.reset_clipping_range() arr = window.snapshot(renderer, offscreen=True) report = window.analyze_snapshot(arr, colors=[(255, 0, 0)]) npt.assert_equal(report.objects, 1) npt.assert_equal(report.colors_found, [True]) lut = actor.colormap_lookup_table(scale_range=(0, 255), hue_range=(0.4, 1.), saturation_range=(1, 1.), value_range=(0., 1.)) renderer.clear() slicer_lut = actor.slicer(data, lookup_colormap=lut) slicer_lut.display(10, None, None) slicer_lut.display(None, 10, None) slicer_lut.display(None, None, 10) slicer_lut.opacity(0.5) slicer_lut.tolerance(0.03) slicer_lut2 = slicer_lut.copy() npt.assert_equal(slicer_lut2.GetOpacity(), 0.5) npt.assert_equal(slicer_lut2.picker.GetTolerance(), 0.03) slicer_lut2.opacity(1) slicer_lut2.tolerance(0.025) slicer_lut2.display(None, None, 10) renderer.add(slicer_lut2) renderer.reset_clipping_range() arr = window.snapshot(renderer, offscreen=True) report = window.analyze_snapshot(arr, find_objects=True) npt.assert_equal(report.objects, 1) renderer.clear() data = (255 * np.random.rand(50, 50, 50)) affine = np.diag([1, 3, 2, 1]) slicer = actor.slicer(data, affine, interpolation='nearest') slicer.display(None, None, 25) renderer.add(slicer) renderer.reset_camera() renderer.reset_clipping_range() arr = window.snapshot(renderer, offscreen=True) report = window.analyze_snapshot(arr, find_objects=True) npt.assert_equal(report.objects, 1) npt.assert_equal(data.shape, slicer.shape) renderer.clear() data = (255 * np.random.rand(50, 50, 50)) affine = np.diag([1, 3, 2, 1]) from dipy.align.reslice import reslice data2, affine2 = reslice(data, affine, zooms=(1, 3, 2), new_zooms=(1, 1, 1)) slicer = actor.slicer(data2, affine2, interpolation='linear') slicer.display(None, None, 25) renderer.add(slicer) renderer.reset_camera() renderer.reset_clipping_range() # window.show(renderer, reset_camera=False) arr = window.snapshot(renderer, offscreen=True) report = window.analyze_snapshot(arr, find_objects=True) npt.assert_equal(report.objects, 1) npt.assert_array_equal([1, 3, 2] * np.array(data.shape), np.array(slicer.shape))
clean_poly_data.SetInputData(triangle_poly_data) mapper = vtk.vtkPolyDataMapper() surface_actor = vtk.vtkActor() if smooth is None: mapper.SetInputData(triangle_poly_data) surface_actor.SetMapper(mapper) elif smooth == "loop": smooth_loop = vtk.vtkLoopSubdivisionFilter() smooth_loop.SetNumberOfSubdivisions(subdivision) smooth_loop.SetInputConnection(clean_poly_data.GetOutputPort()) mapper.SetInputConnection(smooth_loop.GetOutputPort()) surface_actor.SetMapper(mapper) elif smooth == "butterfly": smooth_butterfly = vtk.vtkButterflySubdivisionFilter() smooth_butterfly.SetNumberOfSubdivisions(subdivision) smooth_butterfly.SetInputConnection(clean_poly_data.GetOutputPort()) mapper.SetInputConnection(smooth_butterfly.GetOutputPort()) surface_actor.SetMapper(mapper) return surface_actor surface_actor = surface(vertices, colors=c_arr, smooth="loop") renderer = window.renderer(background=(1, 1, 1)) renderer.add(surface_actor) window.show(renderer, size=(600, 600), reset_camera=False)