def visualize_corres(points, correspondences, model):
points += np.array([1., 0., 0.]).reshape((1, 3))
tree = NN(n_neighbors=1000).fit(points)
for idx in [88190, 132644]: #range(100):
#idx = np.random.randint(points.shape[0]-1)
print('visualizing around point %d' % idx)
dists, indices = tree.kneighbors(points[idx, :].reshape((1, 3)))
neighbors = indices[0, :]
source = points[neighbors, :]
#import ipdb; ipdb.set_trace()
target = model.verts[correspondences[neighbors], :]
edges = vis.getEdges(source, target, 1000, color=np.array([1., 0, 0]))
o3d.draw_geometries([
vis.getPointCloud(points),
vis.getTriangleMesh(model.verts, model.faces), edges
])
def visualize_smoothness(points, correspondences, model):
points += np.array([1., 0., 0.]).reshape((1, 3))
tree = NN(n_neighbors=10).fit(points)
dists, indices = tree.kneighbors(points)
corres = correspondences[indices] # [n_points, 10]
target_points = model.verts[corres, :] # [n_points, 10, 3]
mean = target_points.mean(axis=1) # [n_points, 3]
dists = target_points - mean[:, np.newaxis, :] # [n_points, 10, 3]
dists = np.square(dists).sum(axis=1).sum(axis=1) # [n_points]
max_dist = dists.max()
min_dist = dists.min()
dists = (dists - min_dist) / (max_dist - min_dist)
r = np.array([1.0, 0, 0])
b = np.array([0, 0, 1.])
colors = np.array([(r * (di) + b * (1.0 - di)) for di in dists])
pcd = vis.getPointCloud(points)
pcd.colors = o3d.utility.Vector3dVector(colors)
o3d.draw_geometries([pcd, vis.getTriangleMesh(model.verts, model.faces)])
extrinsics = []
gt_feats = []
gt_correspondences = []
current = 0
used_rotations = []
for i, rotation in enumerate(rotations):
transformation = np.eye(4)
transformation[:3, :3] = rotation
mesh.transform(transformation)
depth_image, extrinsic, intrinsic, points3d_i, correspondence, valid_idx = camera.project(
mesh.vertices, mesh.triangles)
mesh.transform(transformation.T)
depth = depth_image[(valid_idx[:, 0], valid_idx[:, 1])]
helper.save_to_obj(OBJ.format(scan_id, i),
vis.getPointCloud(points3d_i))
gt_correspondence = smpl_corres[correspondence]
np.savetxt(CORRES.format(scan_id, i), gt_correspondence, '%d')
mat_file = MAT.format(scan_id, i)
print('saving to %s' % mat_file)
sio.savemat(mat_file, {
'depth': depth,
'points3d': points3d_i,
'valid_pixel_indices': valid_idx,
'correspondence': gt_correspondence,
'mesh_correspondence': correspondence,
'rotation': rotation,
'width': 320,
'height': 240,
'intrinsic': intrinsic,
'extrinsic': extrinsic,