def test_morton_coding_big_data(self):
import point_cloud_utils as pcu
import numpy as np
import os
if os.name == 'nt':
num_pts = 10000
num_qpts = 100
else:
num_pts = 1000000
num_qpts = 10000
div = 1.0 / 1000.0
pts = np.random.rand(num_pts, 3) / div
pts_int = pts.astype(np.int32)
qpts = np.random.rand(num_qpts, 3) / div
qpts_int = qpts.astype(np.int32)
codes = pcu.morton_encode(pts_int)
codes_sort_idx = np.argsort(codes)
codes_sorted = codes[codes_sort_idx]
qcodes = pcu.morton_encode(qpts_int)
nn_idx = pcu.morton_knn(codes_sorted, qcodes, 7)
codes_sorted[nn_idx]
self.assertEqual(nn_idx.shape, (num_qpts, 7))
def test_morton_coding_tiny_data(self):
import point_cloud_utils as pcu
import numpy as np
div = 1.0 / 1000.0
pts = np.random.rand(10, 3) / div
pts_int = pts.astype(np.int32)
qpts = np.random.rand(10000, 3) / div
qpts_int = qpts.astype(np.int32)
codes = pcu.morton_encode(pts_int)
codes_sort_idx = np.argsort(codes)
codes_sorted = codes[codes_sort_idx]
qcodes = pcu.morton_encode(qpts_int)
nn_idx = pcu.morton_knn(codes_sorted, qcodes, 15)
codes_sorted[nn_idx]
self.assertEqual(nn_idx.shape, (10000, 10))
import numpy as np from mayavi import mlab from scipy.spatial import cKDTree k = 50 div = 1.0 / 1000.0 pts = (np.random.rand(10_000, 3) / div).astype(np.int32) qpts = (np.random.rand(100, 3) / div).astype(np.int32) pts[:, 2] = 0.0 qpts[:, 2] = 0.0 codes = pcu.morton_encode(pts) codes_sorted_idx = np.argsort(codes) codes_sorted = codes[codes_sorted_idx] qcodes = pcu.morton_encode(qpts) nn_idx = pcu.morton_knn(codes_sorted, qcodes, k) nn_pts = pcu.morton_decode(codes_sorted[nn_idx[0]]) kdt = cKDTree(pts) _, nn_gt_idx = kdt.query(np.array([qpts[0]]), k=k) print(nn_gt_idx.shape) nn_gt_pts = pts[nn_gt_idx[0]] mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], scale_factor=5.0) mlab.points3d([qpts[0, 0]], [qpts[0, 1]], [qpts[0, 2]], scale_factor=7.0, color=(1.0, 0.0, 0.0)) mlab.points3d(nn_pts[:, 0], nn_pts[:, 1], nn_pts[:, 2], scale_factor=9.0, color=(0.0, 0.0, 1.0), opacity=0.5) mlab.points3d(nn_gt_pts[:, 0], nn_gt_pts[:, 1], nn_gt_pts[:, 2], scale_factor=9.0, color=(0.0, 1.0, 0.0), opacity=0.5) mlab.show()
qpts = np.random.rand(10_000, 3) / div
qpts_int = qpts.astype(np.int32)
init_time = time.time()
codes = pcu.morton_encode(pts_int, sort=False)
codes_time = time.time()
idxs = np.argsort(codes)
sort_time1 = time.time()
codes = codes[idxs]
sort_time2 = time.time()
qcodes = pcu.morton_encode(qpts_int)
qcodes_time = time.time()
nn_idx = pcu.morton_knn(codes, qcodes, k=20)
knn_time = time.time()
print(nn_idx.shape)
print("Timing results: ")
print(" Init time %f ms" % (1000 * (init_time - start_time)))
print(" Codes time %f ms" % (1000 * (codes_time - init_time)))
print(" Sort time 1 %f ms" % (1000 * (sort_time1 - codes_time)))
print(" Sort time 2 %f ms" % (1000 * (sort_time2 - sort_time1)))
print(" QCodes time %f ms" % (1000 * (qcodes_time - sort_time2)))
print(" KNN time %f ms" % (1000 * (knn_time - qcodes_time)))
# Lots of data
div = 1.0 / 1000.0
pts = np.random.rand(1_000_000, 3) / div
pts_int = pts.astype(np.int32)
qpts = np.random.rand(10_000, 3) / div
qpts_int = qpts.astype(np.int32)
codes = pcu.morton_encode(pts_int)
codes_sort_idx = np.argsort(codes)
codes_sorted = codes[codes_sort_idx]
qcodes = pcu.morton_encode(qpts_int)
nn_idx = pcu.morton_knn(codes_sorted, qcodes, 7)
codes_sorted[nn_idx]
print("0 --------------------------------")
print(nn_idx.shape)
print(nn_idx)
print("\n")
# 1nn
div = 1.0 / 1000.0
pts = np.random.rand(1_000_000, 3) / div
pts_int = pts.astype(np.int32)
qpts = np.random.rand(10_000, 3) / div
qpts_int = qpts.astype(np.int32)
codes = pcu.morton_encode(pts_int)
