def test_downsample_mesh_voxel_grid(self):
import point_cloud_utils as pcu
import numpy as np
# v is a nv by 3 NumPy array of vertices
v = pcu.load_mesh_v(
os.path.join(self.test_path, "duplicated_pcloud.ply"))
bbmin, bbmax = v.min(0), v.max(0)
bbsize = bbmax - bbmin
vdown, _, _ = pcu.downsample_point_cloud_voxel_grid(bbsize / 128.0, v)
self.assertLess(vdown.shape[0], v.shape[0])
def test_downsample_point_cloud_voxel_grid(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"))
bbox = np.max(v, axis=0) - np.min(v, axis=0)
bbox_diag = np.linalg.norm(bbox)
vox_grid_size = 1.0 / 128.0
# Make sure we have normals
self.assertEqual(n.shape, v.shape)
# Vanilla case
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v)
self.assertIsNone(nms)
self.assertIsNone(clr)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With normals
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n)
self.assertIsNone(clr)
self.assertEqual(nms.shape, pts.shape)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With RBG colors
c = np.random.rand(v.shape[0], 3)
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, None, c)
self.assertIsNone(nms)
self.assertEqual(clr.shape, pts.shape)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With RBGA colors
c = np.random.rand(v.shape[0], 4)
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, None, c)
self.assertIsNone(nms)
self.assertEqual(clr.shape[0], pts.shape[0])
self.assertEqual(clr.shape[1], 4)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With normals and RGB colors
c = np.random.rand(v.shape[0], 3)
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
self.assertEqual(nms.shape, pts.shape)
self.assertEqual(clr.shape, pts.shape)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With normals and RBGA colors
c = np.random.rand(v.shape[0], 4)
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
self.assertEqual(nms.shape, pts.shape)
self.assertEqual(clr.shape[0], pts.shape[0])
self.assertEqual(clr.shape[1], 4)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With different voxel size per axis
vox_grid_size = [1.0/128.0, 1.0/99.0, 1.0/222.0]
c = np.random.rand(v.shape[0], 4)
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
self.assertEqual(nms.shape, pts.shape)
self.assertEqual(clr.shape[0], pts.shape[0])
self.assertEqual(clr.shape[1], 4)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# With bounding box dimensions
vox_grid_size = np.array([1.0/128.0, 1.0/99.0, 1.0/222.0])
min_bound = np.min(v, axis=0) - 0.5 * np.array(vox_grid_size)
max_bound = np.max(v, axis=0) + 0.5 * np.array(vox_grid_size)
c = np.random.rand(v.shape[0], 4)
pts, nms, clr = pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c,
min_bound=min_bound, max_bound=max_bound)
self.assertEqual(nms.shape, pts.shape)
self.assertEqual(clr.shape[0], pts.shape[0])
self.assertEqual(clr.shape[1], 4)
self.assertGreater(pts.shape[0], 0)
self.assertEqual(pts.shape[1], 3)
# Should raise if the voxel size is too small
with self.assertRaises(ValueError):
vox_grid_size = [1e-16, 1.0/99.0, 1.0/222.0]
c = np.random.rand(v.shape[0], 4)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
# Should raise if the voxel size is negative
with self.assertRaises(ValueError):
vox_grid_size = [1.0/100.0, -1.0/99.0, 1.0/222.0]
c = np.random.rand(v.shape[0], 4)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
# Invalid color dimension
with self.assertRaises(ValueError):
c = np.random.rand(v.shape[0], 2)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
# Invalid normal dimension
with self.assertRaises(ValueError):
c = np.random.rand(v.shape[0], 2)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n[:, :1], c)
# Invalid number of normals
with self.assertRaises(ValueError):
c = np.random.rand(v.shape[0], 3)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n[1:, :], c)
# Invalid number of colors
with self.assertRaises(ValueError):
c = np.random.rand(v.shape[0]//2, 3)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c)
# Negative bounding box
with self.assertRaises(ValueError):
min_bound = np.min(v, axis=0) - 0.5 * np.array(vox_grid_size)
max_bound = np.max(v, axis=0) + 0.5 * np.array(vox_grid_size)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c, max_bound=min_bound, min_bound=max_bound)
# Badly shaped grid size
with self.assertRaises(ValueError):
vox_grid_size = [1.0/100.0, 1.0/99.0]
min_bound = np.min(v, axis=0) - 0.5 * np.array(vox_grid_size)
max_bound = np.max(v, axis=0) + 0.5 * np.array(vox_grid_size)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c, max_bound=max_bound, min_bound=min_bound)
# Badly shaped max bound
with self.assertRaises(ValueError):
vox_grid_size = [1.0/100.0, 1.0/99.0, 1.0/77.0]
min_bound = np.min(v, axis=0) - 0.5 * np.array(vox_grid_size)
max_bound = np.max(v, axis=0) + 0.5 * np.array(vox_grid_size)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c, max_bound=max_bound[:1], min_bound=min_bound)
# Badly shaped max bound
with self.assertRaises(ValueError):
vox_grid_size = [1.0/100.0, 1.0/99.0, 1.0/77.0]
min_bound = np.min(v, axis=0) - 0.5 * np.array(vox_grid_size)
max_bound = np.max(v, axis=0) + 0.5 * np.array(vox_grid_size)
pcu.downsample_point_cloud_voxel_grid(vox_grid_size, v, n, c, max_bound=max_bound[:1], min_bound=(1.0, 1.0))