def kdtree_radius_search(root: Node, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
if root.is_leaf():
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
return False
# 作业3
# 提示：通过递归的方式实现搜索
# 屏蔽开始
if query[root.axis] <= root.value:
kdtree_radius_search(root.left, db, result_set, query)
if math.fabs(query[root.axis] - root.value) < result_set.worstDist():
kdtree_radius_search(root.right, db, result_set, query)
else:
kdtree_radius_search(root.right, db, result_set, query)
if math.fabs(query[root.axis] - root.value) < result_set.worstDist():
kdtree_radius_search(root.left, db, result_set, query)
# 屏蔽结束
return False
def octree_radius_search_fast(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
# 1. if query ball contains the octant, no need to check child, just compare all point in it!
# 只是不需要向下递归，这里的child是只当前节点的下一层，回溯还是有可能的！所以这里return的false是有原因的嗷！
if contains(query, result_set.worstDist(), root):
# compare all points:
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# no need to check child
return False
# consider leaf point:
if root.is_leaf and len(root.point_indices) > 0:
# compare all points:
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
return inside(query, result_set.worstDist(), root)
# 2. check all children
for c, child in enumerate(root.children):
if child is None:
continue
if False == overlaps(query, result_set.worstDist(), child):
continue
if octree_radius_search_fast(child, db, result_set,query):
return True
return inside(query, result_set.worstDist(), root)
def octree_radius_search_fast(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
if contains(query, result_set.worstDist(), root):
# compare the contents of the octant
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# don't need to check any child
return False
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# no need to go to most relevant child first, because anyway we will go through all children
for c, child in enumerate(root.children):
if child is None:
continue
if False == overlaps(query, result_set.worstDist(), child):
continue
if octree_radius_search_fast(child, db, result_set, query):
return True
return inside(query, result_set.worstDist(), root)
def octree_radius_search(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# 作业6
# 屏蔽开始
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# go to the relevant child first
morton_code = 0
if query[0] > root.center[0]:
morton_code = morton_code | 1
if query[1] > root.center[1]:
morton_code = morton_code | 2
if query[2] > root.center[2]:
morton_code = morton_code | 4
if octree_radius_search(root.children[morton_code], db, result_set, query):
return True
# check other children
for c, child in enumerate(root.children):
if c == morton_code or child is None:
continue
if False == overlaps(query, result_set.worstDist(), child):
continue
if octree_radius_search(child, db, result_set, query):
return True
# 屏蔽结束
# final check of if we can stop search
return inside(query, result_set.worstDist(), root)
def octree_radius_search_fast(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
# 作业5
# 提示：尽量利用上面的inside、overlaps、contains等函数
# 屏蔽开始
if contains(query, result_set.worstDist(), root):
#compare the contents of the octant
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query,0) - leaf_points, axis =1)
for i in range(diff.shape[0]):
# 屏蔽结束
return inside(query, result_set.worstDist(), root)
def octree_radius_search_plus(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# 提前结束：核心-八叉树对3个维度有限制；
# 结束条件：其实是两个方向：向下递归，向上回溯
# 1-当一个节点返回True时，表示找到knn了，维度限制导致不会有更优的了，不再向下，立即结束；
# 2-最坏距离球如果 inside 当前节点，不需要再向上回溯检查其他节点，立即结束；
# 跳过条件：在检查
# 1. search the first relevant child:
# 找到最近的孩子，根据查询点的莫顿码
morton_code = 0
if query[0] > root.center[0]:
morton_code = morton_code | 1
if query[1] > root.center[1]:
morton_code = morton_code | 2
if query[2] > root.center[2]:
morton_code = morton_code | 4
if octree_knn_search(root.children[morton_code],
db,
result_set,
query):
return True
# 2. check other children
inv_worstDist = 1 / result_set.worstDist()
for c, child in enumerate(root.children):
if c == morton_code or child is None:
continue
if False == overlaps_plus(query, inv_worstDist, child):
continue
if octree_knn_search(child, db, result_set, query):
return True
# final check of if we can stop search
return inside(query, result_set.worstDist(), root)
def octree_radius_search_fast(root: Octant, db: np.ndarray,
result_set: RadiusNNResultSet,
query: np.ndarray):
if root is None:
return False
# 作业5
# 提示：尽量利用上面的inside、overlaps、contains等函数
# 屏蔽开始 #如果球包含了这个root Octant则不需要去它的子节点找了
if (root.is_leaf and len(root.point_indices) > 0) or contains(
query, result_set.worstDist(), root):
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
#如果球包含了这个root Octant则不需要去它的子节点找了
children_idx = 0
if query[0] > root.center[0]: # x轴
children_idx = children_idx | 1
if query[1] > root.center[1]: # y轴
children_idx = children_idx | 2
if query[2] > root.center[2]: # z轴
children_idx = children_idx | 4
# 如果在这个子octant中发现，查询点在该子octant中，同时由worst_dist构成的球也被octant包含，所以直接返回
if octree_knn_search(root.children[children_idx], db, result_set, query):
return True
# 如果不满足上边的情况则需要遍历其他子octant
for c, child in enumerate(root.children):
if c == children_idx or root.children[c]:
continue
if overlaps(query, result_set.worstDist(), child) == False:
continue
if octree_knn_search(root.children[c], db, result_set, query):
return True
# 屏蔽结束
return inside(query, result_set.worstDist(), root)
def octree_radius_search(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# 作业6
# 屏蔽开始
# 屏蔽结束
# final check of if we can stop search
return inside(query, result_set.worstDist(), root)
def octree_radius_search_fast(root: Octant, db: np.ndarray, result_set: RadiusNNResultSet, query: np.ndarray):
if root is None:
return False
# 作业5
# 提示：尽量利用上面的inside、overlaps、contains等函数
# 屏蔽开始
# 屏蔽结束
return inside(query, result_set.worstDist(), root)
def radius_search(root: Node, result_set: RadiusNNResultSet, key):
if root is None:
return False
# compare the root itself
result_set.add_point(math.fabs(root.key - key), root.value)
if root.key >= key:
# iterate left branch first
if radius_search(root.left, result_set, key):
return True
elif math.fabs(root.key-key) < result_set.worstDist():
return radius_search(root.right, result_set, key)
return False
else:
# iterate right branch first
if radius_search(root.right, result_set, key):
return True
elif math.fabs(root.key-key) < result_set.worstDist():
return radius_search(root.left, result_set, key)
return False
def octree_radius_search_fast(root: Octant, db: np.ndarray,
result_set: RadiusNNResultSet,
query: np.ndarray):
if root is None:
return False
# 作业5
# 提示：尽量利用上面的inside、overlaps、contains等函数
# 屏蔽开始
if contains(query, result_set.worstDist(), root):
# the octant is contained by the query ball with resule_set.worstDist()
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return False
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# check other children
for c, child in enumerate(root.children):
if child is None:
continue
# if an octant is not overlapping with query ball, skip
if overlaps(query, result_set.worstDist(), child) == False:
continue
if octree_radius_search(child, db, result_set, query):
return True
# 屏蔽结束
return inside(query, result_set.worstDist(), root)
def octree_radius_search(root: Octant,
db: np.ndarray,
result_set: RadiusNNResultSet,
query: np.ndarray,
search='normal'):
if root is None:
return False
# 作业5
# 另外一种极端情况：查询半径已经包括了整个区域
if search == 'fast':
if contains(query, result_set.worstDist(), root):
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points,
axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
return False
# 如果是叶子，则将该区域内返回到result_set
if root.is_leaf and len(root.point_indices) > 0:
# compare the contents of a leaf
leaf_points = db[root.point_indices, :]
diff = np.linalg.norm(np.expand_dims(query, 0) - leaf_points, axis=1)
for i in range(diff.shape[0]):
result_set.add_point(diff[i], root.point_indices[i])
# check whether we can stop search now
return inside(query, result_set.worstDist(), root)
# 作业6
# 屏蔽开始
child_idx = -1
if search == 'normal':
child_idx = 0
# 判断所查询点在八叉树的位置
if query[0] > root.center[0]:
child_idx = child_idx | 1
if query[1] > root.center[1]:
child_idx = child_idx | 2
if query[2] > root.center[2]:
child_idx = child_idx | 4
# 递归判断是否在该区域就可以找到足够多的点
if octree_radius_search(root.children[child_idx], db, result_set,
query, search):
return True
# 没有在查询点区域找到，对其他区域进行搜索
for c, child in enumerate(root.children):
if c == child_idx or child is None: # 搜索区域没有点或所搜索到查询点区域，skip
continue
if False == overlaps(query, result_set.worstDist(),
child): # 搜索区域与查询点和最坏距离构成的球面没有交点，skip
continue
if octree_radius_search(child, db, result_set, query,
search): # 其他情况可以进入搜索区域搜索（递归）
return True
# 屏蔽结束
# final check of if we can stop search
return inside(query, result_set.worstDist(), root)
