def render_mesh(mesh_trimesh, camera_center, camera_transl, focal_length, img_width, img_height):
import pyrender
import trimesh
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.0,
alphaMode='OPAQUE',
baseColorFactor=(1.0, 1.0, 0.9, 1.0))
script_dir = os.path.dirname(os.path.realpath(__file__))
vertex_colors = np.loadtxt(os.path.join(script_dir, 'smplx_verts_colors.txt'))
mesh_new = trimesh.Trimesh(vertices=mesh_trimesh.vertices, faces=mesh_trimesh.faces, vertex_colors=vertex_colors)
mesh_new.vertex_colors = vertex_colors
print("mesh visual kind: %s" % mesh_new.visual.kind)
#mesh = pyrender.Mesh.from_points(out_mesh.vertices, colors=vertex_colors)
mesh = pyrender.Mesh.from_trimesh(mesh_new, smooth=False, wireframe=False)
scene = pyrender.Scene(bg_color=[1.0, 1.0, 1.0, 0.0],
ambient_light=(0.3, 0.3, 0.3))
#scene = pyrender.Scene(bg_color=[0.0, 0.0, 0.0, 0.0])
scene.add(mesh, 'mesh')
camera_pose = np.eye(4)
camera_pose[:3, 3] = camera_transl
camera = pyrender.camera.IntrinsicsCamera(
fx=focal_length, fy=focal_length,
cx=camera_center[0], cy=camera_center[1])
scene.add(camera, pose=camera_pose)
light = pyrender.light.DirectionalLight()
scene.add(light)
r = pyrender.OffscreenRenderer(viewport_width=img_width,
viewport_height=img_height,
point_size=1.0)
color, _ = r.render(scene, flags=pyrender.RenderFlags.RGBA)
color = color.astype(np.float32) / 255.0
output_img = color[:, :, 0:3]
output_img = (output_img * 255).astype(np.uint8)
return output_img
def binary_to_trimesh(binary, step=1):
""" Convert binary voxel image to a mesh
Marching cubes meshed output from
http://scikit-image.org/docs/dev/api/skimage.measure.html#marching-cubes-lewiner
Parameters
----------
binary: i-by-j-by-k array
array to mesh, assumed to be binary segmentation
step: int (default 1)
number of voxels to step over when generating mesh, larger is courser
"""
verts, faces, _, _ = skimage.measure.marching_cubes(binary, step_size=step)
mesh = trimesh.Trimesh(verts, faces)
mesh.fix_normals()
return mesh
def random_triangles(n=3):
""" Create n triangles which can overlap """
coords = []
for i in range(n * 3):
coords.append(
[random.random() * 5,
random.random() * 5,
random.random() * 5])
coords = np.array(coords)
faces = []
for i in range(n):
faces.append([i * 3, i * 3 + 1, i * 3 + 2])
faces = np.array(faces)
return trimesh.Trimesh(faces=faces, vertices=coords, process=False)
def ConvertToSTL():
pointcloud = o3d.io.read_point_cloud("temps\pointCloudTest.ply")
pointcloud.estimate_normals()
# estimate radius voor de rolling ball
distances = pcd.compute_nearest_neighbor_distance()
avg_dist = np.mean(distances)
radius = 1.5 * avg_dist
mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_ball_pivoting(
pcd, o3d.utility.DoubleVector([radius, radius * 2]))
output = trimesh.Trimesh(np.asarray(mesh.vertices),
np.asarray(mesh.triangles),
vertex_normals=np.asarray(mesh.vertex_normals))
output.export('eindresultaat.stl')
def compute_geodesic_matrix(verts, faces, NN):
# get adjacency matrix
mesh = trimesh.Trimesh(vertices=verts, faces=faces, process=False)
vertex_adjacency = mesh.vertex_adjacency_graph
vertex_adjacency_matrix = nx.adjacency_matrix(vertex_adjacency,
range(verts.shape[0]))
# get adjacency distance matrix
graph_x_csr = neighbors.kneighbors_graph(verts,
n_neighbors=NN,
mode='distance',
include_self=False)
distance_adj = csr_matrix((verts.shape[0], verts.shape[0])).tolil()
distance_adj[vertex_adjacency_matrix != 0] = graph_x_csr[
vertex_adjacency_matrix != 0]
# compute geodesic matrix
geodesic_x = graph_shortest_path(distance_adj, directed=False)
return geodesic_x
def load_mesh(gifti_file):
"""
load gifti_file and create a trimesh object
:param gifti_file: str, path to the gifti file on the disk
:return: the corresponding trimesh object
"""
g = nb.load(gifti_file)
coords, faces = g.get_arrays_from_intent(
nb.nifti1.intent_codes['NIFTI_INTENT_POINTSET'])[0].data, \
g.get_arrays_from_intent(
nb.nifti1.intent_codes['NIFTI_INTENT_TRIANGLE'])[0].data
metadata = g.meta.metadata
metadata['filename'] = gifti_file
return trimesh.Trimesh(faces=faces,
vertices=coords,
metadata=metadata,
process=False)
def load_scene(path=None, scene_id=0):
scene = V2aScene(scene_id=scene_id)
base_path = os.path.dirname(os.path.abspath(__file__))
if path is None:
path = os.path.join(base_path, 'SHOP_VRB_scenes_V2A.json')
f = open(path)
data = json.load(f)
scene.objects = []
for idx, obj in enumerate(data['scenes'][scene_id]['objects']):
index = idx
fname = str(obj['file'])
point = obj['3d_coords']
rotation = obj['rotation']
rot = obj['orientation']
quat = Quaternion(rot[1], rot[2], rot[3], rot[0])
#
# bb_dim = [obj['bbox']['x'], obj['bbox']['y'], obj['bbox']['z']]
# bb_pos = copy.copy(point)
# bb_pos[2] += 0.5*bb_dim[2] #TODO: thig objects is only valid for standing objects
vert = np.linspace(0, 7, 7, dtype=int)
mesh = trimesh.Trimesh(
vertices=obj['bbox_robot_coords'],
faces=[[t1, t2, t3]
for t1, t2, t3 in zip(vert[0:-2], vert[1:-1], vert[2:])
])
bb_dim = mesh.bounding_box.extents
bb_pos = mesh.bounding_box.center_mass
# bb_rot = [0, 0, 0, 1.0]
scale = obj['scale_factor']
scene.objects.append(
myObject(index,
fname,
point,
rotation,
bb_dim,
bb_pos,
scale,
orientation=quat,
gripper_setting=getGripperSetting(fname)))
for obj in scene.objects:
print(obj.ID, ': {}'.format(obj.fname))
return scene
def visualize_query_points(query_pts_ms, query_dist_ms, file_out_off):
import trimesh
# grey-scale distance
query_dist_abs_ms = np.abs(query_dist_ms)
query_dist_abs_normalized_ms = query_dist_abs_ms / query_dist_abs_ms.max()
# red-green: inside-outside
query_dist_col = np.zeros((query_dist_ms.shape[0], 3))
pos_dist = query_dist_ms < 0.0
neg_dist = query_dist_ms > 0.0
query_dist_col[pos_dist, 0] = 0.5 + 0.5 * query_dist_abs_normalized_ms[pos_dist]
query_dist_col[neg_dist, 1] = 0.5 + 0.5 * query_dist_abs_normalized_ms[neg_dist]
mesh = trimesh.Trimesh(vertices=query_pts_ms, vertex_colors=query_dist_col)
mesh.export(file_out_off)
def main():
global OUTPUT_DIR
os.makedirs(OUTPUT_DIR, exist_ok=True)
data_dir = download_maybe(output_dir=OUTPUT_DIR)
dataset = tf.data.Dataset.list_files(os.path.join(data_dir, "**", "**",
"**.off"),
shuffle=True)
dataset = dataset.map(read_OFF_file,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
# dataset = dataset.batch(1)
dataset = dataset.prefetch(1)
for x in dataset:
(points, faces), path = x
path = path.numpy().decode()
tm = trimesh.Trimesh(vertices=points, faces=faces)
tm.show(caption=path)
def marching_cube_mesh(cube_lattice, tiles_path):
# extract cube indices
cube_ind = np.transpose(np.indices(cube_lattice.shape),
(1, 2, 3, 0)).reshape(-1, 3)
# extract cube positions
cube_pos = (cube_ind + 0.5) * cube_lattice.unit + cube_lattice.minbound
# extract cube tid
cube_tid = cube_lattice.ravel()
# remove the cube position and tid where tid is 0
filled_cube_pos = cube_pos[cube_tid > 0]
filled_cube_tid = cube_tid[cube_tid > 0]
# load tiles
tiles = [0]
for i in range(1, 256):
tile_path = os.path.join(tiles_path, 't_' + f'{i:03}' + '.obj')
tile = tm.load(tile_path)
tile.vertices *= cube_lattice.unit
tiles.append(tile)
last_v_count = 0
vertice_list = []
face_list = []
# place tiles
for i in range(1, filled_cube_tid.size):
# extract current tile
tile = tiles[filled_cube_tid[i]]
# append the vertices
vertice_list.append(tile.vertices + filled_cube_pos[i])
face_list.append(tile.faces + last_v_count)
last_v_count += len(tile.vertices)
vs = []
fs = []
# if len(vertice_list):
vs = np.vstack(vertice_list)
fs = np.vstack(face_list)
tile_mesh = tm.Trimesh(vs, fs)
return tile_mesh
def to_trimesh(self) -> 'trimesh.Trimesh':
""" Returns trimesh representation of this volume.
See Also
--------
https://github.com/mikedh/trimesh
trimesh GitHub page.
"""
try:
import trimesh
except ImportError:
raise ImportError('Unable to import trimesh. Please make sure it '
'is installed properly')
return trimesh.Trimesh(vertices=self.vertices,
faces=self.faces) # type ignore
def render(self, img, verts, cam, angle=None, axis=None, color=[1.0, 1.0, 0.9]):
mesh = trimesh.Trimesh(vertices=verts, faces=self.faces)
Rx = trimesh.transformations.rotation_matrix(math.radians(180), [1, 0, 0])
mesh.apply_transform(Rx)
if angle and axis:
R = trimesh.transformations.rotation_matrix(math.radians(angle), axis)
mesh.apply_transform(R)
sx, sy, tx, ty = cam
camera = WeakPerspectiveCamera(
scale=[sx, sy],
translation=[tx, ty],
zfar=1000.
)
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.0,
alphaMode='OPAQUE',
baseColorFactor=(color[0], color[1], color[2], 1.0)
)
mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
mesh_node = self.scene.add(mesh, 'mesh')
camera_pose = np.eye(4)
cam_node = self.scene.add(camera, pose=camera_pose)
if self.wireframe:
render_flags = RenderFlags.RGBA | RenderFlags.ALL_WIREFRAME
else:
render_flags = RenderFlags.RGBA
rgb, _ = self.renderer.render(self.scene, flags=render_flags)
valid_mask = (rgb[:, :, -1] > 0)[:, :, np.newaxis]
output_img = rgb[:, :, :-1] * valid_mask + (1 - valid_mask) * img
image = output_img.astype(np.uint8)
self.scene.remove_node(mesh_node)
self.scene.remove_node(cam_node)
return image
def test_x_distance():
import trimesh
vertices = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [-1, 0, 0]])
faces = np.array([[2, 1, 0], [0, 3, 2]])
mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
points = np.array([[0, 1, 0]])
R = points[:, None, None, :] - mesh.vertices[faces][None, :, :, :]
x = integrals.x_distance(R, mesh.face_normals, mesh.area_faces)
assert_allclose(x, np.array([[[0.0, 1.0, 0.0], [2.0, -1.0, -1.0]]]))
def get_surface_curvature(verts,
faces,
normals,
radius=1,
num_sampled_points=300,
mode='grid'):
"""
compute the shortest path between random two random points on the surface.
if mode is grid the points are sampled regularly, else random the points are sampled randomly
"""
num_nodes = verts.shape[0] - 1
sampled_nodes = node_sampling(mode,
sample_size=num_sampled_points,
total_nodes=num_nodes)
x = trimesh.Trimesh(vertices=verts, faces=faces, vertex_normals=normals)
return curvature.discrete_mean_curvature_measure(x, verts[sampled_nodes],
radius)
def to_mesh(self, color=colors['red']):
v = np.array([[0.0000, -1.000, 0.0000], [0.7236, -0.447, 0.5257],
[-0.278, -0.447, 0.8506], [-0.894, -0.447, 0.0000],
[-0.278, -0.447, -0.850], [0.7236, -0.447, -0.525],
[0.2765, 0.4472, 0.8506], [-0.723, 0.4472, 0.5257],
[-0.720, 0.4472, -0.525], [0.2763, 0.4472, -0.850],
[0.8945, 0.4472, 0.0000], [0.0000, 1.0000, 0.0000],
[-0.165, -0.850, 0.4999], [0.4253, -0.850, 0.3090],
[0.2629, -0.525, 0.8090], [0.4253, -0.850, -0.309],
[0.8508, -0.525, 0.0000], [-0.525, -0.850, 0.0000],
[-0.688, -0.525, 0.4999], [-0.162, -0.850, -0.499],
[-0.688, -0.525, -0.499], [0.2628, -0.525, -0.809],
[0.9518, 0.0000, -0.309], [0.9510, 0.0000, 0.3090],
[0.5876, 0.0000, 0.8090], [0.0000, 0.0000, 1.0000],
[-0.588, 0.0000, 0.8090], [-0.951, 0.0000, 0.3090],
[-0.955, 0.0000, -0.309], [-0.587, 0.0000, -0.809],
[0.0000, 0.0000, -1.000], [0.5877, 0.0000, -0.809],
[0.6889, 0.5257, 0.4999], [-0.262, 0.5257, 0.8090],
[-0.854, 0.5257, 0.0000], [-0.262, 0.5257, -0.809],
[0.6889, 0.5257, -0.499], [0.5257, 0.8506, 0.0000],
[0.1626, 0.8506, 0.4999], [-0.425, 0.8506, 0.3090],
[-0.422, 0.8506, -0.309], [0.1624, 0.8506, -0.499]])
f = np.array(
[[15, 3, 13], [13, 14, 15], [2, 15, 14], [13, 1, 14], [17, 2, 14],
[14, 16, 17], [6, 17, 16], [14, 1, 16], [19, 4, 18], [18, 13, 19],
[3, 19, 13], [18, 1, 13], [21, 5, 20], [20, 18, 21], [4, 21, 18],
[20, 1, 18], [22, 6, 16], [16, 20, 22], [5, 22, 20], [16, 1, 20],
[24, 2, 17], [17, 23, 24], [11, 24, 23], [23, 17, 6], [26, 3, 15],
[15, 25, 26], [7, 26, 25], [25, 15, 2], [28, 4, 19], [19, 27, 28],
[8, 28, 27], [27, 19, 3], [30, 5, 21], [21, 29, 30], [9, 30, 29],
[29, 21, 4], [32, 6, 22], [22, 31, 32], [10, 32, 31], [31, 22, 5],
[33, 7, 25], [25, 24, 33], [11, 33, 24], [24, 25, 2], [34, 8, 27],
[27, 26, 34], [7, 34, 26], [26, 27, 3], [35, 9, 29], [29, 28, 35],
[8, 35, 28], [28, 29, 4], [36, 10, 31], [31, 30, 36], [9, 36, 30],
[30, 31, 5], [37, 11, 23], [23, 32, 37], [10, 37, 32],
[32, 23, 6], [39, 7, 33], [33, 38, 39], [12, 39, 38],
[38, 33, 11], [40, 8, 34], [34, 39, 40], [12, 40, 39],
[39, 34, 7], [41, 9, 35], [35, 40, 41], [12, 41, 40], [40, 35, 8],
[42, 10, 36], [36, 41, 42], [12, 42, 41], [41, 36, 9],
[38, 11, 37], [37, 42, 38], [12, 38, 42], [42, 37, 10]]) - 1
# return Mesh(v=v * self.radius + self.center, f=f, vc=np.tile(color, (v.shape[0], 1)))
return trimesh.Trimesh(vertices=v * self.radius + self.center,
faces=f,
vertex_colors=np.tile(color, (v.shape[0], 1)))
def flatten_mesh(mesh, _lambda=1.0):
"""Flatten the mesh, return uv coordinates and the mesh in 2D
Parameters
----------
mesh : Trimesh object
must have boundary
_lambda : int <= 1.0
parameter for trading of area-distortion/angle-preservation.
The default is 1.0
Returns
-------
u : array
first coordinate of the paramterization
v : array
second coordinate of the paramterization
mesh2d : Trimesh object with coordinates (u,v,0)
_lambda <= 1.0
_lambda == 1.0 => conformal mapping
_lambda == 0.5 => not conformal but less area distortion
_lambda --> 0 mapping becomes denegerate (real==imag)
_lambda > 1 (e.g. 1.01-1.1) folding effects
"""
vals, uv = eigen_complex_laplacian(mesh, 2, _lambda)
# Coordinates with initial phase
u = uv[:, 1].real
v = uv[:, 1].imag
# Determine "phase" by matching the uv coordinate function with mesh coordinates
theta = np.linspace(0, 2 * np.pi, 50)
yy = np.imag(np.exp(1j * theta)[:, None] * uv[:, 1])
# plt.plot(np.sum(mesh.vertices[:,0]*xx, axis=1))
ii = np.argmax(np.sum(mesh.vertices[:, 1] * yy, axis=1))
theta = theta[ii]
u = np.real(np.exp(1j * theta) * uv[:, 1])
v = np.imag(np.exp(1j * theta) * uv[:, 1])
mesh2d = trimesh.Trimesh(np.array([u, v, 0 * u]).T,
mesh.faces,
process=False)
return u, v, mesh2d
def _generate_gel_trimesh(self):
# Load config
g = self.conf.sensor.gel
origin = g.origin
X0, Y0, Z0 = origin[0], origin[1], origin[2]
W, H = g.width, g.height
if g.mesh is not None:
gel_trimesh = trimesh.load(g.mesh)
# scale up for clearer indentation
matrix = np.eye(4)
matrix[[0, 1, 2], [0, 1, 2]] = 1.02
gel_trimesh = gel_trimesh.apply_transform(matrix)
elif not g.curvature:
# Flat gel surface
gel_trimesh = trimesh.Trimesh(
vertices=[
[X0, Y0 + W / 2, Z0 + H / 2],
[X0, Y0 + W / 2, Z0 - H / 2],
[X0, Y0 - W / 2, Z0 - H / 2],
[X0, Y0 - W / 2, Z0 + H / 2],
],
faces=[[0, 1, 2], [2, 3, 0]],
)
else:
# Curved gel surface
N = g.countW
M = int(N * H / W)
R = g.R
zrange = g.curvatureMax
y = np.linspace(Y0 - W / 2, Y0 + W / 2, N)
z = np.linspace(Z0 - H / 2, Z0 + H / 2, M)
yy, zz = np.meshgrid(y, z)
h = R - np.maximum(0, R**2 - (yy - Y0)**2 - (zz - Z0)**2)**0.5
xx = X0 - zrange * h / h.max()
gel_trimesh = self._generate_trimesh_from_depth(xx)
return gel_trimesh
def createMesh(size, n, omino_set):
ominos = generateOminos(n)
vertices = []
triangles = []
baseTriangles = [(i,(i+1)%4,(i+1)%4+4) for i in range(4)]+[(i,(i+1)%4+4,i+4) for i in range(4)]+[(5,6,7),(5,7,4)]
for i in range(len(omino_set)*5):
for triangle in baseTriangles:
triangles.append((triangle[0]+8*i,triangle[1]+8*i,triangle[2]+8*i))
sideLength = 7
bevelWidth = .2
bevelHeight = .2
for ominoIndex, coord in omino_set:
omino = list(ominos)[ominoIndex]
for tile in omino:
bevelSides = []
for x in [-1,0,1]:
for y in [y for y in [-1,0,1] if abs(x)+abs(y)==1]:
if not (tile[0]+x,tile[1]+y) in omino:
bevelSides.append((x,y))
for x,y in [(0,0),(0,1),(1,1),(1,0)]:
vertices.append((x+coord[0]+tile[0],y+coord[1]+tile[1],0))
for x,y in [(0,0),(0,1),(1,1),(1,0)]:
xOffset = 0
yOffset = 0
if x == 0:
if (-1,0) in bevelSides:
xOffset = bevelWidth
elif (1,0) in bevelSides:
xOffset = -bevelWidth
if y == 0:
if (0,-1) in bevelSides:
yOffset = bevelWidth
elif (0,1) in bevelSides:
yOffset = -bevelWidth
if xOffset == 0 and yOffset == 0:
if not (tile[0]+[-1,1][x],tile[1]+[-1,1][y]) in omino:
xOffset = [1,-1][x]*bevelWidth
yOffset = [1,-1][y]*bevelWidth
vertices.append((x+xOffset+coord[0]+tile[0],y+yOffset+coord[1]+tile[1],bevelHeight))
for i,vertex in enumerate(vertices):
vertices[i]=[vertex[0]*sideLength,vertex[1]*sideLength,vertex[2]]
mesh = trimesh.Trimesh(vertices=vertices,faces=triangles)
mesh.export("test.stl")
def mesh_from_logits(self, logits):
logits = np.reshape(logits, (self.resolution,) * 3)
# padding to ba able to retrieve object close to bounding box bondary
logits = np.pad(logits, ((1, 1), (1, 1), (1, 1)), 'constant', constant_values=0)
threshold = np.log(self.threshold) - np.log(1. - self.threshold)
vertices, triangles = mcubes.marching_cubes(
logits, threshold)
# remove translation due to padding
vertices -= 1
# rescale to original scale
step = (self.max - self.min) / (self.resolution - 1)
vertices = np.multiply(vertices, step)
vertices += [self.min, self.min, self.min]
return trimesh.Trimesh(vertices, triangles)
def vis_all(self, split, shuffle=False, learning_phase=0):
import matplotlib.pyplot as plt
tf.keras.backend.set_learning_phase(learning_phase)
dataset = self.problem.get_base_dataset(split)
if shuffle:
dataset = dataset.shuffle(self.problem.shuffle_buffer)
for image, label in dataset:
label = label.numpy()
vertices, faces, cloud = self(image)
mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
scene = mesh.scene()
scene.add_geometry(point_cloud(cloud, [0, 0, 255]))
scene.add_geometry(point_cloud(label, [0, 255, 0]))
image = image.numpy()
image -= np.min(image)
image /= np.max(image)
plt.imshow(image)
scene.show(background=(0, 0, 0, 0))
def generate_env(obj_index, out_path):
# step 0: read file
obj_index = str(obj_index).zfill(3)
mesh = trimesh.load(os.path.join(obj_index, obj_index + '_coll.obj'))
# step 1: create convex mesh
vertices_lst = []
for m in mesh:
vertices_lst.append(m.vertices)
vertices_lst = np.vstack(vertices_lst)
convex_mesh = trimesh.Trimesh(vertices=vertices_lst).convex_hull
# step 2: write convex hull as stl file
convex_mesh.export(os.path.join(obj_index, obj_index + '_coll.stl'))
# step 3: record center of mass and box size
convex_com = convex_mesh.center_mass
half_length = convex_mesh.bounding_box_oriented.primitive.extents
def as_mesh(scene_or_mesh):
"""
Convert a possible scene to a mesh.
If conversion occurs, the returned mesh has only vertex and face data.
"""
if isinstance(scene_or_mesh, trimesh.Scene):
if len(scene_or_mesh.geometry) == 0:
mesh = None # empty scene
else:
# we lose texture information here
mesh = trimesh.util.concatenate(
tuple(
trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
for g in scene_or_mesh.geometry.values()))
else:
# assert(isinstance(mesh, trimesh.Trimesh))
mesh = scene_or_mesh
return mesh
def generate_tetrahedral_hollowed_mesh(self, mesh, density, grid=None, thresh=0.25):
# density in points per square unit,
# Does the whole generation process. If grid is None, generates a random one
extents = mesh.bounding_box.extents
center = mesh.bounds.mean(axis=0)
if grid is not None:
self.set_grid(grid, thresh, extents, density, center)
else:
self.generate_random(thresh, extents, density, center)
self.clear_close_points(mesh, thresh)
self.march()
hollow = trimesh.Trimesh(vertices=self.vertices, faces=self.tris)
verts, tets = generate_dual_tetrahedrons(mesh, hollow)
new_volume = mesh.volume - hollow.volume
return verts, tets, new_volume
def to_mesh(self, color=colors['red']):
v = np.array([[-1., -1., -1.], [-1., -1., 1.], [-1., 1., 1.],
[-1., 1., -1.], [-1., 1., -1.], [-1., 1., 1.],
[1., 1., 1.], [1., 1., -1.], [1., 1., -1.], [1., 1., 1.],
[1., -1., 1.], [1., -1., -1.], [-1., -1., 1.],
[-1., -1., -1.], [1., -1., -1.], [1., -1., 1.],
[1., -1., -1.], [-1., -1., -1.], [-1., 1., -1.],
[1., 1., -1.], [1., 1., 1.], [-1., 1., 1.],
[-1., -1., 1.], [1., -1., 1.]])
f = np.array([[0, 1, 2], [0, 2, 3], [4, 5, 6], [4, 6, 7], [8, 9, 10],
[8, 10, 11], [12, 13, 14], [12, 14, 15], [16, 17, 18],
[16, 18, 19], [20, 21, 22], [20, 22, 23]])
# return Mesh(v=v * self.radius + self.center, f=f, point_color=np.tile(color, (v.shape[0], 1)))
return trimesh.Trimesh(vertices=v * self.radius + self.center,
faces=f,
vertex_colors=np.tile(color, (v.shape[0], 1)))
def color_mesh(mesh, mesh_col):
# mesh_color = trimesh.visual.ColorVisuals(mesh, vertex_colors=color)
# # mesh.visual = mesh_colore
# mesh = trimesh.Trimesh(mesh.vertices, mesh.faces, visuals=mesh_color)
# breakpoint()
nverts = len(mesh.vertices)
if len(mesh_col.shape) == 1:
vertex_colors = mesh_col[None, ] * np.ones((nverts, 1))
else:
vertex_colors = mesh_col * 1
trimesh.visual.color.ColorVisuals(mesh, vertex_colors=vertex_colors)
mesh = trimesh.Trimesh(mesh.vertices,
mesh.faces,
vertex_colors=vertex_colors)
return mesh, vertex_colors
def o3d2trimesh(mesh):
'''
Takes o3d mesh and converts it to trimesh object
'''
vertices = np.asarray(mesh.vertices)
faces = np.asarray(mesh.triangles)
face_normals = np.asarray(mesh.triangle_normals)
vertex_normals = np.asarray(mesh.vertex_normals)
vertex_colors = np.asarray(mesh.vertex_colors)
print(len(vertices), len(faces), len(face_normals), len(face_normals))
mesh = trimesh.Trimesh(vertices=vertices,
faces=faces,
face_normals=face_normals,
vertex_normals=vertex_normals,
vertex_colors=vertex_colors)
#print(mesh)
return mesh
def mesh_to_grid(mesh, pitch):
# gridscale: ((min, max), (min, max), (min, max)) where each point i becomes i/i_size*(max-min) + min
#scale mesh properly to match grid
#iterate through all points in grid centers!
# if point is inside the mesh, set grid corners to 1, 0 otherwise
true_faces = []
for i in range(1, len(mesh.faces), 4):
true_faces.append(
[mesh.faces[i], mesh.faces[i + 1], mesh.faces[i + 2]])
tri_version = trimesh.Trimesh(mesh.points, true_faces)
# TODO, pick the smallest cell size
voxels = tri_version.voxelized(pitch)
voxels.fill()
mat = voxels.encoding.dense
return np.array(mat, dtype=np.int)
def load_mesh(gii_file):
"""
load gifti_file and create a trimesh object
:param gifti_file: str, path to the gifti file
:return: the corresponding trimesh object
"""
g = nib.gifti.read(gii_file)
vertices, faces = g.getArraysFromIntent(
nib.nifti1.intent_codes['NIFTI_INTENT_POINTSET'])[0].data, \
g.getArraysFromIntent(
nib.nifti1.intent_codes['NIFTI_INTENT_TRIANGLE'])[0].data
metadata = g.get_meta().metadata
metadata['filename'] = gii_file
return trimesh.Trimesh(faces=faces,
vertices=vertices,
metadata=metadata,
process=False)
def export_ply(self, ply_path):
vertices = self.dims["shape"]["vals"]
# this brings them into about the [-15, +15] range
vertices /= 10_000
faces = self._tl - 1
colours = self.dims["tex"]["vals"]
mesh = trimesh.Trimesh(vertices=vertices,
faces=faces,
vertex_colors=colours)
mesh.invert()
mesh.export(file_obj=ply_path)
def clean(input_mesh):
"""
This function remove faces, and vertex that doesn't belong to any face. Intended to be used before a feed forward pass in pointNet
Input : mesh
output : cleaned mesh
"""
print("cleaning ...")
print("number of point before : ", np.shape(input_mesh.vertices)[0])
pts = input_mesh.vertices
faces = input_mesh.faces
faces = faces.reshape(-1)
unique_points_index = np.unique(faces)
unique_points = pts[unique_points_index]
print("number of point after : ", np.shape(unique_points)[0])
mesh = trimesh.Trimesh(vertices=unique_points,
faces=np.array([[0, 0, 0]]),
process=False)
return mesh