def test(self):
"""tests model
returns: number correct and total number
"""
with torch.no_grad():
out = self.forward()
dist1, dist2, _, _ = self.criterion(self.labels, out)
test_loss = 0.5 * (dist1.mean() + dist2.mean())
labels = torch.Tensor.cpu(self.labels).numpy()
out = torch.Tensor.cpu(out).numpy()
for i in range(out.shape[0]) :
normals = pcu.estimate_normals(out[i], k=16)
#print(n.shape, n)
fs, vs = poisson_reconstruction(out[i], normals, depth=5, full_depth=3)
print(vs.shape, fs.shape)
filename, file_extension = os.path.splitext(self.filename[i])
file = '%s/%s_%s%s' % (self.export_folder[i], filename, 'out', file_extension)
print(file)
self.output_export(vs, fs, file)
#print(labels.shape, out.shape)
#for i in range(len(labels)) :
# mean_error, R, indices = icp(labels[i], out[i], tolerance=0.0001)
# print(out[i].shape, out[i])
# print(indices.shape, np.unique(indices).shape, indices, np.unique(indices))
#print(out[indices[:len(indices)-self.pad_iter]].shape, self.init_faces)
#self.output_export(vertices, faces)
print(test_loss, len(self.labels))
return test_loss, len(self.labels), out, self.labels
def upsample_surface(patch_uvs, patch_tx, patch_models, devices, scale=1.0, num_samples=8, normal_samples=64,
compute_normals=True):
vertices = []
normals = []
with torch.no_grad():
for i in range(len(patch_models)):
if (i + 1) % 10 == 0:
print("Upsamling %d/%d" % (i+1, len(patch_models)))
device = devices[i % len(devices)]
n = num_samples
translate_i, scale_i, rotate_i = (patch_tx[i][j].to(device) for j in range(len(patch_tx[i])))
uv_i = utils.meshgrid_from_lloyd_ts(patch_uvs[i].cpu().numpy(), n, scale=scale).astype(np.float32)
uv_i = torch.from_numpy(uv_i).to(patch_uvs[i])
y_i = patch_models[i](uv_i)
mesh_v = ((y_i.squeeze() @ rotate_i.transpose(0, 1)) / scale_i - translate_i).cpu().numpy()
if compute_normals:
mesh_f = utils.meshgrid_face_indices(n)
mesh_n = pcu.per_vertex_normals(mesh_v, mesh_f)
normals.append(mesh_n)
vertices.append(mesh_v)
vertices = np.concatenate(vertices, axis=0).astype(np.float32)
if compute_normals:
normals = np.concatenate(normals, axis=0).astype(np.float32)
else:
print("Fixing normals...")
normals = pcu.estimate_normals(vertices, k=normal_samples)
return vertices, normals
def test_estimate_normals(self):
import point_cloud_utils as pcu
import numpy as np
# v is a nv by 3 NumPy array of vertices
# f is an nf by 3 NumPy array of face indexes into v
# n is a nv by 3 NumPy array of vertex normals if they are specified, otherwise an empty array
v, f, n = pcu.read_obj(os.path.join(self.test_path, "cube_twist.obj"))
# Estimate normals for the point set, v using 12 nearest neighbors per point
n = pcu.estimate_normals(v, k=12)
self.assertEqual(n.shape, v.shape)
def datalist_generation(fn, nb_samples, sqrt_nb, max_nb, normals):
datalist = []
for d in range(nb_samples):
points = fn(sqrt_nb)
perm = torch.randperm(len(points))
points = torch.tensor(points)[perm]
points = points[:max_nb]
if normals:
normal = torch.tensor(
pcu.estimate_normals(np.array(points), k=16))
datalist.append(Data(pos=points, norm=normal[:max_nb]))
else:
datalist.append(Data(pos=points))
return datalist
def main():
with open('../Models/Cloud_ToyScrew-Yellow.json') as fin:
cloud = []
screwCloud = np.array(json.load(fin))
for p in screwCloud:
if not np.any(np.isnan(np.array(p))):
#if not np.any(np.isnan(np.array(p))) and np.linalg.norm(np.array(p) - np.array((0, 0, 0.4))) < 0.3:
cloud.append(p)
cloud = np.array(cloud)
fullCloud = cloud # [np.random.choice(range(len(cloud)), len(cloud))]
cloudNormals = pcu.estimate_normals(fullCloud,
k=10,
smoothing_iterations=3)
mask = ModelFinder.voxelFilter(fullCloud, size=0.005)
cloud, cloudNormals = fullCloud[mask], cloudNormals[mask]
#cloud, cloudNormals = ModelFinder.meanPlanarCloudSampling(fullCloud, cloudNormals, 0.01, 0.2, 0.005)
flipNormals(cloudNormals)
fig = plt.figure()
ax = fig.gca(projection='3d')
Q = Queue()
meshPoints = ax.scatter([], [], [], color='red')
ani = FuncAnimation(fig,
functools.partial(showHypotheses, ax, cloud, Q),
range(1),
repeat_delay=1000)
ax.scatter(cloud[:, 0], cloud[:, 1], cloud[:, 2], color='blue')
ax.quiver(cloud[:, 0],
cloud[:, 1],
cloud[:, 2],
cloudNormals[:, 0] * 0.01,
cloudNormals[:, 1] * 0.01,
cloudNormals[:, 2] * 0.01,
color='blue')
process = Process(target=findHypotheses, args=(Q, cloud, cloudNormals))
process.start()
ax.set_xlim3d(-.05, 0.05)
ax.set_ylim3d(-.05, 0.05)
ax.set_zlim3d(-0.05, 0.05)
plt.show()
process.terminate()
def main():
with open('Test_Scenes/Cloud_sphere.json') as fin:
cloud = []
screwCloud = np.array(json.load(fin))
for p in screwCloud:
if not np.any(np.isnan(np.array(p))):
# if not np.any(np.isnan(np.array(p))) and np.linalg.norm(np.array(p) - np.array((0, 0, 0.4))) < 0.3:
cloud.append(p)
cloud = np.array(cloud)
fullCloud = cloud # [np.random.choice(range(len(cloud)), len(cloud))]
erf = ERF()
cloudNormals = pcu.estimate_normals(fullCloud,
k=10,
smoothing_iterations=10)
mask = ERF.voxelFilter(fullCloud, size=0.005)
cloud, cloudNormals = fullCloud[mask], cloudNormals[mask]
flipNormals(cloudNormals)
found_spheres, found_cylinders = erf.findInCloud(cloud, cloudNormals)
####################
# Plotting Code #
####################
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_xlim3d(-0.5, 0.5)
ax.set_ylim3d(-0.5, 0.5)
ax.set_zlim3d(-0.5, 0.5)
ax.scatter(cloud[:, 0], cloud[:, 1], cloud[:, 2], color='blue', alpha=0.2)
print("Found ", len(found_spheres), " Spheres!")
print("Found ", len(found_cylinders), " Cylinders!")
# Plot all of the spheres
for ii in range(0, len(found_spheres)):
r = found_spheres[ii][0]
c = found_spheres[ii][1]
u, v = np.mgrid[0:2 * np.pi:20j, 0:np.pi:10j]
x = r * np.cos(u) * np.sin(v) + c[0]
y = r * np.sin(u) * np.sin(v) + c[1]
z = r * np.cos(v) + c[2]
ax.plot_wireframe(x, y, z, color="r")
print(found_cylinders)
# Plot all of the cylinders
for ii in range(0, len(found_cylinders)):
# Plot points at center facing up, then rotate pts by a-vector
r = found_cylinders[ii][0] # radius
c = found_cylinders[ii][1] # centerpoint
a = found_cylinders[ii][
2] # vector of cylinder direction (aka plane direction)
min_z = found_cylinders[ii][3]
max_z = found_cylinders[ii][4]
x = np.linspace(-r, r, 25)
z = np.linspace(min_z, max_z, 5)
xm, zm = np.meshgrid(x, z)
ym = np.sqrt(r**2 - xm**2)
# Rotate based on a-vector
def skew(x):
# return the skew symmetric matrix used for rotating
return np.array([[0, -x[2], x[1]], [x[2], 0, -x[0]],
[-x[1], x[0], 0]])
v = np.cross(np.array([0.0, 0.0, 1.0]), a)
R = np.eye(3) + skew(v) + skew(v) @ skew(v) * 1 / (
1 + np.dot(np.array([0.0, 0.0, 1.0]), a))
# Rotate all of the meshgrid points
rotated_pts = R @ np.stack((xm.flatten(), ym.flatten(), zm.flatten()))
rotated_pts_neg_y = R @ np.stack(
(xm.flatten(), -ym.flatten(), zm.flatten()))
xm = rotated_pts[0, :].reshape((5, 25))
zm = rotated_pts[2, :].reshape((5, 25))
ym1 = rotated_pts[1, :].reshape((5, 25))
ym2 = rotated_pts_neg_y[1, :].reshape((5, 25))
# Shift based on center
xm = xm + c[0]
ym1 = ym1 + c[1]
ym2 = ym2 + c[1]
zm = zm + c[2]
ax.plot_wireframe(xm, ym1, zm, color="g")
ax.plot_wireframe(xm, ym2, zm, color="g")
plt.show()
def __init__(self, traj, top, load=True):
"""
Instantiate the membrane object with loaded trajectory.
:param str traj: Full filepath location to MDTraj-readable trajectory file (e.g., trr, xtc, dcd).
:param str top: Full filepath location to MDTraj-readable topology file (e.g., pdb, gro).
:param bool load: Flag to load trajectory.
"""
self.load = load
self.traj_file = traj
self.top_file = top
self.raw_leaflets = []
self.leaflets = []
self.min_leaflet_size = 10
if self.load:
self.sim = loader.load(self.traj_file, self.top_file)
self.topol = self.sim.topology.to_dataframe()[0]
# Check if simulation loaded
if self.sim is None:
print("ERROR: No simulation data loaded.")
raise FileNotFoundError
# Populate useful one-off lookups
# Construct set for head group filter membership testing
self.hg_set = set(
self.sim.topology.select(
"name " +
" or name ".join([x
for x in particle_naming.headgroup_names])))
# Collect lipid residue indices and lipid particle indices
self.detected_lipids = [
x.index for x in self.sim.topology.residues
if (particle_naming.ion_names.isdisjoint({x.name})
and particle_naming.water_names.isdisjoint({x.name}))
]
logging.debug("Number of lipids detected: %s" %
len(self.detected_lipids))
self.residue_names = sorted(
set([
self.sim.topology.residue(index).name
for index in self.detected_lipids
]))
# Collect all lipid particle indices
self.lipid_particles = self.topol[~np.isin(
self.topol["resName"].values,
list(particle_naming.ion_names) +
list(particle_naming.water_names))].index
# OPTIONAL Collect head group particle indices - not yet sure if this is helpful
# self.hg_particles = self.hg_set.intersection(self.lipid_particles)
# Construct lookups linking lipid residues and particles
self.lipid_particles_by_res = defaultdict(list)
for index in self.topol.index:
self.lipid_particles_by_res[self.sim.topology.atom(
index).residue.index].append(index)
self.lipid_residues_by_particle = {
index: self.sim.topology.atom(index).residue.index
for index in self.topol.index
}
self.hg_particles_by_res = {
resid:
tuple(self.hg_set.intersection(self.lipid_particles_by_res[resid]))
for resid in self.detected_lipids
}
# Pre-calculate all lipid vectors
# NOTE: This requires the original trajectory file to be PBC-corrected, so that all molecules are whole in every frame.
# Use numpy stack to allow indexing using different numbers of head group particles in each lipid
self.hg_centroids = np.stack([
get_centroid_of_particles(self.sim,
self.hg_particles_by_res[resid])
for resid in self.detected_lipids
],
axis=1)
# Use numpy stack to allow indexing using different numbers of particles found in each lipid
self.com_centroids = np.stack([
get_centroid_of_particles(self.sim,
self.lipid_particles_by_res[resid])
for resid in self.detected_lipids
],
axis=1)
self.vectors = self.com_centroids - self.hg_centroids
# Precalculate local neighborhood lipid vector normals
# This is not done in a PBC-aware fashion. It therefore tends to mess up in the corners if too many nearest
# neighbors are selected for the normal estimation, by dragging the vectors towards the box COM.
self.normals = np.asarray(
[pcu.estimate_normals(frame, k=20) for frame in self.hg_centroids])
# The normal detection from the point cloud doesn't correctly estimate the Z-direction. The leaflets are
# assigned using the lipid vectors rather than the normal vectors. Here, the lipid vectors can also be
# used to correct the polarity of the normals.
# Correct "positive" normals - these are the normals which point up from the upper leaflet towards
# +ve Z-direction.
# Where the lipid vectors and normals are negative (i.e., pointing down-Z) make the normals positive (i.e., pointing up-Z)
# Likewise, correct "negative" normals - those pointing down from the lower leaflet towards -ve Z
# Where the lipid vectors and normals are positive (i.e., pointing up-Z) make the normals negative (i.e., pointing down-Z)
pos_inversion = (self.vectors[:, :, 2] < 0) & (self.normals[:, :, 2] <
0)
neg_inversion = (self.vectors[:, :, 2] > 0) & (self.normals[:, :, 2] >
0)
inversion_slice = 1 + (np.logical_or(pos_inversion, neg_inversion) *
-2)
self.normals[:, :, 2] = self.normals[:, :, 2] * inversion_slice
# Detect leaflets using vector alignment
self.detect_leaflets()
# Store lookup for residue leaflet occupancy
self.leaflet_occupancy_by_resid = defaultdict(list)
# Shape: X[resid] = [upper upper lower upper ... ]
for frame_leaflets in self.leaflets:
for resid in self.detected_lipids:
if resid in set(frame_leaflets["upper"]):
self.leaflet_occupancy_by_resid[resid].append("upper")
elif resid in set(frame_leaflets["lower"]):
self.leaflet_occupancy_by_resid[resid].append("lower")
elif resid in set(frame_leaflets["aggregate"]):
self.leaflet_occupancy_by_resid[resid].append("aggregate")
else:
self.leaflet_occupancy_by_resid[resid].append("none")
def set_scene(self, cloud):
self.Scene = cloud
self.SceneNormals = pcu.estimate_normals(cloud, 10, 3)
self.SceneKd = KDTree(cloud)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 25 11:21:05 2020
@author: tamir
"""
import os
import numpy as np
import point_cloud_utils as pcu
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
points = os.path.join(THIS_DIR, "data/point_cloud.obj")
# v is a nv by 3 NumPy array of vertices
v, f, n = pcu.read_obj(points)
# Estimate a normal at each point (row of v) using its 5 nearest neighbors
n = pcu.estimate_normals(v, k=5)
np.testing.assert_allclose(n[0],
np.asarray([0.96283305, 0.11186423, 0.24584327]))
smooth_vtx = fil.filter()
# MLS 平滑
tree = smooth_vtx.make_kdtree()
mls = smooth_vtx.make_moving_least_squares()
mls.set_Compute_Normals(True)
mls.set_polynomial_fit(True)
mls.set_Search_Method(tree)
mls.set_search_radius(10.0)
mls_point = mls.process()
s_vtx_n = np.asarray(mls_point)
print('filter complete')
# 计算法向量
n = pcu.estimate_normals(s_vtx_n, k=16)
# 三角面片重建
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(s_vtx_n)
pcd.normals = o3d.utility.Vector3dVector(n)
# depth越大细节越多
mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
pcd, depth=7)
# 移除Possion重建后额外的平面点
vertices_to_remove = remove(mesh, vtx_middle, reconstruct_shape_l,
reconstruct_shape_r)
mesh.remove_vertices_by_mask(vertices_to_remove)
