def test_filter():
'''
Test Voxel Grid Filter functionality
'''
side = 10
delta = 0.05
offset = [-5, -5, 0]
points, footprint = make_tri_pyramid_with_base(side, delta, offset)
pc = pcl.PointCloudXYZRGB(points.astype(np.float32))
# Build Voxel Grid Filter
vgf = pc.make_voxel_grid_filter()
# Filter with Voxel size 1
vgf.set_leaf_size(1, 1, 1)
pc2 = vgf.filter()
# Filter with Voxel size 0.1
vgf.set_leaf_size(0.1, 0.1, 0.1)
pc3 = vgf.filter()
# Filter with Voxel size 10
vgf.set_leaf_size(10, 10, 10)
pc4 = vgf.filter()
assert_greater(len(pc), len(pc2))
assert_greater(len(pc3), len(pc2))
assert_greater(len(pc), len(pc4))
def setUp(self):
self.useLocal = False
if self.useLocal:
self.tempdir = tempdir = '.'
else:
self.tempdir = tempdir = mkdtemp(prefix='patty-analytics')
self.drivemapLas = os.path.join(tempdir, 'testDriveMap.las')
self.sourcelas = os.path.join(tempdir, 'testSource.las')
self.footprint_csv = os.path.join(tempdir, 'testFootprint.csv')
self.foutlas = os.path.join(tempdir, 'testOutput.las')
self.min = -10
self.max = 10
self.num_rows = 1000
# Create plane with a pyramid
dm_pct = 0.5
dm_rows = np.round(self.num_rows * dm_pct)
dm_min = self.min * dm_pct
dm_max = self.max * dm_pct
delta = dm_max / dm_rows
shape_side = dm_max - dm_min
dm_offset = [0, 0, 0]
self.dense_obj_offset = [3, 2, -(1 + shape_side / 2)]
# make drivemap
plane_row = np.linspace(start=self.min,
stop=self.max,
num=self.num_rows)
plane_points = cartesian((plane_row, plane_row, [0]))
shape_points, footprint = make_tri_pyramid_with_base(
shape_side, delta, dm_offset)
np.savetxt(self.footprint_csv, footprint, fmt='%.3f', delimiter=',')
dm_points = np.vstack([plane_points, shape_points])
plane_grid = np.zeros((dm_points.shape[0], 6), dtype=np.float32)
plane_grid[:, 0:3] = dm_points
self.drivemap_pc = pcl.PointCloudXYZRGB(plane_grid)
self.drivemap_pc = downsample_voxel(self.drivemap_pc,
voxel_size=delta * 20)
# utils.set_registration(self.drivemap_pc)
utils.save(self.drivemap_pc, self.drivemapLas)
# Create a simple pyramid
dense_grid = np.zeros((shape_points.shape[0], 6), dtype=np.float32)
dense_grid[:, 0:3] = shape_points + self.dense_obj_offset
self.source_pc = pcl.PointCloudXYZRGB(dense_grid)
self.source_pc = downsample_voxel(self.source_pc, voxel_size=delta * 5)
utils.save(self.source_pc, self.sourcelas)
def setUp(self):
self.useLocal = False
if self.useLocal:
self.tempdir = tempdir = '.'
else:
self.tempdir = tempdir = mkdtemp(prefix='patty-analytics')
self.drivemapLas = os.path.join(tempdir, 'testDriveMap.las')
self.sourcelas = os.path.join(tempdir, 'testSource.las')
self.footprint_csv = os.path.join(tempdir, 'testFootprint.csv')
self.foutlas = os.path.join(tempdir, 'testOutput.las')
self.min = -10
self.max = 10
self.num_rows = 1000
# Create plane with a pyramid
dm_pct = 0.5
dm_rows = np.round(self.num_rows * dm_pct)
dm_min = self.min * dm_pct
dm_max = self.max * dm_pct
delta = dm_max / dm_rows
shape_side = dm_max - dm_min
dm_offset = [0, 0, 0]
self.dense_obj_offset = [3, 2, -(1 + shape_side / 2)]
# make drivemap
plane_row = np.linspace(
start=self.min, stop=self.max, num=self.num_rows)
plane_points = cartesian((plane_row, plane_row, [0]))
shape_points, footprint = make_tri_pyramid_with_base(
shape_side, delta, dm_offset)
np.savetxt(self.footprint_csv, footprint, fmt='%.3f', delimiter=',')
dm_points = np.vstack([plane_points, shape_points])
plane_grid = np.zeros((dm_points.shape[0], 6), dtype=np.float32)
plane_grid[:, 0:3] = dm_points
self.drivemap_pc = pcl.PointCloudXYZRGB(plane_grid)
self.drivemap_pc = downsample_voxel(self.drivemap_pc,
voxel_size=delta * 20)
# utils.set_registration(self.drivemap_pc)
utils.save(self.drivemap_pc, self.drivemapLas)
# Create a simple pyramid
dense_grid = np.zeros((shape_points.shape[0], 6), dtype=np.float32)
dense_grid[:, 0:3] = shape_points + self.dense_obj_offset
self.source_pc = pcl.PointCloudXYZRGB(dense_grid)
self.source_pc = downsample_voxel(self.source_pc, voxel_size=delta * 5)
utils.save(self.source_pc, self.sourcelas)
