压缩矩形列表
我有一个未排序的矩形列表(描述为一对左下角和右上角坐标)。我正在寻找一种有效的算法来通过替换相邻或重叠的 bbox 来压缩此列表。
这是我的代码,我正在沿垂直轴对所有 bbox 进行排序,尝试沿水平轴压缩和排序结果,然后再次压缩。这不是最理想的,但足够快。
(** boundingbox, (x0,y0) means left down, (x1,y1) right upper edge *)
type bbox_t = { x0 : int; y0 : int; x1 : int; y1 : int; }
let _test_if_compressable a b =
assert(a.x0 >= 0);
assert(a.y0 >= 0);
assert(b.x0 >= 0);
assert(b.y0 >= 0);
assert(a.x1 >= a.x0);
assert(a.y1 >= a.y0);
assert(b.x1 >= b.x0);
assert(b.y1 >= b.y0);
let same_x_ab = (a.x0 == b.x0) && (a.x1 == b.x1) in
let same_y_ab = (a.y0 == b.y0) && (a.y1 == b.y1) in
(same_x_ab && same_y_ab) ||
(same_x_ab && (a.y1 >= (b.y0-1)) && (a.y0 <= b.y0)) ||
(same_x_ab && (b.y1 >= (a.y0-1)) && (b.y0 <= a.y0)) ||
(same_y_ab && (a.x1 >= (b.x0-1)) && (a.x0 <= b.x0)) ||
(same_y_ab && (b.x1 >= (a.x0-1)) && (b.x0 <= a.x0))
;;
(* compresses list of bboxes by joining bboxes of same dimension
* @param sort1 primary sorting function (hsort)
* @param sort2 secondary sorting function (vsort)
* @param bboxlst list of bboxes
* @return list of bboxes
*)
let compress_bboxes sort1 sort2 bboxlst =
let rec compr lst newlst =
let _calc_new bbox1 bbox2 =
let miny = min bbox1.y0 bbox2.y0
and maxy = max bbox1.y1 bbox2.y1
and minx = min bbox1.x0 bbox2.x0
and maxx = max bbox1.x1 bbox2.x1
in
{x0=minx; y0=miny; x1=maxx; y1=maxy}
in
match lst with
[] -> List.rev newlst
| hd::[] -> List.rev (hd::newlst)
| hd1::hd2::tl when hd1 = hd2 -> compr tl (hd1::newlst)
| hd1::hd2::tl when _test_if_compressable hd1 hd2 -> let b = _calc_new hd1 hd2 in compr tl (b::newlst)
| hd1::hd2::tl ->
compr (hd2::tl) (hd1::newlst)
in
let newxlst = compr (sort1 bboxlst) [] in
let newylst = compr (sort2 newxlst) [] in
newylst
;;
另一种解决方案是贪婪的,但非常低:
let first_partition e lst =
let rec _first_partition accu =
function
[] -> None
| hd::tl when not (_test_if_compressable hd e) ->
_first_partition (hd::accu) tl
| hd::tl -> Some (hd, (List.rev_append accu tl))
in
_first_partition [] lst
in
let rec _compr accu =
function
[] -> List.rev accu
| hd::tl ->
match (first_partition hd tl) with
None -> _compr (hd::accu) tl
| Some (c,r) -> let newbbox = get_surrounding_bbox [c;hd] in
_compr (newbbox::accu) r
in
_compr [] lst (* call this repeately to improve compression *)
您还有其他提示吗?该算法不能完美压缩,但应该快速并减少生成的矩形(bbox)的大小。有人可以帮忙吗?
I have an unsorted list of rectangles (described as pair of lower left and upper right coordinates). I am looking for an efficient algorithm to compress this list by replacing neighboring or overlapping bboxes.
Here are my code, I am sorting all bboxes along vertical axis, try to compress and sorting result along horizontal axis and compress again. This is suboptimal but fast enough.
(** boundingbox, (x0,y0) means left down, (x1,y1) right upper edge *)
type bbox_t = { x0 : int; y0 : int; x1 : int; y1 : int; }
let _test_if_compressable a b =
assert(a.x0 >= 0);
assert(a.y0 >= 0);
assert(b.x0 >= 0);
assert(b.y0 >= 0);
assert(a.x1 >= a.x0);
assert(a.y1 >= a.y0);
assert(b.x1 >= b.x0);
assert(b.y1 >= b.y0);
let same_x_ab = (a.x0 == b.x0) && (a.x1 == b.x1) in
let same_y_ab = (a.y0 == b.y0) && (a.y1 == b.y1) in
(same_x_ab && same_y_ab) ||
(same_x_ab && (a.y1 >= (b.y0-1)) && (a.y0 <= b.y0)) ||
(same_x_ab && (b.y1 >= (a.y0-1)) && (b.y0 <= a.y0)) ||
(same_y_ab && (a.x1 >= (b.x0-1)) && (a.x0 <= b.x0)) ||
(same_y_ab && (b.x1 >= (a.x0-1)) && (b.x0 <= a.x0))
;;
(* compresses list of bboxes by joining bboxes of same dimension
* @param sort1 primary sorting function (hsort)
* @param sort2 secondary sorting function (vsort)
* @param bboxlst list of bboxes
* @return list of bboxes
*)
let compress_bboxes sort1 sort2 bboxlst =
let rec compr lst newlst =
let _calc_new bbox1 bbox2 =
let miny = min bbox1.y0 bbox2.y0
and maxy = max bbox1.y1 bbox2.y1
and minx = min bbox1.x0 bbox2.x0
and maxx = max bbox1.x1 bbox2.x1
in
{x0=minx; y0=miny; x1=maxx; y1=maxy}
in
match lst with
[] -> List.rev newlst
| hd::[] -> List.rev (hd::newlst)
| hd1::hd2::tl when hd1 = hd2 -> compr tl (hd1::newlst)
| hd1::hd2::tl when _test_if_compressable hd1 hd2 -> let b = _calc_new hd1 hd2 in compr tl (b::newlst)
| hd1::hd2::tl ->
compr (hd2::tl) (hd1::newlst)
in
let newxlst = compr (sort1 bboxlst) [] in
let newylst = compr (sort2 newxlst) [] in
newylst
;;
Another solution is a greedy one, but very low:
let first_partition e lst =
let rec _first_partition accu =
function
[] -> None
| hd::tl when not (_test_if_compressable hd e) ->
_first_partition (hd::accu) tl
| hd::tl -> Some (hd, (List.rev_append accu tl))
in
_first_partition [] lst
in
let rec _compr accu =
function
[] -> List.rev accu
| hd::tl ->
match (first_partition hd tl) with
None -> _compr (hd::accu) tl
| Some (c,r) -> let newbbox = get_surrounding_bbox [c;hd] in
_compr (newbbox::accu) r
in
_compr [] lst (* call this repeately to improve compression *)
Do you have additional hints? The algorithm must not compress perfectly, but should be fast and reduce the size of resulting rectangles (bboxes). Could anybody help?
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我会考虑使用 kd 树。基本上,您构建了一个二叉树,在每个级别上将平面分割在一个点上。您可以交替沿 x 方向或 y 方向进行分割。
如果使用左下坐标作为键,则给定节点中的矩形可以包含的唯一矩形是右子树中的矩形。
编辑:实际上这可能不太像那样工作。一个矩形可能被其左子树中的一个矩形包含。
在午休期间,我快速实现了 kd 树。它的运行时间与您的第一个函数相当,但似乎取得了更好的结果。我还没有检查它的正确性(尽管我使用与您使用的相同的测试和压缩代码),但是在 100000 个随机矩形上(x,y 值从 (0,0) 到 (99,99 )kd 树方法将其压缩为 47539 个矩形,而排序列表方法将其压缩为 68393 个。 kd 树稍微慢一些,尤其是在较小的输入上(对于 100 个矩形,它花费了两倍的时间,对于 100,000 个矩形,它仅慢了 4% )。这是我的代码:
有一些明显的改进空间(一方面,我使用第一个元素而不是中值元素对 kd 树进行分区)
I would look into using a kd tree. Basically you build a binary tree where at each level you split the plane on a point. You alternate whether you split in the x or y direction.
If you use the bottom left coordinate as your key, then the only rectangles that could be contained by a rectangle in a given node are those in the right sub-tree.
Edit: actually this might not work quite like that. It's possible that a rectangle could be contained by a rectangle in its left subtree.
During my lunch break I did a quick implementation of a kd tree. It runs in comparable time to your first function but appears to achieve better results. I haven't checked it for correctness (although I'm using the same test and compress code that you're using), but on 100000 random rectangles (with x,y values going from (0,0) to (99,99) the kd tree approach compressed this to 47539 rects while the sorted list approach got it down to 68393. The kd tree was slightly slower, especially on smaller inputs (for 100 rects it took twice as long, for 100,000 it was only 4% slower). Here is my code:
There is some obvious room for improvement (for one thing, I'm partition the kd tree using the first element instead of the median element)