def plot(self, Ps):
from mayavi import mlab
mlab.figure(mlab.figure(), bgcolor=(1, 1, 1))
for s in range(len(Ps)):
mlab.mesh(Ps[s][:, :, 0], Ps[s][:, :, 1], Ps[s][:, :, 2], color=(65 / 256, 105 / 256, 225 / 256))
mlab.show()
def test_close(self):
""" Various tests for mlab.close().
"""
f = mlab.figure()
self.assertTrue(f.running)
mlab.close(f)
self.assertFalse(f.running)
f = mlab.figure(314)
self.assertTrue(f.running)
mlab.close(314)
self.assertFalse(f.running)
f = mlab.figure('test_figure')
self.assertTrue(f.running)
mlab.close('test_figure')
self.assertFalse(f.running)
f = mlab.figure()
self.assertTrue(f.running)
mlab.close()
self.assertFalse(f.running)
figs = [mlab.figure() for i in range(5)]
for f in figs:
self.assertTrue(f.running)
mlab.close(all=True)
for f in figs:
self.assertFalse(f.running)
def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def testVisually(self):
'''blocks selected visually.'''
# if self.comm.rank == 0:
g2z,zvec = PETSc.Scatter().toZero(self.bnd.gindBlockWBand)
g2z.scatter(self.bnd.gindBlockWBand,zvec, PETSc.InsertMode.INSERT)
x = self.bnd.BlockSub2CenterCarWithoutBand(\
self.bnd.BlockInd2SubWithoutBand(zvec.getArray()) )
lx = self.bnd.BlockSub2CenterCarWithoutBand(\
self.bnd.BlockInd2SubWithoutBand(self.bnd.gindBlockWBand.getArray()))
try:
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
if self.comm.rank == 0:
mlab.figure()
mlab.points3d(x[:,0],x[:,1],x[:,2])
mlab.figure()
mlab.points3d(lx[:,0],lx[:,1],lx[:,2])
mlab.show()
#fig.add(pts1)
#fig.add(pts2)
except ImportError:
import pylab as pl
from mpl_toolkits.mplot3d import Axes3D #@UnusedImport
fig = pl.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter3D(x[:,0],x[:,1],x[:,2],c='blue',marker='o')
ax.scatter3D(lx[:,0],lx[:,1],lx[:,2],c='red',marker='D')
pl.savefig('testVis{0}.png'.format(self.comm.rank))
pl.show()
def showDsweep():
k_s_sweep = [10**(x-5) for x in range(10)]
sl_div_diam_sweep = [5.0*(x+1)/1000 for x in range(20)]
vol_ratio_sweep = [5.0*(x+1)/100 for x in range(10)]
Dmsd = numpy.load(data_dir + "/D_numpy.npy")
kDa = 1.660538921e-30;
mass = 40.0*kDa;
viscosity = 8.9e-4;
diameter = 5e-9;
T = 300.0;
Dbase = k_b*T/(3.0*numpy.pi*viscosity*diameter);
Dmsd = Dmsd/Dbase
mlab.figure(1, size=(800, 800), fgcolor=(1, 1, 1),
bgcolor=(0.5, 0.5, 0.5))
mlab.clf()
contours = numpy.arange(0.01,2,0.2).tolist()
obj = mlab.contour3d(Dmsd,contours=contours,transparent=True,vmin=contours[0],vmax=contours[-1])
outline = mlab.outline(color=(.7, .7, .7),extent=(0,10,0,20,0,10))
axes = mlab.axes(outline, color=(.7, .7, .7),
nb_labels = 5,
ranges=(k_s_sweep[0], k_s_sweep[-1], sl_div_diam_sweep[0], sl_div_diam_sweep[-1], vol_ratio_sweep[0], vol_ratio_sweep[-1]),
xlabel='spring stiffness',
ylabel='step length',
zlabel='volume ratio')
mlab.colorbar(obj,title='D',nb_labels=5)
mlab.show()
def figure(size=None, zdown=True):
"""
Create a default figure in Mayavi with white background
Parameters:
* size : tuple = (dx, dy)
The size of the figure. If ``None`` will use the default size.
* zdown : True or False
If True, will turn the figure upside-down to make the z-axis point down
Return:
* fig : Mayavi figure object
The figure
"""
_lazy_import_mlab()
if size is None:
fig = mlab.figure(bgcolor=(1, 1, 1))
else:
fig = mlab.figure(bgcolor=(1, 1, 1), size=size)
if zdown:
fig.scene.camera.view_up = numpy.array([0., 0., -1.])
fig.scene.camera.elevation(60.)
fig.scene.camera.azimuth(180.)
return fig
def show_volume(self, bbox):
"""
show the volume with the given bounding box
"""
# load the data
with h5py.File(self.h5fn, 'r') as f:
seg1 = f["segmentation"]["labels"][bbox[2]:bbox[5],bbox[1]:bbox[4],bbox[0]:bbox[3]]
raw = f["raw"]["volume"][bbox[2]:bbox[5],bbox[1]:bbox[4],bbox[0]:bbox[3]]
print "Drawing volume"
t0 = time.time()
#draw everything
fig1 = mlab.figure(1, size=(500,450))
mlab.clf(fig1)
visCell.drawImagePlane(fig1, raw, 'gist_ncar')
visCell.drawVolumeWithoutReferenceCell(fig1, seg1, np.array((-1,)), (0,0,1),0.5)
with h5py.File(self.h5fn, 'r') as f:
visCell.drawLabels(fig1, f, seg1, bbox)
fig2 = mlab.figure(2, size=(500,450))
mlab.clf(fig2)
visCell.draw2DView(fig2, raw[20:-20,:,:], seg1[20:-20,:,:], -1)
t = time.time() - t0
print "Time for drawing:",t
def plotSpherical(dataset, vertices, triangles, ptitle="", tsize=0.4, theight=0.95):
"""Plot the spherical data given a data set, triangle set and vertex set.
The vertex set defines the direction cosines of the individual samples.
The triangle set defines how the surfrace must be structured between the samples.
The data set defines, for each direction cosine, the length of the vector.
Args:
| dataset(numpy.array(double)): array of data set values
| vertices(numpy.array([])): array of direction cosine vertices as [x y z]
| triangles(numpy.array([])): array of triangles as []
| ptitle(string): title or header for this display
| tsize(double): title size (units not quite clear)
| theight(double): title height (y value) (units not quite clear)
Returns:
| provides and mlab figure.
Raises:
| No exception is raised.
"""
# calculate a (x,y,z) data set from the direction vectors
x = dataset * vertices[:, 0]
y = dataset * vertices[:, 1]
z = dataset * vertices[:, 2]
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
# Visualize the points
pts = mlab.triangular_mesh(x, y, z, triangles) # z, scale_mode='none', scale_factor=0.2)
mlab.title(ptitle, size=tsize, height=theight)
mlab.show()
def m2screenshot(mayavi_fig=None, mpl_axes=None, autocrop=True):
""" Capture a screeshot of the Mayavi figure and display it in the
matplotlib axes.
"""
import pylab as pl
# Late import to avoid triggering wx imports before needed.
try:
from mayavi import mlab
except ImportError:
# Try out old install of Mayavi, with namespace packages
from enthought.mayavi import mlab
if mayavi_fig is None:
mayavi_fig = mlab.gcf()
else:
mlab.figure(mayavi_fig)
if mpl_axes is not None:
pl.axes(mpl_axes)
filename = tempfile.mktemp('.png')
mlab.savefig(filename, figure=mayavi_fig)
image3d = pl.imread(filename)
if autocrop:
bg_color = mayavi_fig.scene.background
image3d = autocrop_img(image3d, bg_color)
pl.imshow(image3d)
pl.axis('off')
os.unlink(filename)
def draw_volume(self):
if self.vector_field_visible or self.scalar_field_visible:
coordinate_system = self.space.basis_in_global_coordinate_system()
grid = coordinate_system.to_parent(self.grid.reshape(3, -1).T).T.reshape(self.grid.shape)
if self.vector_field is None:
mlab.figure(self.fig, bgcolor=self.fig.scene.background)
self.vector_field = mlab.pipeline.vector_field(grid[0], grid[1], grid[2],
self.vector_data[0],
self.vector_data[1],
self.vector_data[2],
scalars=self.scalar_data,
name=self.space.name)
else:
origin = np.array([np.min(grid[0]), np.min(grid[1]), np.min(grid[2])])
spacing = np.array([grid[0, 1, 0, 0] - grid[0, 0, 0, 0],
grid[1, 0, 1, 0] - grid[1, 0, 0, 0],
grid[2, 0, 0, 1] - grid[2, 0, 0, 0]])
self.vector_field.spacing = spacing
self.vector_field.origin = origin
self.vector_field.vector_data = np.rollaxis(self.vector_data, 0, 4)
self.vector_field.scalar_data = self.scalar_data
if self.vector_volume is None and self.vector_field_visible:
self.vector_volume = mlab.pipeline.vectors(self.vector_field)
if self.scalar_volume is None and self.scalar_field_visible:
self.scalar_volume = mlab.pipeline.volume(self.vector_field)
if not self.vector_field_visible:
self.remove_vector_field()
if not self.scalar_field_visible:
self.remove_scalar_field()
def zoncaview(m):
"""
m is a healpix sky map, such as provided by WMAP or Planck.
"""
nside = hp.npix2nside(len(m))
vmin = -1e3; vmax = 1e3
# Set up some grids:
xsize = ysize = 1000
theta = np.linspace(np.pi, 0, ysize)
phi = np.linspace(-np.pi, np.pi, xsize)
longitude = np.radians(np.linspace(-180, 180, xsize))
latitude = np.radians(np.linspace(-90, 90, ysize))
# Project the map to a rectangular matrix xsize x ysize:
PHI, THETA = np.meshgrid(phi, theta)
grid_pix = hp.ang2pix(nside, THETA, PHI)
grid_map = m[grid_pix]
# Create a sphere:
r = 0.3
x = r*np.sin(THETA)*np.cos(PHI)
y = r*np.sin(THETA)*np.sin(PHI)
z = r*np.cos(THETA)
# The figure:
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
mlab.mesh(x, y, z, scalars=grid_map, colormap="jet", vmin=vmin, vmax=vmax)
mlab.draw()
return
def draw_coordinate_system_axes(fig, coordinate_system, offset=0.0, scale=1.0, draw_labels=True):
points, lengths = coordinate_system_arrows(coordinate_system, offset=offset, scale=scale)
mlab.figure(fig, bgcolor=fig.scene.background)
arrows = mlab.quiver3d(
points[:, 0],
points[:, 1],
points[:, 2],
lengths[0, :],
lengths[1, :],
lengths[2, :],
scalars=np.array([3, 2, 1]),
mode="arrow",
)
arrows.glyph.color_mode = "color_by_scalar"
arrows.glyph.glyph.scale_factor = scale
data = arrows.parent.parent
data.name = coordinate_system.name
glyph_scale = arrows.glyph.glyph.scale_factor * 1.1
# label_col = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
labels = []
if draw_labels:
for i in range(3):
labels.append(
mlab.text(
points[i, 0] + glyph_scale * coordinate_system.basis[i, 0],
points[i, 1] + glyph_scale * coordinate_system.basis[i, 1],
coordinate_system.labels[i],
z=points[i, 2] + glyph_scale * coordinate_system.basis[i, 2],
# color=label_col[i],
width=0.1 * scale,
)
)
return arrows, labels
def plot3D(name, X, Y, Z, zlabel):
"""
Plots a 3d surface plot of Z using the mayavi mlab.mesh function.
Parameters
----------
name: string
The name of the figure.
X: 2d ndarray
The x-axis data.
Y: 2d ndarray
The y-axis data.
Z: 2d nd array
The z-axis data.
zlabel: The title that appears on the z-axis.
"""
mlab.figure(name)
mlab.clf()
plotData = mlab.mesh(X/(np.max(X) - np.min(X)),
Y/(np.max(Y) - np.min(Y)),
Z/(np.max(Z) - np.min(Z)))
mlab.outline(plotData)
mlab.axes(plotData, ranges=[np.min(X), np.max(X),
np.min(Y), np.max(Y),
np.min(Z), np.max(Z)])
mlab.xlabel('Space ($x$)')
mlab.ylabel('Time ($t$)')
mlab.zlabel(zlabel)
def show_sensors(pth, red=None, green=None):
"""
show sensors stored in a h5.
:param red: a list of labels to be shown in red
"""
if red is None:
red = []
if green is None:
green = []
mlab.figure()
sensors = h5py.File(pth)
d = numpy.array(sensors['locations'])
labels = list(sensors['labels'])
highlight = numpy.ones(d.shape[0])
for i, l in enumerate(labels):
if l in red:
highlight[i] = 5.0
elif l in green:
highlight[i] = 7.0
else:
highlight[i] = 2.0
mlab.points3d(d[:,0], d[:,1], d[:,2], highlight, scale_mode='none')
mlab.axes()
def test_surface_normals(plot=False, skip_asserts=False,
write_reference=False):
"Test the surface normals of a horseshoe mesh"
sim = openmodes.Simulation()
mesh = sim.load_mesh(osp.join(mesh_dir, 'horseshoe_rect.msh'))
part = sim.place_part(mesh)
basis = sim.basis_container[part]
r, rho = basis.integration_points(mesh.nodes, triangle_centres)
normals = mesh.surface_normals
r = r.reshape((-1, 3))
if write_reference:
write_2d_real(osp.join(reference_dir, 'surface_r.txt'), r)
write_2d_real(osp.join(reference_dir, 'surface_normals.txt'), normals)
r_ref = read_2d_real(osp.join(reference_dir, 'surface_r.txt'))
normals_ref = read_2d_real(osp.join(reference_dir, 'surface_normals.txt'))
if not skip_asserts:
assert_allclose(r, r_ref)
assert_allclose(normals, normals_ref)
if plot:
from mayavi import mlab
mlab.figure()
mlab.quiver3d(r[:, 0], r[:, 1], r[:, 2],
normals[:, 0], normals[:, 1], normals[:, 2],
mode='cone')
mlab.view(distance='auto')
mlab.show()
def testQuaternion():
dtheta = 30.0
quat = Quaternion()
print quat.q
print quat.toRot()
print det(quat.toRot())
figm = mlab.figure(bgcolor=(1,1,1))
for i in range(100):
print quat.sampleUnif(0.5*np.pi)
k,theta = quat.toAxisAngle()
print theta*180.0/np.pi
plotCosy(figm, quat.toRot())
figm = mlab.figure(bgcolor=(1,1,1))
for i in range(100):
print quat.sample(dtheta)
k,theta = quat.toAxisAngle()
print theta*180.0/np.pi
plotCosy(figm, quat.toRot())
figm1 = mlab.figure(bgcolor=(1,1,0.0))
for i in range(100):
# sample rotation axis
k = np.random.rand(3)-0.5
# sample uiniformly from +- 5 degrees
theta = (np.asscalar(np.random.rand(1)) + dtheta - 0.5) *np.pi/180.0 # (np.a sscalar(np.random.rand(1))-0.5)*np.pi/(180.0/(2.0*dtheta))
print 'perturbation: {} theta={}'.format(k/norm(k),theta*180.0/np.pi)
dR = RodriguesRotation(k/norm(k),theta)
plotCosy(figm1, dR)
mlab.show()
def plot3dformesh(x,cv,f):
return
cv = band.toZeroStatic(cv)
if MPI.COMM_WORLD.rank == 0:
v2 = cv.getArray()
pl.figure(bgcolor=(1,1,1),fgcolor=(0.5,0.5,0.5))
pl.triangular_mesh(x[:,0],x[:,1],x[:,2],f,scalars=v2)
def test_empty(self):
data=numpy.zeros((20,20,20))
mlab.figure( bgcolor=(0,0,0), size=(1000,845) )
ATMA.GUI.DataVisualizer.segmentation( data )
mlab.close()
def make_montage(info, kind, check=False):
from . import _reorder
assert kind in ('mgh60', 'mgh70', 'uw_70', 'uw_60')
picks = mne.pick_types(info, meg=False, eeg=True, exclude=())
if kind in ('mgh60', 'mgh70'):
ch_names = mne.utils._clean_names(
[info['ch_names'][pick] for pick in picks], remove_whitespace=True)
if kind == 'mgh60':
assert len(ch_names) in (59, 60)
else:
assert len(ch_names) in (70,)
montage = mne.channels.read_montage(kind, ch_names=ch_names)
else:
ch_names = getattr(_reorder, 'ch_names_' + kind)
ch_names = ch_names
montage = mne.channels.read_montage('standard_1020', ch_names=ch_names)
assert len(montage.ch_names) == len(ch_names)
montage.ch_names = ['EEG%03d' % ii for ii in range(1, 61)]
sphere = mne.make_sphere_model('auto', 'auto', info)
montage.pos /= np.linalg.norm(montage.pos, axis=-1, keepdims=True)
montage.pos *= sphere.radius
montage.pos += sphere['r0']
info = mne.pick_info(info, picks)
eeg_pos = np.array([ch['loc'][:3] for ch in info['chs']])
assert len(eeg_pos) == len(montage.pos), (len(eeg_pos), len(montage.pos))
if check:
from mayavi import mlab
mlab.figure(size=(800, 800))
mlab.points3d(*sphere['r0'], scale_factor=2 * sphere.radius,
color=(0., 0., 1.), opacity=0.1, mode='sphere')
mlab.points3d(*montage.pos.T, scale_factor=0.01,
color=(1, 0, 0), mode='sphere', opacity=0.5)
mlab.points3d(*eeg_pos.T, scale_factor=0.005, color=(1, 1, 1),
mode='sphere', opacity=1)
return montage, sphere
def plot_sphere_func(f, grid='Clenshaw-Curtis', theta=None, phi=None, colormap='jet', fignum=0):
# Note: all grids except Clenshaw-Curtis have holes at the poles
import matplotlib
matplotlib.use('WxAgg')
matplotlib.interactive(True)
from mayavi import mlab
if grid == 'Driscoll-Healy':
b = f.shape[0] / 2
elif grid == 'Clenshaw-Curtis':
b = (f.shape[0] - 2) / 2
elif grid == 'SOFT':
b = f.shape[0] / 2
elif grid == 'Gauss-Legendre':
b = (f.shape[0] - 2) / 2
if theta is None or phi is None:
theta, phi = meshgrid(b=b, convention=grid)
phi = np.r_[phi, phi[0, :][None, :]]
theta = np.r_[theta, theta[0, :][None, :]]
f = np.r_[f, f[0, :][None, :]]
x = np.sin(theta) * np.cos(phi)
y = np.sin(theta) * np.sin(phi)
z = np.cos(theta)
mlab.figure(fignum, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(600, 400))
mlab.clf()
mlab.mesh(x, y, z, scalars=f, colormap=colormap)
# mlab.view(90, 70, 6.2, (-1.3, -2.9, 0.25))
mlab.show()
def test_rawData(self):
raw=numpy.uint32(numpy.random.rand(200,200,200)*255)
mlab.figure( bgcolor=(0,0,0), size=(1000,845) )
ATMA.GUI.DataVisualizer.rawSlider( raw )
mlab.close()
def vis_mesh(model_id, edge_length_threshold, shuffle=False, wireframe=False):
import numpy as np
from mayavi import mlab
from shapenet.core.meshes import get_mesh_dataset
from shapenet.core import cat_desc_to_id
from util3d.mayavi_vis import vis_mesh
from template_ffd.inference.meshes import get_inferred_mesh_dataset
from template_ffd.model import load_params
import random
def vis(mesh, **kwargs):
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vis_mesh(v, f, include_wireframe=wireframe, **kwargs)
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
inf_mesh_dataset = get_inferred_mesh_dataset(
model_id, edge_length_threshold)
with inf_mesh_dataset:
with get_mesh_dataset(cat_id) as gt_mesh_dataset:
example_ids = list(inf_mesh_dataset.keys())
if shuffle:
random.shuffle(example_ids)
for example_id in example_ids:
inf = inf_mesh_dataset[example_id]
gt = gt_mesh_dataset[example_id]
mlab.figure()
vis(inf, color=(0, 1, 0), opacity=0.2)
mlab.figure()
vis(gt, opacity=0.2)
mlab.show()
def Main(outputFolder, isData, normalized = True):
assert isinstance(outputFolder, str)
min, max = 0., 1.
if os.path.splitext(args.file)[1] == '.arff':
datasets, targets, targetMap = loadFromArff(args.file)
elif os.path.splitext(args.file)[1] == '.npy':
datasets = numpy.load(args.file)
min = -1.
else:
assert False
datasets = (lambda x : histogramEqualization(x, min=min, max=max) if normalized else x)(datasets)
if normalized : assert (datasets.min(), datasets.max()) == (min, max)
if not os.path.isdir("%s/Pictures" % outputFolder):
os.makedirs("%s/Pictures" % outputFolder)
global listIndex
if listIndex is None or (len(listIndex) >= len(datasets)):
listIndex = xrange(len(datasets))
for index in listIndex:
assert 0 <= index < len(datasets)
mlab.figure("Index : %d" % index, bgcolor=(1,1,1))
showArray(datasets[index], isData)
mlab.savefig("%s/Pictures/Index_%d.png" % (outputFolder, index))
if isData:
saveData('%s/Pictures/%s_Index_%d.txt' % (outputFolder, targetMap.reverse_mapping[targets[index]], index), datasets[index])
else:
saveData('%s/Pictures/Index_%d.txt' % (outputFolder, index), datasets[index])
mlab.close()
def plot_matrix(connectmat_file, centers_file, threshold_pct=5, weight_edges=False,
node_scale_factor=2, edge_radius=.5, resolution=8, name_scale_factor=1,
names_file=None, node_indiv_colors=[], highlight_nodes=[], fliplr=False):
"""
Given a connectivity matrix and a (x,y,z) centers file for each region, plot the 3D network
"""
matrix = core.file_reader(connectmat_file)
nodes = core.file_reader(centers_file)
if names_file:
names = core.file_reader(names_file,1)
num_nodes = len(nodes)
edge_thresh_pct = threshold_pct / 100.0
matrix_flat = np.array(matrix).flatten()
edge_thresh = np.sort(matrix_flat)[len(matrix_flat)-int(len(matrix_flat)*edge_thresh_pct)]
matrix = core.file_reader(connectmat_file)
ma = np.array(matrix)
thresh = scipy.stats.scoreatpercentile(ma.ravel(),100-threshold_pct)
ma_thresh = ma*(ma > thresh)
if highlight_nodes:
nr = ma.shape[0]
subset_mat = np.zeros((nr, nr))
for i in highlight_nodes:
subset_mat[i,:] = 1
subset_mat[:,i] = 1
ma_thresh = ma_thresh * subset_mat
if fliplr:
new_nodes = []
for node in nodes:
new_nodes.append([45-node[0],node[1],node[2]]) # HACK
nodes = new_nodes
mlab.figure(bgcolor=(1, 1, 1), size=(400, 400))
for count,(x,y,z) in enumerate(nodes):
if node_indiv_colors:
mlab.points3d(x,y,z, color=colors[node_indiv_colors[count]], scale_factor=node_scale_factor, resolution=resolution)
else:
mlab.points3d(x,y,z, color=(0,1,0), scale_factor=node_scale_factor, resolution=resolution)
if names_file:
width = .025*name_scale_factor*len(names[count])
print width
print names[count]
mlab.text(x, y,names[count], z=z,width=.025*len(names[count]),color=(0,0,0))
for i in range(num_nodes-1):
x0,y0,z0 = nodes[i]
for j in range(i+1, num_nodes):
#if matrix[i][j] > edge_thresh:
if ma_thresh[i][j] > edge_thresh:
x1,y1,z1 = nodes[j]
if weight_edges:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=matrix[i][j]/matrix_flat.max(),
color=(1,1,1))
else:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=edge_radius,
color=(1,1,1))
def test_normals_new5 ():
#pts1 = clouds.downsample(pts1, 0.02).astype('float64')
# pts1 = np.array([[0.,0.,0.], [0.,0.5,0.], [0.,1.,0.], [1.,0.,0.0], [1.,0.5,0.0], [1.,1.,0.25]])
# pts2 = np.array([[0.,0.,0.], [0.,0.5,0.], [0.,1.,0.], [1.,0.,1.], [1.,0.5,1.], [1.,1.,1.25]])
# e1 = np.array([[1.,0.,0.], [1.,0.,0.], [1.,0.,0.], [-1.,0.,0.], [-1.,0.,0.], [-1.,0.,0.]])
# e2 = np.array([[1.,0.,0.], [1.,0.,0.], [1.,0.,0.], [-1.,0.,0.], [-1.,0.,0.], [-1.,0.,0.]])
pts1, pts2, e1, e2 = create_flap_points_normals(3.0,1,dim=3)
f1 = fit_ThinPlateSpline(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=None, use_cvx=True)
#f2 = fit_ThinPlateSpline(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=None, use_cvx=True)
f2 = te.tps_eval(pts1, pts2, e1, e2, bend_coef=0.01, rot_coef=1e-5, wt_n=None, nwsize=0.15, delta=0.0001)
#f2 = te.tps_fit_normals_cvx(pts1, pts2, e1, e2, bend_coef=0.1, rot_coef=1e-5, normal_coef=0.1, wt_n=None, nwsize=0.15, delta=0.0001)
#f2 = te.tps_fit_normals_exact_cvx(pts1, pts2, e1, e2, bend_coef=0.1, rot_coef=1e-5, normal_coef = 0.1, wt_n=None, nwsize=0.15, delta=0.002)
# import IPython
# IPython.embed()
mlab.figure(1, bgcolor=(0,0,0))
mayavi_utils.plot_warping(f1, pts1, pts2, fine=False, draw_plinks=False)
_,f1e2 = te.transformed_normal_direction(pts1, e1, f1, delta=0.0001)#np.asarray([tu.tps_jacobian(f2, pt, 2).dot(nm) for pt,nm in zip(pts1,e1)])
test_normals_pts(f1.transform_points(pts1), f1e2, wsize=0.15,delta=0.15)
test_normals_pts(pts2, e2, wsize=0.15,delta=0.15)
#mlab.show()
mlab.figure(2,bgcolor=(0,0,0))
#mlab.clf()
mayavi_utils.plot_warping(f2, pts1, pts2, fine=False, draw_plinks=False)
_,f2e2 = te.transformed_normal_direction(pts1, e1, f2, delta=0.0001)#np.asarray([tu.tps_jacobian(f2, pt, 2).dot(nm) for pt,nm in zip(pts1,e1)])
test_normals_pts(f2.transform_points(pts1), f2e2, wsize=0.15,delta=0.15)
test_normals_pts(pts2, e2, wsize=0.15,delta=0.15)
mlab.show()
def test_normals_new3 ():
#pts1 = clouds.downsample(pts1, 0.02).astype('float64')
pts1 = gen_circle_points(0.5, 30)
pts2 = gen_circle_points_pulled_in(0.5,30,6,0.4)#gen_circle_points(0.5, 30) + np.array([0.1,0.1])
wt_n = None#np.linalg.norm(pts1-pts2,axis=1)*2+1
f1 = fit_ThinPlateSpline(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=wt_n, use_cvx=True)
#f2 = fit_ThinPlateSpline(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=None, use_cvx=True)
#f2 = te.tps_eval(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=None, nwsize=0.15, delta=0.0001)
#f2 = te.tps_fit_normals_cvx(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, normal_coef=10, wt_n=wt_n, nwsize=0.15, delta=0.0001)
#f2 = te.tps_fit_normals_cvx(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, normal_coef=0.1, wt_n=None, nwsize=0.15, delta=0.0001)
f2 = te.tps_fit_normals_exact_cvx(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, normal_coef = 1, wt_n=None, nwsize=0.15, delta=0.0001)
mlab.figure(1, bgcolor=(0,0,0))
mayavi_utils.plot_warping(f1, pts1, pts2, fine=False, draw_plinks=True)
test_normals_pts(np.c_[f1.transform_points(pts1),np.zeros((pts2.shape[0],1))], wsize=0.15,delta=0.15)
test_normals_pts(np.c_[pts2,np.zeros((pts2.shape[0],1))], wsize=0.15,delta=0.15)
#mlab.show()
mlab.figure(2,bgcolor=(0,0,0))
#mlab.clf()
mayavi_utils.plot_warping(f2, pts1, pts2, fine=False, draw_plinks=True)
test_normals_pts(np.c_[f2.transform_points(pts1),np.zeros((pts2.shape[0],1))], wsize=0.15,delta=0.15)
test_normals_pts(np.c_[pts2,np.zeros((pts2.shape[0],1))], wsize=0.15,delta=0.15)
mlab.show()
def test_normals_new4 (n=2,l=0.5,dim=2):
pts1, pts2, e1, e2 = create_flap_points_normals(n,l,dim)
delta = 1e-2
f1 = fit_ThinPlateSpline(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=None, use_cvx=True)
#f1 = te.tps_fit_normals_cvx(pts1, pts2, e1, e2, bend_coef=0.1, rot_coef=1e-5, normal_coef=0.1, wt_n=None, nwsize=0.15, delta=0.0001)
#f2 = fit_ThinPlateSpline(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, wt_n=None, use_cvx=True)
#f2 = te.tps_eval(pts1, pts2, e1, e2, bend_coef=0.0, rot_coef=1e-5, wt_n=None, nwsize=0.15, delta=1e-8)
#f2 = te.tps_fit_normals_cvx(pts1, pts2, bend_coef=0.1, rot_coef=1e-5, normal_coef=10, wt_n=None, nwsize=0.15, delta=1e-6)
f2 = te.tps_fit_normals_cvx(pts1, pts2, e1, e2, bend_coef=0.0, rot_coef=1e-5, normal_coef=1, wt_n=None, nwsize=0.15, delta=delta)
mlab.figure(1, bgcolor=(0,0,0))
mayavi_utils.plot_warping(f1, pts1, pts2, fine=False, draw_plinks=False)
_,f1e2 = te.transformed_normal_direction(pts1, e1, f1, delta=delta)#np.asarray([tu.tps_jacobian(f2, pt, 2).dot(nm) for pt,nm in zip(pts1,e1)])
test_normals_pts(np.c_[f1.transform_points(pts1),np.zeros((pts2.shape[0],1))], np.c_[f1e2,np.zeros((f1e2.shape[0],1))], wsize=0.15,delta=0.15)
test_normals_pts(np.c_[pts2,np.zeros((pts2.shape[0],1))], np.c_[e2,np.zeros((e2.shape[0],1))], wsize=0.15,delta=0.15)
#mlab.show()
mlab.figure(2,bgcolor=(0,0,0))
#mlab.clf()
mayavi_utils.plot_warping(f2, pts1, pts2, fine=False, draw_plinks=False)
_,f2e2 = te.transformed_normal_direction(pts1, e1, f2, delta=delta)
test_normals_pts(np.c_[f2.transform_points(pts1),np.zeros((pts2.shape[0],1))], np.c_[f2e2,np.zeros((f2e2.shape[0],1))], wsize=0.15,delta=0.15)
test_normals_pts(np.c_[pts2,np.zeros((pts2.shape[0],1))], np.c_[e2,np.zeros((e2.shape[0],1))], wsize=0.15,delta=0.15)
mlab.show()
def short_branches():
"""
Visualization of short branches of the skeleton.
"""
data1_sk = glob.glob('/backup/yuliya/vsi05/skeletons_largdom/*.h5')
data1_sk.sort()
for i,j, k in zip(d[1][37:47], data1_sk[46:56], ell[1][37:47]):
g = nx.read_gpickle(i)
dat = tb.openFile(j)
skel = np.copy(dat.root.skel)
bra = np.copy(dat.root.branches)
mask = np.zeros_like(skel)
dat.close()
length = nx.get_edge_attributes(g, 'length')
number = nx.get_edge_attributes(g, 'number')
num_dict = {}
for m in number:
for v in number[m]:
num_dict.setdefault(v, []).append(m)
find_br = ndimage.find_objects(bra)
for l in list(length.keys()):
if length[l]<0.5*k: #Criteria
for b in number[l]:
mask[find_br[b-1]] = bra[find_br[b-1]]==b
mlab.figure(bgcolor=(1,1,1), size=(1200,1200))
mlab.contour3d(skel, colormap='hot')
mlab.contour3d(mask)
mlab.savefig('/backup/yuliya/vsi05/skeletons/short_bran/'+ i[42:-10] + '.png')
mlab.close()
def test_base_line2 (pts1, pts2):
pts1 = clouds.downsample(pts1, 0.02)
pts2 = clouds.downsample(pts2, 0.02)
print pts1.shape
print pts2.shape
#plotter = PlotterInit()
def plot_cb(src, targ, xtarg_nd, corr, wt_n, f):
plot_requests = plot_warping(f.transform_points, src, targ, fine=False)
for req in plot_requests:
plotter.request(req)
f1,_ = tps_rpm_bij(pts1, pts2, reg_init=10, reg_final=1, rot_reg=np.r_[1e-3,1e-3,1e-1], n_iter=50, plot_cb=plot_cb, plotting=0)
#raw_input("Done with tps_rpm_bij")
#plotter.request(gen_mlab_request(mlab.clf))
f2,_ = tps_rpm_bij_normals(pts1, pts2, reg_init=10, reg_final=01, n_iter=50, rot_reg=np.r_[1e-3,1e-3,1e-1], normal_coeff = 0.01,
nwsize = 0.07, plot_cb=plot_cb, plotting =0)
#raw_input('abcd')
from tn_rapprentice import tps
#print tps.tps_cost(f1.lin_ag, f1.trans_g, f1.w_ng, pts1, pts2, 1)
#print tps.tps_cost(f2.lin_ag, f2.trans_g, f2.w_ng, pts1, pts2, 1)
#plotter.request(gen_mlab_request(mlab.clf))
mlab.figure(1)
mayavi_utils.plot_warping(f1, pts1, pts2, fine=False, draw_plinks=True)
#mlab.show()
mlab.figure(2)
#mlab.clf()
mayavi_utils.plot_warping(f2, pts1, pts2, fine=False, draw_plinks=True)
mlab.show()
def main():
fileDir='./ascfiles2'
mapFile = './maps/worldmap.png'
colorFile = './colormaps/color1.txt'
# color map used for test
color1 = [[0,0,0,0,0],
[1,255,255,0,10],
[10,255,255,0,50],
[100,255,100,0,150],
[1000,255,0,0,255]]
color2 = [[0,0,0,0,0],
[100,0,255,255,10],
[1000,0,255,255,50],
[10000,0,100,255,150],
[100000,0,0,255,255]]
s = loadData(fileDir)
mlab.figure(bgcolor=(0,0,0), size=(800,800) )
vol = drawVolume(mlab,s)
colormap = readColorFile(colorFile)
changeVolumeColormap(vol,colormap)
drawMap(mlab, s, mapFile)
changeToBestView(mlab)
#refine graph
refine = RefineGraph(gc)
refine.Update(AreaParam=area_param, PolyParam=poly_param)
gr = refine.GetOutput()
#gr=FullyConnectedGraph(gr) # uncomment to get only fully connected components of the graph
gr = fixG(
gr, copy=True
) # this is to fix nodes indixing to be starting from 0 (important for visualization)
#-------------------------------------------------------------------------#
# read/ write
#-------------------------------------------------------------------------#
# # save graph
WritePajek(path='', name='mygraph.pajek', graph=fixG(gc))
# #load graph
loaded_g = ReadPajek('mygraph.pajek').GetOutput()
#-------------------------------------------------------------------------#
# Visulaize
#-------------------------------------------------------------------------#
#from VascGraph.GraphLab import GraphPlot, StackPlot, MainDialogue
mlab.figure()
stack_plot = StackPlot()
stack_plot.Update(s)
graph_plot = GraphPlot()
graph_plot.Update(loaded_g)
#!/usr/local/bin/ipython --gui=wx
from mayavi.mlab import triangular_mesh, figure
import numpy as np
from os.path import join
fig = figure()
data_path = './data/'
smpl_v = np.load(join(data_path, 'verts.npy'))
verts = smpl_v.T
faces = np.load(join(data_path, 'faces.npy'))
tm = triangular_mesh(verts[0],
verts[1],
verts[2],
faces,
color=(.7, .7, .9),
figure=fig)
fig.scene.reset_zoom()
import ipdb
ipdb.set_trace()
def setUp(self, figure=None):
self.engine = mlab.get_engine()
fig = mlab.figure()
mlab.pipeline.surface(BuiltinImage(), figure=fig)
self.camera = self.engine.current_scene.scene.camera
def demo():
import mayavi.mlab as mlab
from viz_util import draw_lidar, draw_lidar_simple, draw_gt_boxes3d
dataset = kitti_object(os.path.join(ROOT_DIR, 'dataset/KITTI/object'))
data_idx = 0
# Load data from dataset
objects = dataset.get_label_objects(data_idx)
objects[0].print_object()
img = dataset.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_height, img_width, img_channel = img.shape
print(('Image shape: ', img.shape))
pc_velo = dataset.get_lidar(data_idx)[:, 0:3]
# COMMENT finally --> done for visualization
pc_velo[0, 2] = pc_velo[0, 2] - 5
calib = dataset.get_calibration(data_idx)
# Draw raw lidar in KITTI velo coord
print(' -------- Raw LiDAR points in KITTI velo coordination --------')
fig = draw_lidar(pc_velo)
raw_input()
# Draw lidar in rect camera coord
print(' -------- LiDAR points in rect camera coordination --------')
pc_rect = calib.project_velo_to_rect(pc_velo)
fig = draw_lidar_simple(pc_rect)
raw_input()
# Draw 2d and 3d boxes on image
print(' -------- 2D/3D bounding boxes in images --------')
show_image_with_boxes(img, objects, calib)
raw_input()
# Show all LiDAR points. Draw 3d box in LiDAR point cloud
print(
' -------- LiDAR points and 3D boxes in velodyne coordinate --------')
# show_lidar_with_boxes(pc_velo, objects, calib)
# raw_input()
# Change False to True
show_lidar_with_boxes(pc_velo, objects, calib, True, img_width, img_height)
raw_input()
# Visualize LiDAR points on images
print(' -------- LiDAR points projected to image plane --------')
show_lidar_on_image(pc_velo, img, calib, img_width, img_height)
raw_input()
# Show LiDAR points that are in the 3d box
print(' -------- LiDAR points in a 3D bounding box --------')
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(objects[0], calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
box3droi_pc_velo, _ = extract_pc_in_box3d(pc_velo, box3d_pts_3d_velo)
print(('Number of points in 3d box: ', box3droi_pc_velo.shape[0]))
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(1000, 500))
draw_lidar(box3droi_pc_velo, fig=fig)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig)
mlab.show(1)
raw_input()
# UVDepth Image and its backprojection to point clouds
print(' -------- LiDAR points in a frustum from a 2D box --------')
imgfov_pc_velo, pts_2d, fov_inds = get_lidar_in_image_fov(
pc_velo, calib, 0, 0, img_width, img_height, True)
imgfov_pts_2d = pts_2d[fov_inds, :]
imgfov_pc_rect = calib.project_velo_to_rect(imgfov_pc_velo)
cameraUVDepth = np.zeros_like(imgfov_pc_rect)
cameraUVDepth[:, 0:2] = imgfov_pts_2d
cameraUVDepth[:, 2] = imgfov_pc_rect[:, 2]
# Show that the points are exactly the same
backprojected_pc_velo = calib.project_image_to_velo(cameraUVDepth)
print(imgfov_pc_velo[0:20])
print(backprojected_pc_velo[0:20])
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(1000, 500))
draw_lidar(backprojected_pc_velo, fig=fig)
raw_input()
# Only display those points that fall into 2d box
print(' -------- LiDAR points in a frustum from a 2D box --------')
xmin,ymin,xmax,ymax = \
objects[0].xmin, objects[0].ymin, objects[0].xmax, objects[0].ymax
boxfov_pc_velo = \
get_lidar_in_image_fov(pc_velo, calib, xmin, ymin, xmax, ymax)
print(('2d box FOV point num: ', boxfov_pc_velo.shape[0]))
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(1000, 500))
draw_lidar(boxfov_pc_velo, fig=fig)
mlab.show(1)
raw_input()
def extract_frustum_data_rgb_detection(det_filename,
split,
output_filename,
viz=False,
type_whitelist=['Car'],
img_height_threshold=25,
lidar_point_threshold=5):
''' Extract point clouds in frustums extruded from 2D detection boxes.
Update: Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
det_filename: string, each line is
img_path typeid confidence xmin ymin xmax ymax
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
type_whitelist: a list of strings, object types we are interested in.
img_height_threshold: int, neglect image with height lower than that.
lidar_point_threshold: int, neglect frustum with too few points.
Output:
None (will write a .pickle file to the disk)
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'dataset/KITTI/object'),
split)
det_id_list, det_type_list, det_box2d_list, det_prob_list = \
read_det_file(det_filename)
cache_id = -1
cache = None
id_list = []
type_list = []
box2d_list = []
prob_list = []
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
frustum_angle_list = [] # angle of 2d box center from pos x-axis
for det_idx in range(len(det_id_list)):
data_idx = det_id_list[det_idx]
print('det idx: %d/%d, data idx: %d' % \
(det_idx, len(det_id_list), data_idx))
if cache_id != data_idx:
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(\
pc_velo[:,0:3], calib, 0, 0, img_width, img_height, True)
cache = [calib, pc_rect, pc_image_coord, img_fov_inds]
cache_id = data_idx
else:
calib, pc_rect, pc_image_coord, img_fov_inds = cache
if det_type_list[det_idx] not in type_whitelist: continue
# 2D BOX: Get pts rect backprojected
xmin, ymin, xmax, ymax = det_box2d_list[det_idx]
box_fov_inds = (pc_image_coord[:,0]<xmax) & \
(pc_image_coord[:,0]>=xmin) & \
(pc_image_coord[:,1]<ymax) & \
(pc_image_coord[:,1]>=ymin)
box_fov_inds = box_fov_inds & img_fov_inds
pc_in_box_fov = pc_rect[box_fov_inds, :]
# Get frustum angle (according to center pixel in 2D BOX)
box2d_center = np.array([(xmin + xmax) / 2.0, (ymin + ymax) / 2.0])
uvdepth = np.zeros((1, 3))
uvdepth[0, 0:2] = box2d_center
uvdepth[0, 2] = 20 # some random depth
box2d_center_rect = calib.project_image_to_rect(uvdepth)
frustum_angle = -1 * np.arctan2(box2d_center_rect[0, 2],
box2d_center_rect[0, 0])
# Pass objects that are too small
if ymax-ymin<img_height_threshold or \
len(pc_in_box_fov)<lidar_point_threshold:
continue
id_list.append(data_idx)
type_list.append(det_type_list[det_idx])
box2d_list.append(det_box2d_list[det_idx])
prob_list.append(det_prob_list[det_idx])
input_list.append(pc_in_box_fov)
frustum_angle_list.append(frustum_angle)
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp)
pickle.dump(box2d_list, fp)
pickle.dump(input_list, fp)
pickle.dump(type_list, fp)
pickle.dump(frustum_angle_list, fp)
pickle.dump(prob_list, fp)
if viz:
import mayavi.mlab as mlab
for i in range(10):
p1 = input_list[i]
fig = mlab.figure(figure=None,
bgcolor=(0.4, 0.4, 0.4),
fgcolor=None,
engine=None,
size=(500, 500))
mlab.points3d(p1[:, 0],
p1[:, 1],
p1[:, 2],
p1[:, 1],
mode='point',
colormap='gnuplot',
scale_factor=1,
figure=fig)
fig = mlab.figure(figure=None,
bgcolor=(0.4, 0.4, 0.4),
fgcolor=None,
engine=None,
size=(500, 500))
mlab.points3d(p1[:, 2],
-p1[:, 0],
-p1[:, 1],
seg,
mode='point',
colormap='gnuplot',
scale_factor=1,
figure=fig)
raw_input()
def extract_frustum_data(idx_filename,
split,
output_filename,
viz=False,
perturb_box2d=False,
augmentX=1,
type_whitelist=['Car']):
''' Extract point clouds and corresponding annotations in frustums
defined generated from 2D bounding boxes
Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
idx_filename: string, each line of the file is a sample ID
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
viz: bool, whether to visualize extracted data
perturb_box2d: bool, whether to perturb the box2d
(used for data augmentation in train set)
augmentX: scalar, how many augmentations to have for each 2D box.
type_whitelist: a list of strings, object types we are interested in.
Output:
None (will write a .pickle file to the disk)
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'dataset/KITTI/object'),
split)
data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
id_list = [] # int number
box2d_list = [] # [xmin,ymin,xmax,ymax]
box3d_list = [] # (8,3) array in rect camera coord
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
label_list = [] # 1 for roi object, 0 for clutter
type_list = [] # string e.g. Car
heading_list = [] # ry (along y-axis in rect camera coord) radius of
# (cont.) clockwise angle from positive x axis in velo coord.
box3d_size_list = [] # array of l,w,h
frustum_angle_list = [] # angle of 2d box center from pos x-axis
pos_cnt = 0
all_cnt = 0
for data_idx in data_idx_list:
print('------------- ', data_idx)
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
objects = dataset.get_label_objects(data_idx)
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
fig_path = str(data_idx) + ".png"
x = pc_rect[:, 0]
y = pc_rect[:, 1]
plt.scatter(x, y, marker=".", color='k')
plt.savefig(fig_path)
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(
pc_velo[:, 0:3], calib, 0, 0, img_width, img_height, True)
for obj_idx in range(len(objects)):
if objects[obj_idx].type not in type_whitelist: continue
# 2D BOX: Get pts rect backprojected
box2d = objects[obj_idx].box2d
for _ in range(augmentX):
# Augment data by box2d perturbation
if perturb_box2d:
xmin, ymin, xmax, ymax = random_shift_box2d(box2d)
print(box2d)
print(xmin, ymin, xmax, ymax)
else:
xmin, ymin, xmax, ymax = box2d
box_fov_inds = (pc_image_coord[:,0]<xmax) & \
(pc_image_coord[:,0]>=xmin) & \
(pc_image_coord[:,1]<ymax) & \
(pc_image_coord[:,1]>=ymin)
box_fov_inds = box_fov_inds & img_fov_inds
pc_in_box_fov = pc_rect[box_fov_inds, :]
# fig_path = str(data_idx) + ".png"
# x = pc_in_box_fov[:, 0]
# y = pc_in_box_fov[:, 1]
# plt.scatter(x, y, marker = ".", color='k')
# plt.savefig(fig_path)
# Get frustum angle (according to center pixel in 2D BOX)
box2d_center = np.array([(xmin + xmax) / 2.0,
(ymin + ymax) / 2.0])
uvdepth = np.zeros((1, 3))
uvdepth[0, 0:2] = box2d_center
uvdepth[0, 2] = 20 # some random depth
box2d_center_rect = calib.project_image_to_rect(uvdepth)
frustum_angle = -1 * np.arctan2(box2d_center_rect[0, 2],
box2d_center_rect[0, 0])
# 3D BOX: Get pts velo in 3d box
obj = objects[obj_idx]
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
_, inds = extract_pc_in_box3d(pc_in_box_fov, box3d_pts_3d)
label = np.zeros((pc_in_box_fov.shape[0]))
label[inds] = 1
# Get 3D BOX heading
heading_angle = obj.ry
# Get 3D BOX size
box3d_size = np.array([obj.l, obj.w, obj.h])
# Reject too far away object or object without points
# if ymax-ymin<25 or np.sum(label)==0:
# continue
id_list.append(data_idx)
box2d_list.append(np.array([xmin, ymin, xmax, ymax]))
box3d_list.append(box3d_pts_3d)
input_list.append(pc_in_box_fov)
label_list.append(label)
type_list.append(objects[obj_idx].type)
heading_list.append(heading_angle)
box3d_size_list.append(box3d_size)
frustum_angle_list.append(frustum_angle)
# collect statistics
pos_cnt += np.sum(label)
all_cnt += pc_in_box_fov.shape[0]
#print("length:", len(id_list))
print('Average pos ratio: %f' % (pos_cnt / float(all_cnt)))
print('Average npoints: %f' % (float(all_cnt) / len(id_list)))
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp)
pickle.dump(box2d_list, fp)
pickle.dump(box3d_list, fp)
pickle.dump(input_list, fp)
pickle.dump(label_list, fp)
pickle.dump(type_list, fp)
pickle.dump(heading_list, fp)
pickle.dump(box3d_size_list, fp)
pickle.dump(frustum_angle_list, fp)
if viz:
import mayavi.mlab as mlab
for i in range(10):
p1 = input_list[i]
seg = label_list[i]
fig = mlab.figure(figure=None,
bgcolor=(0.4, 0.4, 0.4),
fgcolor=None,
engine=None,
size=(500, 500))
mlab.points3d(p1[:, 0],
p1[:, 1],
p1[:, 2],
seg,
mode='point',
colormap='gnuplot',
scale_factor=1,
figure=fig)
fig = mlab.figure(figure=None,
bgcolor=(0.4, 0.4, 0.4),
fgcolor=None,
engine=None,
size=(500, 500))
mlab.points3d(p1[:, 2],
-p1[:, 0],
-p1[:, 1],
seg,
mode='point',
colormap='gnuplot',
scale_factor=1,
figure=fig)
raw_input()
def sv_user_traj_3d_interactive(gps_ds, sv_pos, user_pos, ele=10., azim=20.):
"""
This method will provide an interactive 3d model plotted using mayavi to show all trajectories.
:param gps_ds:
:param sv_pos:
:param user_pos:
:return:
"""
mlab.figure(1,
bgcolor=(0.48, 0.48, 0.48),
fgcolor=(0, 0, 0),
size=(400, 400))
mlab.clf()
##########################################################################
# Display continents outline, using the VTK Builtin surface 'Earth'
continents_src = BuiltinSurface(source='earth', name='Continents')
# The on_ratio of the Earth source controls the level of detail of the
# continents outline.
continents_src.data_source.on_ratio = 1
continents = mlab.pipeline.surface(continents_src, color=(0, 0, 0))
for svn in range(0, len(sv_pos)):
mlab.plot3d(sv_pos[svn, 0, :] / radius,
sv_pos[svn, 1, :] / radius,
sv_pos[svn, 2, :] / radius,
color=(1, 1, 0.5),
opacity=0.5,
tube_radius=None)
xml = len(sv_pos[svn, 0, :]) / 2
yml = len(sv_pos[svn, 1, :]) / 2
zml = len(sv_pos[svn, 2, :]) / 2
xm, ym, zm = sv_pos[svn, 0, int(xml)] / radius, sv_pos[
svn, 1, int(yml)] / radius, sv_pos[svn, 2, int(zml)] / radius
label = mlab.text(xm,
ym,
gps_ds.Rx_sv_list[svn],
z=zm,
width=0.0155 * len(gps_ds.Rx_sv_list[svn]))
label.property.shadow = True
mlab.plot3d(user_pos[:, 0] / radius,
user_pos[:, 1] / radius,
user_pos[:, 2] / radius,
color=(1, 1, 1),
opacity=0.5,
tube_radius=None)
xml = len(user_pos[:, 0]) / 2
yml = len(user_pos[:, 1]) / 2
zml = len(user_pos[:, 2]) / 2
xm, ym, zm = user_pos[int(xml), 0] / radius, user_pos[
int(yml), 1] / radius, user_pos[int(zml), 2] / radius
label = mlab.text(xm, ym, "User", z=zm, width=0.077)
label.property.shadow = True
###############################################################################
# Display a semi-transparent sphere, for the surface of the Earth
# We use a sphere Glyph, throught the points3d mlab function, rather than
# building the mesh ourselves, because it gives a better transparent
# rendering.
ocean_blue = (0.4, 0.5, 1.0)
sphere = mlab.points3d(
0,
0,
0,
scale_mode='none',
scale_factor=2,
# color=(0.67, 0.77, 0.93),
color=ocean_blue,
resolution=50,
opacity=.85,
name='Earth')
#
# These parameters, as well as the color, where tweaked through the GUI,
# with the record mode to produce lines of code usable in a script.
sphere.actor.property.specular = 0.45
sphere.actor.property.specular_power = 5
# Backface culling is necessary for more a beautiful transparent
# rendering.
sphere.actor.property.backface_culling = True
# Plot the equator and the tropiques
theta = np.linspace(0, 2 * np.pi, 100)
for angle in (-np.pi / 6, 0, np.pi / 6):
x = np.cos(theta) * np.cos(angle)
y = np.sin(theta) * np.cos(angle)
z = np.ones_like(theta) * np.sin(angle)
print(x)
mlab.plot3d(x, y, z, color=(1, 1, 1), opacity=0.2, tube_radius=None)
mlab.view(azim, ele)
mlab.show()
coords.append((float(long_), float(lat)))
########## 坐标转换##########
# 将经纬度的位置转换为三维坐标
import numpy as np
coords = np.array(coords)
lat, long = coords.T * np.pi / 180
x = np.cos(long) * np.cos(lat)
y = np.cos(long) * np.sin(lat)
z = np.sin(long)
##########建立窗口##########
from mayavi import mlab
mlab.figure(bgcolor=(0.48, 0.48, 0.48), size=(400, 400))
##########绘制地球##########
# 绘制半透明球体表示地球
sphere = mlab.points3d(0, 0, 0, scale_factor=2,
color=(0.67, 0.77, 0.93),
resolution=50,
opacity=0.7,
name='Earth')
# 调整镜面反射参数
sphere.actor.property.specular = 0.45
sphere.actor.property.specular_power = 5
# 设置背面剔除，以更好的显示透明效果
sphere.actor.property.backface_culling = True
def plot_graphs(locs, stations, nbsta, CLUSTER, nbmin, threshold):
"""
Displays two figures.
On the first plot, all events are represented. Those belonging to a cluster are color-coded
and labelled with the cluster index.
On the second plot, all events are also represented, but those belonging to a cluster are
colored in black. The links between events of a same cluster are also plotted and color-coded
in function of the number of stations where the correlation value exceeds the threshold.
Uses mlab from mayavi
:param locs: list of the whole Waveloc locations (each element of the list is a dictionary)
:param stations: dictionary of stations
:param nbsta:2-D matrix containing the number of stations where the cross-correlation value
is greater than a given threshold for all possible event pairs
:param CLUSTER: dictionary containing the indexes of the events for each cluster
:param nbmin: minimum number of stations where the cross-correlation value should be greater
than a given threshold to form a cluster
:param threshold: correlation value threshold set to form a cluster
:type locs: dictionary
:type stations: dictionary
:type nbsta: matrix
:type CLUSTER: dictionary
:type nbmin: integer
:type threshold: float
"""
from mayavi import mlab
# Event coordinates
stack_x, stack_y, stack_z = [], [], []
for loc in locs:
stack_x.append(loc['x_mean'])
stack_y.append(loc['y_mean'])
stack_z.append(-loc['z_mean'])
# Extract coordinates
xsta, ysta, zsta = [], [], []
for sta in sorted(stations):
xsta.append(stations[sta]['x'])
ysta.append(stations[sta]['y'])
zsta.append(stations[sta]['elev'])
# 3D PLOT USING MAYAVI
logging.info("Plotting...")
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(1000, 900))
mlab.points3d(xsta,
ysta,
zsta,
color=(1, 0, 0),
scale_factor=0.05,
mode='cube')
mlab.axes(extent=[362, 370, 7647, 7653, -0.5, 2.5], color=(0, 0, 0))
mlab.outline(extent=[362, 370, 7647, 7653, -0.5, 2.5], color=(0, 0, 0))
mlab.points3d(stack_x,
stack_y,
stack_z,
scale_factor=0.1,
color=(0.8, 0.8, 0.8))
mlab.title("threshold=%s, nbmin=%s" % (threshold, nbmin),
height=0.1,
size=0.35,
color=(0, 0, 0))
for i_ev in range(len(nbsta)):
for i_c in range(1, len(CLUSTER) + 1):
if i_ev + 1 in CLUSTER[i_c]:
mlab.points3d(stack_x[i_ev],
stack_y[i_ev],
stack_z[i_ev],
scale_factor=0.1,
color=tuple(
CZ_Clust_2_color(100 * (len(CLUSTER) - i_c) /
len(CLUSTER))))
mlab.text3d(stack_x[i_ev],
stack_y[i_ev],
stack_z[i_ev],
str(i_c),
color=(0, 0, 0),
scale=0.1)
logging.info("Done!")
logging.info("Plotting...")
mlab.figure(2, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(1000, 900))
mlab.points3d(xsta,
ysta,
zsta,
color=(1, 0, 0),
scale_factor=0.05,
mode='cube')
mlab.axes(extent=[362, 370, 7647, 7653, -0.5, 2.5], color=(0, 0, 0))
mlab.outline(extent=[362, 370, 7647, 7653, -0.5, 2.5], color=(0, 0, 0))
mlab.points3d(stack_x,
stack_y,
stack_z,
scale_factor=0.1,
color=(0.8, 0.8, 0.8))
mlab.title("threshold=%s, nbmin=%s" % (threshold, nbmin),
height=0.1,
size=0.35,
color=(0, 0, 0))
print nbsta
for ind_I in range(len(nbsta)):
for ind_J in range(ind_I + 1, len(nbsta)):
W_IJ = nbsta[ind_I, ind_J]
if W_IJ >= nbmin:
mlab.points3d(stack_x[ind_J],
stack_y[ind_J],
stack_z[ind_J],
scale_factor=0.1,
color=(0, 0, 0))
mlab.points3d(stack_x[ind_I],
stack_y[ind_I],
stack_z[ind_I],
scale_factor=0.1,
color=(0, 0, 0))
d = (stack_x[ind_J] - stack_x[ind_I],
stack_y[ind_J] - stack_y[ind_I],
stack_z[ind_J] - stack_z[ind_I])
norm = np.sqrt(d[0]**2 + d[1]**2 + d[2]**2)
mlab.quiver3d(stack_x[ind_I],
stack_y[ind_I],
stack_z[ind_I],
d[0],
d[1],
d[2],
color=tuple(CZ_W_2_color(W_IJ)),
mode='2ddash',
scale_factor=norm,
scale_mode='scalar')
logging.info("Done!")
mlab.show()
0), np.full(len(y_ob),
0), np.full(len(z_ob), 0)
for o in range(len(x_ob)):
x_ob_map[o] = int(round(float(x_ob[o]) / binSize)) + w
y_ob_map[o] = int(round(float(y_ob[o]) / binSize)) + w
z_ob_map[o] = int(round(float(z_ob[o]) / binSize)) + z_height
print('Smoothing')
gaussian = gf(a, sigma=sigma, truncate=3)
b = 2 * (expit(spread * gaussian) - 0.5)
from mayavi import mlab
import moviepy.editor as mpy
fig_myv = mlab.figure(size=(750, 700), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
fig_myv.scene.disable_render = True
#duration = 9
mlab.clf()
src = mlab.pipeline.scalar_field(b)
m = mlab.pipeline.iso_surface(
src,
#contours = [0.2, 0.3, 0.5],
contours=[0.1, 0.2, .3],
opacity=0.3,
colormap='Reds',
figure=fig_myv)
mlab.axes( #ranges = [-1300, 1300, -1300, 1300, -3500, 3500],
ranges=[-500, 500, -500, 500, -350, 350],
color=(0, 0, 0),
nb_labels=5,
import rasterio
from mayavi import mlab
from tvtk.api import tvtk
import numpy as np
# Use gadalwarp or qgis to reproject to utm 17N
filenames = ['be.tif', 'raw.tif']
outnames = ['be.png', 'raw.png']
for i in range(2):
dataset = rasterio.open(filenames[i])
surface = dataset.read()[0]
dataset.close()
# Get rid of any nodata values
surface[surface == np.min(surface)] = np.nan
mlab.figure(size=(640, 640), bgcolor=(1., 1., 1.))
surf = mlab.surf(surface, colormap='gist_earth', warp_scale=1)
mlab.savefig(outnames[i])
mlab.clf()
def draw_network_3D(graph, nodes_membership, **kwargs):
''' Draw the graph with its labels '''
from mayavi import mlab
plot_prop = {}
plot_prop['nodes_size'] = kwargs.get('nodes_size', 10)
plot_prop['edges_width'] = kwargs.get('edges_width', 1)
plot_prop['color_map'] = kwargs.get('color_map', plt.cm.Spectral)
plot_prop['font_size'] = kwargs.get('font_size', 15)
plot_prop['layout_method'] = kwargs.get('layout_method', 'neato')
plot_prop['node_alpha'] = kwargs.get('node_alpha', 0.8)
plot_prop['draw_edges'] = kwargs.get('draw_edges', False)
plot_prop['draw_edges_weights'] = kwargs.get('draw_edges_weights', False)
plot_prop['axisX'] = kwargs.get('axisX', 0)
plot_prop['axisY'] = kwargs.get('axisY', 2)
plot_prop['mst_sparsification'] = kwargs.get('mst_sparsification', False)
plot_prop['highlight_comm'] = kwargs.get('highlight_comm', None)
plot_prop['v_min'] = kwargs.get('v_min', np.min(nodes_membership.values()))
plot_prop['v_max'] = kwargs.get('v_max', np.max(nodes_membership.values()))
# http://colorbrewer2.org/index.html?type=qualitative&scheme=Paired&n=12
import brewer2mpl
# Generates perceptually diverging maps to better see differences of
# sequential data
lut = np.array(brewer2mpl.get_map('Paired', 'Qualitative', 12).mpl_colors)
# Add the alpha channel
lut = np.hstack((lut, np.ones((lut.shape[0], 1)))) * 255
# Reduce the alpha of non highlighted community
if plot_prop['highlight_comm'] is not None:
for i, colorRGB in enumerate(lut):
if i != plot_prop['highlight_comm']:
lut[i, :] = desaturate(colorRGB,
kwargs.get('desat_scale', 0.8),
alpha=kwargs.get('alpha', 1))
xyz = kwargs.get('node_pos', None)
#output_file_name = kwargs.get('output_file_name', None)
#xyz=np.array([node_pos[v] for v in sorted(graph.nodes())])
scalars = nodes_membership.values()
#mlab.options.backend = 'envisage'
mlab.figure(1, bgcolor=(0, 0, 0))
# mlab.clf()
# http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html
pts = mlab.points3d(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
scalars,
scale_factor=plot_prop['nodes_size'],
scale_mode='none',
colormap='Set2',
resolution=12,
reset_zoom=True,
vmax=plot_prop['v_max'],
vmin=plot_prop['v_min'])
# Set the new colormap look-up-table
pts.module_manager.scalar_lut_manager.lut.table = lut
if plot_prop['draw_edges'] is not None:
if plot_prop['highlight_comm'] is not None:
highlight_subgraph = list(
community_subgraphs(
graph, nodes_membership))[plot_prop['highlight_comm']]
if plot_prop['mst_sparsification']:
el = nx.minimum_spanning_tree(highlight_subgraph).edges()
else:
el = highlight_subgraph.edges()
else:
if plot_prop['mst_sparsification']:
el = nx.minimum_spanning_tree(graph).edges()
else:
el = graph.edges()
pts.mlab_source.dataset.lines = np.array(el)
tube = mlab.pipeline.tube(pts, tube_radius=plot_prop['edges_width'])
tube.filter.radius_factor = 1.0
tube.filter.vary_radius = 'vary_radius_by_scalar'
if plot_prop['highlight_comm'] is not None:
tubecolor = tuple(lut[plot_prop['highlight_comm'], 0:3] / 255.0)
mlab.pipeline.surface(tube, color=tubecolor)
mlab.show()
''' RISPOSTA DI AESTRIVEX CHE USA ANCHE LUI QUESTI DATI
http://stackoverflow.com/questions/22253298/mayavi-points3d-with-different-size-and-colors/
nodes = points3d(x,y,z)
nodes.glyph.scale_mode = 'scale_by_vector'
#this sets the vectors to be a 3x5000 vector showing some random scalars
nodes.mlab_source.dataset.point_data.vectors = np.tile( np.random.random((5000,)), (3,1))
nodes.mlab_source.dataset.point_data.scalars = np.random.random((5000,))
'''
'''
#PbBrF_lat = rsp.lattice.lattice_from_cif('/home/finn/Documents/eclipse_workspace/2017_MA3074/preparation/structure_simulation/PbBrF.cif', HKL_normal = [0,0,1], HKL_para_x=[0,1,0], energy=25)
from mayavi import mlab
try:
engine = mayavi.engine
except NameError:
from mayavi.api import Engine
engine = Engine()
engine.start()
if len(engine.scenes) == 0:
engine.new_scene(size=(600, 800))
scene = engine.scenes[0]
fig = mlab.gcf(engine)
mlab.figure(figure=fig, bgcolor=(1.0, 1.0, 1.0), fgcolor=(0.0, 0.0, 0.0), engine=engine)
Au_lat = rsp.lattice(
a = 4.0782,
b = 4.0782,
c = 4.0782,
alpha = 90,
beta = 90,
gamma = 90,
basis = [['Au', 0,0,0],['Au', 0.5,0.5,0],['Au', 0,0.5,0.5],['Au', 0.5,0,0.5]],
HKL_normal = [0,0,1],
HKL_para_x = [1,1,0]
)
index2 = struct1.get_rel_index(43900000)
comps1 = comps1[index1:index2 + 1]
comps2 = comps2[index1:index2 + 1]
index1 = struct1.chrom.getAbsoluteIndex(41900000)
index2 = struct1.chrom.getAbsoluteIndex(43900000)
struct1.subsamplePoints(index1, index2)
struct2.subsamplePoints(index1, index2)
colors = np.zeros_like(struct1.getPoints(), dtype=int)
index1 = struct1.get_rel_index(42700000)
index2 = struct1.get_rel_index(42900000)
colors[index1:index2] = -1
mlab.close(all=True)
mlab.figure(bgcolor=(1, 1, 1))
coords1 = np.array(struct1.getCoords())
mlab.plot3d(coords1[:, 0],
coords1[:, 1],
coords1[:, 2],
colors,
colormap="RdYlBu")
coords2 = np.array(struct2.getCoords())
mlab.plot3d(coords2[:, 0],
coords2[:, 1],
coords2[:, 2],
colors,
colormap="RdYlGn")
mlab.savefig("sup11a.png")
mlab.close(all=True)
if show_boundary:
xix_boundary_r_vals_t = xix_boundary_r_vals
xix_boundary_l_vals_t = xix_boundary_l_vals
if show_density or show_density_pert:
rho_vals_t = rho_vals
# ================================
# Starting figure and visualisation modules
# ================================
zgrid_zy, ygrid_zy = np.mgrid[0:nz:(nz) * 1j, 0:ny:(ny) * 1j]
fig = mlab.figure(
size=res
) # (1920, 1080) for 1080p , tuple(101 * np.array((16,9))) #16:9 aspect ratio for video upload
# Spacing of grid so that we can display a visualisation cube without having the same number of grid points in each dimension
spacing = np.array([x_spacing, z_spacing, y_spacing])
if show_density or show_density_pert:
# Scalar field density
rho = mlab.pipeline.scalar_field(rho_vals_t,
name="density",
figure=fig)
rho.spacing = spacing
mpf.volume_red_blue(rho, rho_vals_t)
#Masking points
if show_mag_front:
#w = pl.log(pdata['snr']) #title = "log(snr)" #fn = "log_snr" # driven rate #xs = pdata['ml'] #ys = pdata['ap'] #zs = pdata['dv'] xs = pdata['dv'] ys = pdata['ml'] zs = pdata['ap'] ws = pdata['drate'] - pdata['brate'] title = "driven rate" fn = "driven_rate" figure = mlab.figure(fgcolor=(1, 1, 1), size=(800, 600)) figure.scene.disable_render = True #mlab.points3d(pdata['ml'],pdata['ap'],pdata['dv'],pl.log(pdata['snr'])) #glyphs = mlab.points3d(pdata['ml'],pdata['ap'],pdata['dv'],w) glyphs = mlab.points3d(xs, ys, zs, ws) mlab.scalarbar(orientation='vertical', title=title) #mlab.axes(xlabel='ML',ylabel='AP',zlabel='DV',color=(1,1,1)) mlab.axes(xlabel='DV', ylabel='ML', zlabel='AP', color=(1, 1, 1)) #mlab.view(azimuth=270,reset_roll=True) loffset = 0.05 pid = ws.argmax() session = pdata['session'][pid] ch = pdata['ch'][pid] cl = pdata['cl'][pid] w = ws[pid]
def plot(self, labels=False, show=True):
'''
This function plots 2D and 3D models
:param labels:
:param show: If True, the plots are displayed at the end of this call. If False, plt.show() should be called outside this function
:return:
'''
if self.k == 3:
import mayavi.mlab as mlab
predictFig = mlab.figure(figure='predict')
# errorFig = mlab.figure(figure='error')
if self.testfunction:
truthFig = mlab.figure(figure='test')
dx = 1
pts = 25j
X, Y, Z = np.mgrid[0:dx:pts, 0:dx:pts, 0:dx:pts]
scalars = np.zeros(X.shape)
errscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
# errscalars[i][j][k1] = self.predicterr_normalized([X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
scalars[i][j][k1] = self.predict_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
if self.testfunction:
tfscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
tfplot = tfscalars[i][j][k1] = self.testfunction(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
plot = mlab.contour3d(tfscalars,
contours=15,
transparent=True,
figure=truthFig)
plot.compute_normals = False
# obj = mlab.contour3d(scalars, contours=10, transparent=True)
plot = mlab.contour3d(scalars,
contours=15,
transparent=True,
figure=predictFig)
plot.compute_normals = False
# errplt = mlab.contour3d(errscalars, contours=15, transparent=True, figure=errorFig)
# errplt.compute_normals = False
if show:
mlab.show()
if self.k == 2:
fig = pylab.figure(figsize=(8, 6))
samplePoints = list(zip(*self.X))
# Create a set of data to plot
plotgrid = 61
x = np.linspace(self.normRange[0][0],
self.normRange[0][1],
num=plotgrid)
y = np.linspace(self.normRange[1][0],
self.normRange[1][1],
num=plotgrid)
# x = np.linspace(0, 1, num=plotgrid)
# y = np.linspace(0, 1, num=plotgrid)
X, Y = np.meshgrid(x, y)
# Predict based on the optimized results
zs = np.array([
self.predict([x, y]) for x, y in zip(np.ravel(X), np.ravel(Y))
])
Z = zs.reshape(X.shape)
# Z = (Z*(self.ynormRange[1]-self.ynormRange[0]))+self.ynormRange[0]
#Calculate errors
zse = np.array([
self.predict_var([x, y])
for x, y in zip(np.ravel(X), np.ravel(Y))
])
Ze = zse.reshape(X.shape)
spx = (self.X[:, 0] * (self.normRange[0][1] - self.normRange[0][0])
) + self.normRange[0][0]
spy = (self.X[:, 1] * (self.normRange[1][1] - self.normRange[1][0])
) + self.normRange[1][0]
contour_levels = 25
ax = fig.add_subplot(222)
CS = pylab.contourf(X, Y, Ze, contour_levels)
pylab.colorbar()
pylab.plot(spx, spy, 'ow')
ax = fig.add_subplot(221)
if self.testfunction:
# Setup the truth function
zt = self.testfunction(
np.array(list(zip(np.ravel(X), np.ravel(Y)))))
ZT = zt.reshape(X.shape)
CS = pylab.contour(X,
Y,
ZT,
contour_levels,
colors='k',
zorder=2)
# contour_levels = np.linspace(min(zt), max(zt),50)
if self.testfunction:
contour_levels = CS.levels
delta = np.abs(contour_levels[0] - contour_levels[1])
contour_levels = np.insert(contour_levels, 0,
contour_levels[0] - delta)
contour_levels = np.append(contour_levels,
contour_levels[-1] + delta)
CS = plt.contourf(X, Y, Z, contour_levels, zorder=1)
pylab.plot(spx, spy, 'ow', zorder=3)
pylab.colorbar()
ax = fig.add_subplot(212, projection='3d')
# fig = plt.gcf()
#ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z, rstride=3, cstride=3, alpha=0.4)
if self.testfunction:
ax.plot_wireframe(X, Y, ZT, rstride=3, cstride=3)
if show:
pylab.show()
if gid % 2 == 1 and gid >= params.gid_granule_begin + granules.ngranule:
ggid = gd.gid_dict[gid + 1][3]
w_new = weights[gid]
if gcol.has_key(ggid):
w_old = gcol[ggid]
if w_old < w_new:
gcol[ggid] = w_new
else:
gcol.update({ggid: w_new})
return gcol
import bindict as gd
gd.load(filedict)
from mayavi.mlab import figure, text
fig = figure(bgcolor=(0, 0, 0))
from mayavi import mlab
points3d = mlab.points3d
import params
import granules
class GranulesManager:
def __init__(self):
self.gran = set()
self.actor1 = None
self.gran_color = (0., 157 / 255., 157 / 255.)
self.mitrals = None
self.colors = None
self.projected = False
def apply_transform_to_points(points, trans_mat):
"""a function that applies a 4x4 transformation matrix to an of
homogeneous points. The array of points should have shape Nx4"""
if not trans_mat.shape == (4, 4):
raise ValueError('transform matrix must be 4x4')
if not points.shape[1] == 4:
raise ValueError('point array must have shape Nx4')
return np.dot(trans_mat, points.T).T
if __name__ == '__main__':
f = mlab.figure()
N = 4
# create a few points in 3-space
X = np.random.random_integers(-3, 3, N)
Y = np.random.random_integers(-3, 3, N)
Z = np.random.random_integers(-3, 3, N)
# plot the points with mlab
pts = mlab.points3d(X, Y, Z)
# now were going to create a single N x 4 array of our points
# adding a fourth column of ones expresses the world points in
# homogenous coordinates
W = np.ones(X.shape)
def saveFigure(self, name=None):
'''
Similar to plot, except that figures are saved to file
:param name: the file name of the plot image
'''
if self.k == 3:
import mayavi.mlab as mlab
mlab.options.offscreen = True
predictFig = mlab.figure(figure='predict')
mlab.clf(figure='predict')
errorFig = mlab.figure(figure='error')
mlab.clf(figure='error')
if self.testfunction:
truthFig = mlab.figure(figure='test')
mlab.clf(figure='test')
dx = 1
pts = 75j
X, Y, Z = np.mgrid[0:dx:pts, 0:dx:pts, 0:dx:pts]
scalars = np.zeros(X.shape)
errscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
errscalars[i][j][k1] = self.predicterr_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
scalars[i][j][k1] = self.predict_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
if self.testfunction:
tfscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
tfscalars[i][j][k1] = self.testfunction(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
mlab.contour3d(tfscalars,
contours=15,
transparent=True,
figure=truthFig,
compute_normals=False)
# obj = mlab.contour3d(scalars, contours=10, transparent=True)
pred = mlab.contour3d(scalars,
contours=15,
transparent=True,
figure=predictFig)
pred.compute_normals = False
errpred = mlab.contour3d(errscalars,
contours=15,
transparent=True,
figure=errorFig)
errpred.compute_normals = False
mlab.savefig('%s_prediction.wrl' % name, figure=predictFig)
mlab.savefig('%s_error.wrl' % name, figure=errorFig)
if self.testfunction:
mlab.savefig('%s_actual.wrl' % name, figure=truthFig)
mlab.close(all=True)
if self.k == 2:
samplePoints = list(zip(*self.X))
# Create a set of data to plot
plotgrid = 61
x = np.linspace(0, 1, num=plotgrid)
y = np.linspace(0, 1, num=plotgrid)
X, Y = np.meshgrid(x, y)
# Predict based on the optimized results
zs = np.array([
self.predict_normalized([x, y])
for x, y in zip(np.ravel(X), np.ravel(Y))
])
Z = zs.reshape(X.shape)
Z = (
Z *
(self.ynormRange[1] - self.ynormRange[0])) + self.ynormRange[0]
# Calculate errors
zse = np.array([
self.predicterr_normalized([x, y])
for x, y in zip(np.ravel(X), np.ravel(Y))
])
Ze = zse.reshape(X.shape)
if self.testfunction:
# Setup the truth function
zt = self.testfunction(
np.array(
list(
zip(
np.ravel((X * (self.normRange[0][1] -
self.normRange[0][0])) +
self.normRange[0][0]),
np.ravel((Y * (self.normRange[1][1] -
self.normRange[1][0])) +
self.normRange[1][0])))))
ZT = zt.reshape(X.shape)
# Plot real world values
X = (X * (self.normRange[0][1] - self.normRange[0][0])
) + self.normRange[0][0]
Y = (Y * (self.normRange[1][1] - self.normRange[1][0])
) + self.normRange[1][0]
spx = (self.X[:, 0] * (self.normRange[0][1] - self.normRange[0][0])
) + self.normRange[0][0]
spy = (self.X[:, 1] * (self.normRange[1][1] - self.normRange[1][0])
) + self.normRange[1][0]
fig = plt.figure(figsize=(8, 6))
# contour_levels = np.linspace(min(zt), max(zt),50)
contour_levels = 15
plt.plot(spx, spy, 'ow')
cs = plt.colorbar()
if self.testfunction:
pass
plt.plot(spx, spy, 'ow')
cs = plt.colorbar()
plt.plot(spx, spy, 'ow')
ax = fig.add_subplot(212, projection='3d')
ax.plot_surface(X, Y, Z, rstride=3, cstride=3, alpha=0.4)
if self.testfunction:
ax.plot_wireframe(X, Y, ZT, rstride=3, cstride=3)
if name:
plt.savefig(name)
else:
plt.savefig('pyKrigingResult.png')
if __name__ == '__main__':
lidar_dir = '/root/share/project/didi/data/didi/didi-2/Out/1/15/lidar'
gt_boxes3d_dir = '/root/share/project/didi/data/didi/didi-2/Out/1/15/processed/gt_boxes3d'
lidar_top_dir = '/root/share/project/didi/data/didi/didi-2/Out/1/15/processed/lidar_top'
lidar_top_img_dir = '/root/share/project/didi/data/didi/didi-2/Out/1/15/processed/lidar_top_img'
mark_dir = '/root/share/project/didi/data/didi/didi-2/Out/1/15/processed/mark-top-box'
avi_file = '/root/share/project/didi/data/didi/didi-2/Out/1/15/processed/mark-top-box.avi'
os.makedirs(mark_dir, exist_ok=True)
os.makedirs(lidar_top_dir, exist_ok=True)
os.makedirs(lidar_top_img_dir, exist_ok=True)
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(500, 500))
for file in sorted(glob.glob(lidar_dir + '/*.npy')):
name = os.path.basename(file).replace('.npy', '')
lidar_file = lidar_dir + '/' + name + '.npy'
top_file = lidar_top_dir + '/' + name + '.npy'
top_img_file = lidar_top_img_dir + '/' + name + '.png'
mark_file = mark_dir + '/' + name + '.png'
boxes3d_file = gt_boxes3d_dir + '/' + name + '.npy'
lidar = np.load(lidar_file)
top, top_img = lidar_to_top(lidar)
boxes3d = np.load(boxes3d_file)
def draw_lidar(pc,
color=None,
fig=None,
bgcolor=(0, 0, 0),
pts_scale=1,
pts_mode='point',
pts_color=None):
"""
Add lidar points
Args:
pc: point cloud xyz [npoints, 3]
color:
fig: fig handler
Returns:
"""
''' Draw lidar points
Args:
pc: numpy array (n,3) of XYZ
color: numpy array (n) of intensity or whatever
fig: mayavi figure handler, if None create new one otherwise will use it
Returns:
fig: created or used fig
'''
if fig is None:
fig = mlab.figure(figure=None,
bgcolor=bgcolor,
fgcolor=None,
engine=None,
size=(1600, 1000))
if color is None:
color = pc[:, 2]
# add points
mlab.points3d(pc[:, 0],
pc[:, 1],
pc[:, 2],
color,
color=pts_color,
mode=pts_mode,
colormap='gnuplot',
scale_factor=pts_scale,
figure=fig)
# # draw origin
# mlab.points3d(0, 0, 0, color=(1, 1, 1), mode='sphere', scale_factor=0.2)
# draw axis
axes = np.array([
[2., 0., 0., 0.],
[0., 2., 0., 0.],
[0., 0., 2., 0.],
],
dtype=np.float64)
mlab.plot3d([0, axes[0, 0]], [0, axes[0, 1]], [0, axes[0, 2]],
color=(1, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[1, 0]], [0, axes[1, 1]], [0, axes[1, 2]],
color=(0, 1, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[2, 0]], [0, axes[2, 1]], [0, axes[2, 2]],
color=(0, 0, 1),
tube_radius=None,
figure=fig)
return fig
import numpy as np
from mayavi import mlab
import pickle
import scipy.io
import glob
import os
import sys
print(sys.argv)
k = sys.argv[1]
#mlab.options.backend = 'envisage'
mlab.figure(figure=None,
bgcolor=None,
fgcolor=None,
engine=None,
size=(1000, 800))
#list_of_files = glob.glob('./mats_8_neuru/*.mat') # * means all if need specific format then *.csv
list_of_files = glob.glob(
f'./mats_{k}/*.mat') # * means all if need specific format then *.csv
latest_file = max(list_of_files, key=os.path.getctime)
#fname = 'data/sim_2400_normal_q.npy'
#P = scipy.io.loadmat('mats/t10.mat');
if len(sys.argv) == 3:
n = sys.argv[2]
P = scipy.io.loadmat(f'./mats_{k}/t{n}.mat')
else:
P = scipy.io.loadmat(latest_file)
print(latest_file)
#P = P['p']
pairs1_ = (np.dot(R, pairs1.T) + T.reshape(3, 1)).T
Colors0 = np.ones((PC0.shape[0], 1), dtype=np.float32) * 1.0
KeyColors0 = np.ones((KeyPts0.shape[0], 1), dtype=np.float32) * 0.5
KeyColors0_ = np.ones((KeyPts0.shape[0], 1), dtype=np.float32) * 0.8
Colors1 = np.ones((PC1.shape[0], 1), dtype=np.float32) * 0.0
KeyColors1 = np.ones((KeyPts1.shape[0], 1), dtype=np.float32) * 0.5
KeyColors1_ = np.ones((KeyPts1.shape[0], 1), dtype=np.float32) * 0.2
Colors4FusedPC = np.r_[Colors0, Colors1]
# Colors4FusedPC=np.r_[SingleColor0, SingleColor1]
Colors4FusedPC = Colors4FusedPC.reshape(Colors4FusedPC.shape[0], )
shift4Show = 12
fig = mlab.figure(bgcolor=(0, 0, 0), size=(1500, 900))
PtSize = 0.4
node = mlab.points3d(PC0[:, 0],
PC0[:, 1],
PC0[:, 2],
mode="point",
figure=fig)
node.glyph.scale_mode = 'scale_by_vector'
node.mlab_source.dataset.point_data.scalars = Colors0
#node.mlab_source.dataset.point_data.scalars = SingleColor0
node = mlab.points3d(PC1[:, 0],
PC1[:, 1],
PC1[:, 2] + shift4Show,
mode="point",
def draw_lidar(
pc,
color=None,
fig=None,
bgcolor=(0, 0, 0),
pts_scale=0.3,
pts_mode="sphere",
pts_color=None,
color_by_intensity=False,
pc_label=False,
):
""" Draw lidar points
Args:
pc: numpy array (n,3) of XYZ
color: numpy array (n) of intensity or whatever
fig: mayavi figure handler, if None create new one otherwise will use it
Returns:
fig: created or used fig
"""
# ind = (pc[:,2]< -1.65)
# pc = pc[ind]
pts_mode = "point"
print("====================", pc.shape)
if fig is None:
fig = mlab.figure(figure=None,
bgcolor=bgcolor,
fgcolor=None,
engine=None,
size=(1600, 1000))
if color is None:
color = pc[:, 2]
if pc_label:
color = pc[:, 4]
if color_by_intensity:
color = pc[:, 2]
mlab.points3d(
pc[:, 0],
pc[:, 1],
pc[:, 2],
color,
color=pts_color,
mode=pts_mode,
colormap="gnuplot",
scale_factor=pts_scale,
figure=fig,
)
# draw origin
mlab.points3d(0, 0, 0, color=(1, 1, 1), mode="sphere", scale_factor=0.2)
# draw axis
axes = np.array(
[[2.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 2.0, 0.0]],
dtype=np.float64,
)
mlab.plot3d(
[0, axes[0, 0]],
[0, axes[0, 1]],
[0, axes[0, 2]],
color=(1, 0, 0),
tube_radius=None,
figure=fig,
)
mlab.plot3d(
[0, axes[1, 0]],
[0, axes[1, 1]],
[0, axes[1, 2]],
color=(0, 1, 0),
tube_radius=None,
figure=fig,
)
mlab.plot3d(
[0, axes[2, 0]],
[0, axes[2, 1]],
[0, axes[2, 2]],
color=(0, 0, 1),
tube_radius=None,
figure=fig,
)
# draw fov (todo: update to real sensor spec.)
fov = np.array(
[[20.0, 20.0, 0.0, 0.0], [20.0, -20.0, 0.0, 0.0]],
dtype=np.float64 # 45 degree
)
mlab.plot3d(
[0, fov[0, 0]],
[0, fov[0, 1]],
[0, fov[0, 2]],
color=(1, 1, 1),
tube_radius=None,
line_width=1,
figure=fig,
)
mlab.plot3d(
[0, fov[1, 0]],
[0, fov[1, 1]],
[0, fov[1, 2]],
color=(1, 1, 1),
tube_radius=None,
line_width=1,
figure=fig,
)
# draw square region
TOP_Y_MIN = -20
TOP_Y_MAX = 20
TOP_X_MIN = 0
TOP_X_MAX = 40
TOP_Z_MIN = -2.0
TOP_Z_MAX = 0.4
x1 = TOP_X_MIN
x2 = TOP_X_MAX
y1 = TOP_Y_MIN
y2 = TOP_Y_MAX
mlab.plot3d(
[x1, x1],
[y1, y2],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
mlab.plot3d(
[x2, x2],
[y1, y2],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
mlab.plot3d(
[x1, x2],
[y1, y1],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
mlab.plot3d(
[x1, x2],
[y2, y2],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
# mlab.orientation_axes()
mlab.view(
azimuth=180,
elevation=70,
focalpoint=[12.0909996, -1.04700089, -2.03249991],
distance=62.0,
figure=fig,
)
return fig
A = A.tocsr()
# create the right-hand-side vector, consisting of the forcing term
# for the interior nodes and zeros for the boundary nodes
d = np.zeros(N)
d[interior] = forcing(nodes[interior, 0], nodes[interior, 1], nodes[interior,
2])
d[boundary] = true_soln(nodes[boundary, 0], nodes[boundary, 1], nodes[boundary,
2])
# find the solution at the nodes
u = scipy.sparse.linalg.spsolve(A, d)
utrue = true_soln(nodes[:, 0], nodes[:, 1], nodes[:, 2])
err = u - utrue
mlab.figure(1)
mlab.triangular_mesh(vert[:, 0],
vert[:, 1],
vert[:, 2],
smp,
color=(1.0, 1.0, 1.0),
opacity=0.1)
scatter_contour(nodes, err, contours=8, opacity=0.3)
mlab.points3d(nodes[:, 0],
nodes[:, 1],
nodes[:, 2],
color=(0.0, 0.0, 0.0),
scale_factor=0.01)
mlab.title('error')
mlab.figure(2)
mlab.triangular_mesh(vert[:, 0],
# -*- coding: utf-8 -*-
from mayavi import mlab
from figure_imagedata import plot_cell
if __name__ == "__main__":
from tvtk.api import tvtk
import numpy as np
x = np.array([0, 3, 9, 15])
y = np.array([0, 1, 5])
z = np.array([0, 2, 3])
r = tvtk.RectilinearGrid()
r.x_coordinates = x
r.y_coordinates = y
r.z_coordinates = z
r.dimensions = len(x), len(y), len(z)
r.point_data.scalars = np.arange(0.0, r.number_of_points)
r.point_data.scalars.name = 'scalars'
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
mlab.pipeline.surface(mlab.pipeline.extract_edges(r), color=(0, 0, 0))
mlab.pipeline.glyph(r, mode='sphere', scale_factor=0.5, scale_mode='none')
plot_cell(r.get_cell(1))
mlab.text(0.01, 0.9, "get_cell(1)", width=0.25)
axes = mlab.orientation_axes()
mlab.show()
m4 = 1
m5 = 2
m6 = 2
m7 = 4
s = sin(m0 * phi)**m1 + cos(m2 * phi)**m3 + sin(m4 * theta)**m5 + cos(
m6 * theta)**m7
x = sin(phi) * cos(theta)
y = cos(phi)
z = sin(phi) * sin(theta)
################################################################################
# Plot the data
from mayavi import mlab
# A first plot in 3D
fig = mlab.figure(1)
mlab.clf()
mesh = mlab.mesh(x, y, z, scalars=s)
cursor3d = mlab.points3d(0.,
0.,
0.,
mode='axes',
color=(0, 0, 0),
scale_factor=0.5)
mlab.title('Click on the ball')
# A second plot, flat
fig2d = mlab.figure(2)
mlab.clf()
im = mlab.imshow(s)
cursor = mlab.points3d(0,
def plot3dformesh(x, cv, f):
cv = band.toZeroStatic(cv)
if MPI.COMM_WORLD.rank == 0:
v2 = cv.getArray()
pl.figure(bgcolor=(1, 1, 1), fgcolor=(0.5, 0.5, 0.5))
pl.triangular_mesh(x[:, 0], x[:, 1], x[:, 2], f, scalars=v2)
lower_thr = sorted_data[int(0.2 * l)] upper_thr = sorted_data[int(0.8 * l)] # The white matter boundary: find the densest part of the upper half # of histogram, and take a value 10% higher, to cut _in_ the white matter hist, bins = np.histogram(data[data > np.mean(data)], bins=50) brain_thr_idx = np.argmax(hist) brain_thr = bins[brain_thr_idx + 4] del hist, bins, brain_thr_idx # Display the data ############################################################# from mayavi import mlab from tvtk.api import tvtk fig = mlab.figure(bgcolor=(0, 0, 0), size=(400, 500)) # to speed things up fig.scene.disable_render = True src = mlab.pipeline.scalar_field(data) # Our data is not equally spaced in all directions: src.spacing = [1, 1, 1.5] src.update_image_data = True #---------------------------------------------------------------------- # Brain extraction pipeline # In the following, we create a Mayavi pipeline that strongly # relies on VTK filters. For this, we make heavy use of the # mlab.pipeline.user_defined function, to include VTK filters in # the Mayavi pipeline.
