def main():
# configuration
db_size = 64000
dim = 3
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
iteration_num = 100
print("octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
db_np = (np.random.rand(db_size, dim) - 0.5) * 100
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = np.random.rand(3)
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print("kdtree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
db_np = np.random.rand(db_size, dim)
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
query = np.random.rand(3)
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
root_dir = './velodyne' # 数据集路径
cat = os.listdir(root_dir)
iteration_num = len(cat)
print("scipy-KDTree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
# print(db_np.shape)
begin_t = time.time()
tree = KDTree(db_np, leafsize=leaf_size)
construction_time_sum += time.time() - begin_t
query = db_np[222, :]
begin_t = time.time()
distance, indices = tree.query(x=query, k=k)
print(distance)
print(indices)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
# print(nn_idx)
print("Octree: build %.3f, knn %.3f, brute %.3f" % (construction_time_sum*1000/iteration_num,
knn_time_sum*1000/iteration_num,
brute_time_sum*1000/iteration_num))
print("octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[222, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
print("result ------------")
print(result_set)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
# print("result ------------")
# print(result_set)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
# print(nn_idx)
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" % (construction_time_sum*1000/iteration_num,
knn_time_sum*1000/iteration_num,
radius_time_sum*1000/iteration_num,
brute_time_sum*1000/iteration_num))
otc_knn = knn_time_sum*1000
otc_radius = radius_time_sum*1000
otc_brute = brute_time_sum*1000
print("kdtree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
query = db_np[222, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
print("result ------------")
print(result_set)
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
# print("result ------------")
# print(result_set)
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
# print(nn_idx)
kd_knn = knn_time_sum * 1000
kd_radius = radius_time_sum * 1000
kd_brute = brute_time_sum * 1000
print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" % (construction_time_sum * 1000 / iteration_num,
knn_time_sum * 1000 / iteration_num,
radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print("Octree knn/brute = %.3f, radius/bruet = %.3f" % (otc_knn/otc_brute, otc_radius/otc_brute))
print("Kdtree knn/brute = %.3f, radius/bruet = %.3f" % (kd_knn / kd_brute, kd_radius / kd_brute))
def main():
# configuration
leaf_size = 32
min_extent = 0.001
k = 8
radius = 1
root_dir = '/home/xcy/myWork/pointCloudProcessing/lesson2/bin' # 数据集路径
cat = os.listdir(root_dir)
iteration_num = len(cat)
print("octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
radius_fast_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
#
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_fast_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print(
"Octree: build %.3f, knn %.3f, radius %.3f, radius_fast %.3f, brute %.3f"
% (construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
radius_fast_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print("kdtree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
# begin_t = time.time()
# root = kdtree.kdtree_construction_median(db_np, leaf_size)
# construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num / (brute_time_sum * 1000 / iteration_num),
radius_time_sum * 1000 / iteration_num /
(brute_time_sum * 1000 / iteration_num),
brute_time_sum * 1000 / iteration_num))
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
# data
root_dir = '../data/bin'
cat = os.listdir(root_dir)
iteration_num = len(cat)
print("Brute-force --------------")
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
query = db_np[0, :]
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Brute-force: %.3f" % (brute_time_sum * 1000 / iteration_num))
# scipy.spatial.KDTree
print("scipy.spatial.KDTree --------------")
construction_time_sum = 0
knn_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
tree = spatial.KDTree(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
res = tree.query(query, k)
knn_time_sum += time.time() - begin_t
print("scipy.spatial.KDTree: build %.3f, knn %.3f" %
(construction_time_sum * 1000 / iteration_num,
knn_time_sum * 1000 / iteration_num))
print("Octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print("KD-Tree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("KD-Tree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1.0
root_dir = './' # 数据集路径
files = os.listdir(root_dir)
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
iteration_num = 0
for file in files:
if file.find('bin') == -1:
continue
iteration_num += 1
filename = os.path.join(root_dir, file)
db_np = read_velodyne_bin(filename)
#point_indices=np.array([1,2,3,4,5,6,7])
#db_larger=point_indices[db_np[point_indices,1]>0.4]
#db_larger_idx=np.where
#print("test data",db_np[point_indices,1])
#print("test index",db_np[point_indices,1]>0.4)
#print("test1",db_larger,db_np[db_larger,1])
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print("--------------")
KDTreeBenchmark(root_dir, files, k, leaf_size, radius)
print("--------------")
KDTreeBenchmark(root_dir, files, k, leaf_size, radius, "mean")
print("--------------")
KDTreeBenchmark(root_dir, files, k, leaf_size, radius, "meanraw")
print("--------------")
KDTreeBenchmark(root_dir, files, k, leaf_size, radius, "median", "adapt")
print("--------------")
KDTreeBenchmark(root_dir, files, k, leaf_size, radius, "meanraw", "adapt")
print("--------------")
KDTreeBenchmark(root_dir, files, k, leaf_size, radius, "mean", "adapt")
# construction_time_sum = 0
# knn_time_sum = 0
# radius_time_sum = 0
# brute_time_sum = 0
# iteration_num = 0
# for file in files:
# if file.find('bin') == -1:
# continue
# iteration_num += 1
# filename = os.path.join(root_dir, file)
# db_np = read_velodyne_bin(filename)
# begin_t = time.time()
# root = kdtree.kdtree_construction(db_np, leaf_size)
# construction_time_sum += time.time() - begin_t
# query = db_np[0,:]
# begin_t = time.time()
# result_set = KNNResultSet(capacity=k)
# kdtree.kdtree_knn_search(root, db_np, result_set, query)
# print("result set from KD Tree\n",result_set)
# knn_time_sum += time.time() - begin_t
# # print("--------")
# begin_t = time.time()
# result_set = RadiusNNResultSet(radius=radius)
# kdtree.kdtree_radius_search(root, db_np, result_set, query)
# #print(result_set)
# radius_time_sum += time.time() - begin_t
# #print("--------")
# begin_t = time.time()
# diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
# nn_idx = np.argsort(diff)
# nn_dist = diff[nn_idx]
# #print(nn_idx[0:k])
# #print(nn_dist[0:k])
# brute_time_sum += time.time() - begin_t
# depth = [0]
# max_depth = [0]
# kdtree.traverse_kdtree(root, depth, max_depth)
# print("tree depth: %d, max depth: %d" % (depth[0],max_depth[0]))
# print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" % (construction_time_sum * 1000 / iteration_num,
# knn_time_sum * 1000 / iteration_num,
# radius_time_sum * 1000 / iteration_num,
# brute_time_sum * 1000 / iteration_num))
print("--------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
iteration_num = 0
for file in files:
if file.find('bin') == -1:
continue
iteration_num += 1
filename = os.path.join(root_dir, file)
db_np = read_velodyne_bin(filename)
begin_t = time.time()
tree = KDTree(db_np)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = tree.query(([query[0], query[1], query[2]]), k=k, p=2)
print("result set", result_set)
knn_time_sum += time.time() - begin_t
# print("--------")
begin_t = time.time()
result_set = tree.query_ball_point(([query[0], query[1], query[2]]),
radius)
# print(result_set)
radius_time_sum += time.time() - begin_t
# print("--------")
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("scipy idx", nn_idx[0:k])
print("scipy dist", nn_dist[0:k])
print("Scipy Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
filename = '000000.bin'
db_np = read_velodyne_bin(filename)
iteration_num = 1
cat = []
print("octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print("kdtree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
#filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum * 1000 /
iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
def main():
# configuration
leaf_size = 32
min_extent = 0.005
k = 8
radius = 1
nums = 1 # 点的数量（搜索）
abs_path = os.path.abspath(
os.path.dirname(__file__)) # 数据集路径 (数据集直接放在当前路径下)
filename = os.path.join(abs_path, '000000.bin') # 只读取一个文件,如果要读取所有文件,需要循环读入
iteration_num = 1
# 读取数据并进行采样
db_np_origin = read_velodyne_bin(filename) # N*3
db_np_idx = np.random.choice(db_np_origin.shape[0],
size=(30000, )) # 随机采样30000个点
db_np = db_np_origin[db_np_idx]
#root_dir = '/Users/renqian/cloud_lesson/kitti' # 数据集路径
#cat = os.listdir(root_dir)
#iteration_num = len(cat)
print("搜索数据集中的 %d 个点: " % nums)
print(
"---------------------------------Octree ----------------------------------"
)
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
#filename = os.path.join(root_dir, cat[i])
'''
db_np_origin = read_velodyne_bin(filename) # N*3
db_np_idx = np.random.choice(db_np_origin.shape[0],size =(30000,)) # 随机采样30000个点
db_np = db_np_origin[db_np_idx]
'''
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size,
min_extent) # Octree Construction
construction_time_sum += time.time() - begin_t
print("The sum of the points: ", db_np.shape[0])
nums = db_np.shape[0]
# 查找nums个点
for num in range(nums):
query = db_np[num, :] # 搜索点数组（假设第一个点为搜索点）
# knn search
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
# radius search
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
#octree.octree_radius_search_fast(root, db_np, result_set, query)
octree.octree_radius_search(root,
db_np,
result_set,
query,
search='fast')
radius_time_sum += time.time() - begin_t
# brute search
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000 / iteration_num, knn_time_sum *
1000 / iteration_num, radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
print(
"---------------------------------Kdtree ----------------------------------"
)
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
knn_scipy_time_sum = 0
radius_scipy_time_sum = 0
for i in range(iteration_num):
#filename = os.path.join(root_dir, cat[i])
#db_np = read_velodyne_bin(filename) # N*3
print("The sum of the points: ", db_np.shape[0])
begin_t = time.time()
root = kdtree.kdtree_construction(db_np,
leaf_size) # kd tree construction
construction_time_sum += time.time() - begin_t
tree = spatial.KDTree(db_np) # 利用spatial库函数构造kdtree
for num in range(nums):
query = db_np[num, :]
# scipy.spatial.KDtree
begin_t = time.time()
tree.query(query, k=k)
knn_scipy_time_sum += time.time() - begin_t
#print(tree.query(query, k=k))
# knn search
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
#print(result_set)
# scipy.spatial.KDtree (radius search)
begin_t = time.time()
tree.query_ball_point(query, radius)
radius_scipy_time_sum += time.time() - begin_t
# radius search
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
# print(result_set)
# brute search
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print(
"Kdtree: build %.3f, knn_scipy %.3f, knn %.3f, radius_scipy %.3f, radius %.3f, brute %.3f"
%
(construction_time_sum * 1000 / iteration_num, knn_scipy_time_sum *
1000 / iteration_num, knn_time_sum * 1000 / iteration_num,
radius_scipy_time_sum * 1000 / iteration_num, radius_time_sum *
1000 / iteration_num, brute_time_sum * 1000 / iteration_num))
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
root_dir = '/home/gc/github_repository/DeepBlue/lesson_2/kitty/' # 数据集路径
cat = os.listdir(root_dir)
iteration_num = len(cat)
print("octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
db_np = db_np.T
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[0,:]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" % (construction_time_sum*1000/iteration_num,
knn_time_sum*1000/iteration_num,
radius_time_sum*1000/iteration_num,
brute_time_sum*1000/iteration_num))
otc_knn = knn_time_sum*1000
otc_radius = radius_time_sum*1000
otc_brute = brute_time_sum*1000
print("kdtree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration_num):
filename = os.path.join(root_dir, cat[i])
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
query = db_np[0,:]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" % (construction_time_sum * 1000 / iteration_num,
knn_time_sum * 1000 / iteration_num,
radius_time_sum * 1000 / iteration_num,
brute_time_sum * 1000 / iteration_num))
kd_knn = knn_time_sum * 1000
kd_radius = radius_time_sum * 1000
kd_brute = brute_time_sum * 1000
print("Octree knn/brute = %.3f, radius/bruet = %.3f" % (otc_knn/otc_brute, otc_radius/otc_brute) )
print("Kdtree knn/brute = %.3f, radius/bruet = %.3f" % (kd_knn / otc_brute, kd_radius / otc_brute) )
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
#root_dir = '/Users/renqian/cloud_lesson/kitti' # 数据集路径
#cat = os.listdir(root_dir)
#iteration_num = len(cat)
# load date
filename = '/home/ljn/SLAM/dateset/000000.bin'
db_np = read_velodyne_bin(filename)
iteration = 1
print("octree -----------------------------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
result_set_knn = KNNResultSet(capacity=k)
query = db_np[95, :]
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
begin_t = time.time()
octree.octree_knn_search(root, db_np, result_set_knn, query)
knn_time_sum += time.time() - begin_t
print('knn search result\n', result_set_knn)
begin_t = time.time()
result_set_rnn = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set_rnn, query)
radius_time_sum += time.time() - begin_t
print('rnn search result\n', result_set_rnn)
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
nn_dist_idx_pre = np.linspace(0, nn_dist.shape[0], nn_dist.shape[0])
nn_dist_idx = nn_dist_idx_pre[nn_idx]
brute_time_sum += time.time() - begin_t
#brute knn search
result_set_knn_brute = KNNResultSet(capacity=k)
for index in range(k):
result_set_knn_brute.add_point(nn_dist[index], nn_dist_idx[index])
# brute radiusNN search
result_set_rnn_brute = RadiusNNResultSet(radius=radius)
for index in range(nn_dist.shape[0]):
if nn_dist[index] < radius:
result_set_rnn_brute.add_point(nn_dist[index], nn_dist_idx[index])
continue
else:
break
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000, knn_time_sum * 1000,
radius_time_sum * 1000, brute_time_sum * 1000))
print("kdtree -----------------------------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
for i in range(iteration):
query = db_np[95, :]
begin_t = time.time()
root = kdtree.kdtree_construction(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
begin_t = time.time()
result_set_knn = KNNResultSet(capacity=k)
kdtree.kdtree_knn_search(root, db_np, result_set_knn, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set_rnn = RadiusNNResultSet(radius=radius)
kdtree.kdtree_radius_search(root, db_np, result_set_rnn, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
nn_dist_idx_pre = np.linspace(0, nn_dist.shape[0] - 1,
nn_dist.shape[0])
nn_dist_idx = nn_dist_idx_pre[nn_idx]
# brute knn search
result_set_knn_brute = KNNResultSet(capacity=k)
for index in range(k):
result_set_knn_brute.add_point(nn_dist[index], nn_dist_idx[index])
# brute radiusNN search
result_set_rnn_brute = RadiusNNResultSet(radius=radius)
for index in range(nn_dist.shape[0]):
if nn_dist[index] < radius:
result_set_rnn_brute.add_point(nn_dist[index],
nn_dist_idx[index])
continue
else:
break
print("Kdtree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000, knn_time_sum * 1000,
radius_time_sum * 1000, brute_time_sum * 1000))
print("scipy kdtree -----------------------------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
query = db_np[95, :]
begin_t = time.time()
tree = spatial.KDTree(db_np, leaf_size)
construction_time_sum += time.time() - begin_t
#no knn
begin_t = time.time()
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set_rnn = tree.query_ball_point(query, radius)
radius_time_sum += time.time() - begin_t
print('rnn search result\n', result_set_rnn)
print("Octree: build %.3f, knn %.3f, radius %.3f" %
(construction_time_sum * 1000, knn_time_sum * 1000,
radius_time_sum * 1000))
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
N = 30000 # 对N个点做搜索
# read data
filename = '../000000.bin'
db_np = read_velodyne_bin(filename)
indices = np.arange(db_np.shape[0])
# brute force
begin_t = time.time()
brute_result_indices = brute_force(db_np, indices, k, N)
brute_time = time.time() - begin_t
print("time consumption for brute force: ", brute_time)
# scipy kd-tree
begin_t = time.time()
scipy_result_indices = scipy_kd_tree(db_np, k, leaf_size, N)
scipy_time = time.time() - begin_t
print("time consumption for scipy kd-tree: ", scipy_time)
# kd tree
begin_t = time.time()
print("constructing a kd-tree")
root = kd_tree.kd_tree_construction(db_np, leaf_size)
kd_tree_construction_time = time.time() - begin_t
print("construct a kd-tree takes ", kd_tree_construction_time)
begin_t = time.time()
print("using kd-tree-knn")
kd_tree_knn_result = []
for i in range(N):
knn_result_set = KNNResultSet(k)
kd_tree.kd_tree_knn_search(root, db_np, knn_result_set, db_np[i])
kd_tree_knn_result.append(knn_result_set)
kd_tree_knn_time = time.time() - begin_t
print("time consumption for kd-tree-knn: ", kd_tree_knn_time)
begin_t = time.time()
print("using kd-tree-radius")
kd_tree_radius_result = []
for i in range(N):
radius_result_set = RadiusNNResultSet(radius)
kd_tree.kd_tree_radius_search(root, db_np, radius_result_set, db_np[i])
kd_tree_radius_result.append(radius_result_set)
kd_tree_radius_time = time.time() - begin_t
print("time consumption for kd-tree-radius: ", kd_tree_radius_time)
# octree
begin_t = time.time()
print("constructing a octree")
root = octree.octree_construction(db_np, leaf_size, min_extent)
octree_construction_time = time.time() - begin_t
print("construct a octree takes ", octree_construction_time)
begin_t = time.time()
print("using octree-knn")
octree_knn_result = []
for i in range(N):
knn_result_set = KNNResultSet(k)
octree.octree_knn_search(root, db_np, knn_result_set, db_np[i])
octree_knn_result.append(knn_result_set)
octree_knn_time = time.time() - begin_t
print("time consumption for octree-knn: ", octree_knn_time)
begin_t = time.time()
print("using octree-radius")
octree_radius_result = []
for i in range(N):
radius_result_set = RadiusNNResultSet(radius)
octree.octree_radius_search(root, db_np, radius_result_set, db_np[i])
octree_radius_result.append(radius_result_set)
octree_radius_time = time.time() - begin_t
print("time consumption for octree-radius: ", octree_radius_time)
# print the reuslt
print("---------------- brute force reusult ---------------------: ")
print(brute_result_indices[2])
print("---------------- scipy kd-tree-knn reusult ----------------: ")
print(scipy_result_indices[2])
print("----------------- kd-tree-knn reusult ---------------------: ")
print(kd_tree_knn_result[2])
print("------------------ kd-tree-radius reusult -----------------: ")
print(kd_tree_radius_result[2])
print("------------------ octree-knn reusult ---------------------: ")
print(octree_knn_result[2])
print("------------------ octree-radius reusult ------------------: ")
print(octree_radius_result[2])
def main():
# configuration
leaf_size = 32
min_extent = 0.0001
k = 8
radius = 1
# root_dir = './Users/renqian/cloud_lesson/kitti' # 数据集路径
# cat = os.listdir(root_dir)
# iteration_num = len(cat)
print("octree --------------")
construction_time_sum = 0
knn_time_sum = 0
radius_time_sum = 0
brute_time_sum = 0
# for i in range(iteration_num):
# filename = os.path.join(root_dir, cat[i])
# db_np = read_velodyne_bin(filename)
#
# begin_t = time.time()
# root = octree.octree_construction(db_np, leaf_size, min_extent)
# construction_time_sum += time.time() - begin_t
#
# query = db_np[0,:]
#
# begin_t = time.time()
# result_set = KNNResultSet(capacity=k)
# octree.octree_knn_search(root, db_np, result_set, query)
# knn_time_sum += time.time() - begin_t
#
# begin_t = time.time()
# result_set = RadiusNNResultSet(radius=radius)
# octree.octree_radius_search_fast(root, db_np, result_set, query)
# radius_time_sum += time.time() - begin_t
#
# begin_t = time.time()
# diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
# nn_idx = np.argsort(diff)
# nn_dist = diff[nn_idx]
# brute_time_sum += time.time() - begin_t
filename = './000000.bin'
db_np = read_velodyne_bin(filename)
begin_t = time.time()
root = octree.octree_construction(db_np, leaf_size, min_extent)
construction_time_sum += time.time() - begin_t
query = db_np[0, :]
begin_t = time.time()
result_set = KNNResultSet(capacity=k)
octree.octree_knn_search(root, db_np, result_set, query)
knn_time_sum += time.time() - begin_t
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_fast(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
begin_t = time.time()
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
brute_time_sum += time.time() - begin_t
print("Octree: build %.3f, knn %.3f, radius %.3f, brute %.3f" %
(construction_time_sum * 1000, knn_time_sum * 1000,
radius_time_sum * 1000, brute_time_sum * 1000))
begin_t = time.time()
result_set = RadiusNNResultSet(radius=radius)
octree.octree_radius_search_plus(root, db_np, result_set, query)
radius_time_sum += time.time() - begin_t
print("Octree: radius_plus %.3f" % (radius_time_sum * 1000))
