def test_conversions():
vpl.quick_test_plot()
arr = vpl.screenshot_fig()
vpl.close()
image_data = vpl.image_io.vtkimagedata_from_array(arr)
arr2 = vpl.image_io.vtkimagedata_to_array(image_data)
assert np.array_equal(arr, arr2)
def test_figure_io():
vpl.close()
assert vpl.gcf(False) is None
vpl.auto_figure(False)
assert vpl.gcf() is None
with pytest.raises(vpl.figures.figure_manager.NoFigureError):
vpl.screenshot_fig()
fig = vpl.figure()
assert vpl.gcf() is None
del fig
vpl.auto_figure(True)
fig = vpl.gcf()
assert fig is not None
assert fig is vpl.gcf()
vpl.close()
assert vpl.gcf(False) is None
vpl.scf(fig)
assert fig is vpl.gcf()
vpl.close()
fig = vpl.figure()
assert fig is vpl.gcf()
vpl.close()
def test_figure_io(self):
vpl.close()
self.assertIs(vpl.gcf(False), None)
vpl.auto_figure(False)
self.assertIs(vpl.gcf(), None)
with self.assertRaises(vpl.figures.figure_manager.NoFigureError):
vpl.screenshot_fig()
fig = vpl.figure()
self.assertIs(vpl.gcf(), None)
del fig
vpl.auto_figure(True)
fig = vpl.gcf()
self.assertTrue(fig is not None)
self.assertIs(fig, vpl.gcf())
vpl.close()
self.assertIs(vpl.gcf(False), None)
vpl.scf(fig)
self.assertIs(fig, vpl.gcf())
vpl.close()
fig = vpl.figure()
self.assertIs(fig, vpl.gcf())
vpl.close()
def test_add_remove(self):
fig = vpl.figure()
plots = vpl.quick_test_plot(None)
fig += plots
fig.show(False)
fig -= plots
for i in plots:
fig += i
fig.update()
time.sleep(.05)
for i in plots:
fig -= i
fig.update()
time.sleep(.05)
vpl.close(fig)
def test_view():
vpl.auto_figure(True)
vpl.close()
grads = np.array(vpl.geometry.orthogonal_bases(np.random.rand(3)))
point = np.random.uniform(-10, 10, 3)
vpl.quiver(np.broadcast_to(point, (3, 3)), grads, color=np.eye(3))
vpl.view(focal_point=point, camera_position=point - grads[0],
up_view=grads[1])
vpl.reset_camera()
vpl.text("Should be looking in the direction of the red arrow, "
"with the green arrow pointing up")
# Linux seems to need an extra prod to render this for some reason.
vpl.show(block=False)
def test_multi_figures(self):
vpl.close()
vpl.auto_figure(False)
plot = vpl.plot(np.random.uniform(-10, 10, (10, 3)), join_ends=True)
figs = []
for i in range(1, 4):
fig = vpl.figure("figure {}".format(i))
fig += plot
vpl.view(camera_direction=np.random.uniform(-1, 1, 3), fig=fig)
vpl.reset_camera(fig)
fig.show(False)
figs.append(fig)
fig.show()
vpl.auto_figure(True)
def test_trim_image():
fig = vpl.figure()
# Shouldn't do anything if the figure is empty.
assert vpl.screenshot_fig(trim_pad_width=.05).shape[:2] == fig.render_size
vpl.quick_test_plot()
arr = vpl.screenshot_fig()
vpl.close()
trimmed = vpl.image_io.trim_image(arr, fig.background_color, 10)
background_color = np.asarray(fig.background_color) * 255
# Check that no non-background coloured pixels have been lost.
assert (arr != background_color).any(-1).sum() \
== (trimmed != background_color).any(-1).sum()
return trimmed
def test_type_normalise():
mesh = numpy_stl().Mesh.from_file(path)
vectors = mesh.vectors
unique_points = set(tuple(i) for i in vectors.reshape(len(vectors) * 3, 3))
points_enum = {point: i for (i, point) in enumerate(unique_points)}
points = np.array(sorted(unique_points, key=points_enum.get))
point_args = np.apply_along_axis(lambda x: points_enum[tuple(x)], -1,
vectors)
vpl.plots.MeshPlot.NUMPY_STL_AVAILABLE = False
for fmt in (path, mesh, vectors, (points, point_args)):
normalised = vpl.mesh_plot(fmt).vectors
assert np.array_equal(normalised, vectors)
vpl.plots.MeshPlot.NUMPY_STL_AVAILABLE = True
vpl.close()
def test_qfigure():
vpl.QtFigure._abc_assert_no_abstract_methods()
self = vpl.QtFigure("a Qt widget figure")
assert self is vpl.gcf()
direction = np.array([1, 0, 0])
vpl.quiver(np.array([0, 0, 0]), direction)
vpl.view(camera_direction=direction)
vpl.reset_camera()
self.show(block=False)
self.close()
self.showMaximized(block=not VTKPLOTLIB_WINDOWLESS_TEST)
out = vpl.screenshot_fig(fig=self)
vpl.close(fig=self)
globals().update(locals())
return out
def test_view(self):
vpl.auto_figure(True)
vpl.close()
grads = np.array(vpl.geometry.orthogonal_bases(np.random.rand(3)))
point = np.random.uniform(-10, 10, 3)
vpl.quiver(np.broadcast_to(point, (3, 3)), grads, color=np.eye(3))
vpl.view(focal_point=point,
camera_position=point - grads[0],
up_view=grads[1])
# vpl.view(camera_direction=grads[0],
# up_view=grads[1],
# )
#
vpl.reset_camera()
# vpl.view(point)
vpl.text(
"Should be looking in the direction of the red arrow, with the green arrow pointing up"
)
vpl.show()
def test_save(self):
plots = vpl.scatter(np.random.uniform(-10, 10, (30, 3)))
# I can't get python2 to cooperate with unicode here.
# The os functions just don't like them.
if sys.version[0] == "3":
path = Path.cwd() / u"ҢघԝઌƔࢳܢˀા\\Հએࡓ\u061cཪЈतயଯ\u0886.png"
try:
os.mkdir(str(path.parent))
vpl.save_fig(path)
self.assertTrue(path.exists())
os.remove(str(path))
finally:
if path.parent.exists():
os.rmdir(str(path.parent))
else:
path = Path.cwd() / "image.png"
vpl.save_fig(path)
os.remove(str(path))
array = vpl.screenshot_fig(2)
self.assertEqual(array.shape,
tuple(i * 2 for i in vpl.gcf().render_size) + (3, ))
plt.imshow(array)
plt.show()
shape = tuple(i * j for (i, j) in zip(vpl.gcf().render_size, (2, 3)))
vpl.screenshot_fig(pixels=shape).shape
# The following will fail depending on VTK version
# self.assertEqual(vpl.screenshot_fig(pixels=shape).shape,
# shape[::-1] + (3,))
vpl.close()
fig = vpl.figure()
for plot in plots:
fig += plot
vpl.show()
def test_save():
plots = vpl.scatter(np.random.uniform(-10, 10, (30, 3)))
path = TEST_DIR / "name.png"
if path.exists():
os.remove(str(path))
vpl.save_fig(path)
assert path.exists()
array = vpl.screenshot_fig(magnification=2)
assert array.shape == tuple(i * 2 for i in vpl.gcf().render_size) + (3, )
shape = tuple(i * j for (i, j) in zip(vpl.gcf().render_size, (2, 3)))
vpl.screenshot_fig(pixels=shape).shape
# The following will fail depending on VTK version
# .assertEqual(vpl.screenshot_fig(pixels=shape).shape,
# shape[::-1] + (3,))
vpl.close()
return array
def renderimage(tread,options):
reifenflanke = 'rimAndShoulder/reifenflanke.stl'
modulfelge = 'rimAndShoulder/modulfelge.stl'
# Read the STL using numpy-stl
geom = mesh.Mesh.from_file(tread)
shoulder = mesh.Mesh.from_file(reifenflanke)
rim = mesh.Mesh.from_file(modulfelge)
# Plot the mesh
vpl.mesh_plot(geom,color=[76,76,76])
if options == 'full':
vpl.mesh_plot(shoulder,color=[76,76,76])
vpl.mesh_plot(rim,color=[1,1,1])
# Show the figure
vpl.view(camera_direction = [0.8,0.5,0],up_view=[0,0,1])
#fig = vpl.gcf()
#fig.background_color = "transparent"
#vpl.show()
#name = 'render.png'
vpl.save_fig('render/render.png',off_screen=True,magnification=2)
vpl.close()
def get_projections(file, camera_directions=CAMERA_DIRS):
""" Get projection views in given camera directions from an STL file.
Returns: Numpy array of shape (PROJ_SHAPE, len(camera_directions)).
"""
views = []
for dir in camera_directions[::-1]:
mesh = Mesh.from_file(file)
vpl.mesh_plot(mesh, color="black")
r = vpl.view(camera_position=(0, 0, 0), camera_direction=dir)
vpl.reset_camera()
vpl.zoom_to_contents(padding=1)
# Upscale first so downscale is more accurate
arr = vpl.screenshot_fig(magnification=10, off_screen=True)
# Change 3-channel RGB image to single channel binary matrix
arr = cv2.cvtColor(arr, cv2.COLOR_BGR2GRAY)
arr[arr == 0] = 1
arr[arr == 218] = 0
arr = cv2.resize(arr, dsize=PROJ_SHAPE, interpolation=cv2.INTER_LINEAR)
vpl.close()
views.append(arr)
views = np.array(views).reshape(
(PROJ_SHAPE[0], PROJ_SHAPE[1], len(camera_directions)))
return views
