道格拉斯-普克算法（经纬度或坐标点抽稀）

骑猪看日落 2022-02-19 14:55 409阅读 1赞

## 起因 ##

最近在做一个车联网项目，有一个场景是车辆定时上报当前所在经纬度等位置信息上报给平台，平台通过web页面在高德地图上展示车辆行驶路径。

由于车辆上报规则是每隔4s上报一次，一个小时也就是900个点，一天也就是21600个点，如果是10辆车就是216000个点，那如果是100辆车，甚至是10000辆车对于数据库存储来说会是一个灾难，对于渲染地图，过多的点，也减少页面的流畅度。

考虑到车辆直线行驶的时候，只需要记录第一个点和最后一个点即可，期间的点位都是没有必要的，如果拐弯的话，只需要记录一两个拐弯的点即可，这样就可以减少点数，进而减少存储或者绘图压力。

有没有一种合适的算法解决此问题呢，遂查阅资料，了解到一个算法----道格拉斯-普克算法

## 介绍 ##

> 道格拉斯-普克算法 (Douglas–Peucker algorithm，亦称为拉默-道格拉斯-普克算法、迭代适应点算法、分裂与合并算法)是将曲线近似表示为一系列点，并减少点的数量的一种算法。它的优点是具有平移和旋转不变性，给定曲线与阈值后，抽样结果一定。—摘自百度百科

![道格拉斯图解][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4NjAyOTU3_size_16_color_FFFFFF_t_70]  
如果有8个点，如上图（1），抽稀步骤如下：

1.  在曲线首尾两点间虚连一条直线,求出其余各点到该直线的距离，如右图（1）。
2.  选到点到直线距离的最大者与阈值相比较,若大于阈值,则记录该点,否则将直线两端点间各点全部舍去，如右图（2），记录第4个点，然后根据地4个点，将点分成两段1-4，4-8
3.  然后分别对1-4，4-8重复第1、2步操作，迭代操作，即仍选距离最大者与阈值比较,依次取舍,直到无点可舍去，最后得到满足给定精度限差的曲线点坐标，如图（3）、（4）依次保留第6点、第7点，舍去其他点,即完成线的化简。

结合步骤，这里有两点数学知识，一是两点确定一条直线方程，二是求点到直线的距离。

点到直线的距离公式如下

![点到直线的距离公式][2019041618451547.png]

## PHP代码 ##

/**
         * 点到直线的距离
         * @param $a float
         * @param $b float
         * @param $c float
         * @param $xy string 点坐标例如 "2,2"
         * @return number
         */
        public function getDistanceFromPointToLine($a, $b, $c, $xy)
        {
        
            $x = explode(",", $xy)[0];
            $y = explode(",", $xy)[1];
            return abs(($a * $x + $b * $y + $c) / sqrt($a * $a + $b * $b));
        }
    
        /**
         * 根据两个点求直线方程 ax+by+c=0
         * @param $xy1 string 点1,例如"1,1"
         * @param $xy2 string 点2,例如"2,2"
         * @return array
         */
        public function getLineByPoint($xy1, $xy2)
        {
        
            $x1 = explode(",", $xy1)[0];//第一个点的横坐标
            $y1 = explode(",", $xy1)[1];//第一个点的纵坐标
            $x2 = explode(",", $xy2)[0];//第二个点的横坐标
            $y2 = explode(",", $xy2)[1];//第二个点的横坐标
    
            $a = $y2 - $y1;
            $b = $x1 - $x2;
            $c = ($y1 - $y2) * $x1 - $y1 * ($x1 - $x2);
    
            return [$a, $b, $c];
        }
    
        /**
         * 稀疏点
         * @param $points array 参数为["1,2","2,3"]点集
         * @param $max float 阈值,越大稀疏效果越好但是细节越差
         * @return array
         */
        public function sparePoints($points, $max)
        {
        
            if (count($points) < 3) {
        
                return $points;
            }
            $xy1 = $points[0];//取第一个点
            $xy2 = end($points);//取最后一个点
            list($a, $b, $c) = $this->getLineByPoint($xy1, $xy2);//获取直线方程的a,b,c值
    
            $ret   = [];//最后稀疏以后的点集
            $dmax  = 0;//记录点到直线的最大距离
            $split = 0;//分割位置
            for ($i = 1; $i < count($points) - 1; $i++) {
        
                $d = $this->getDistanceFromPointToLine($a, $b, $c, $points[$i]);
                if ($d > $dmax) {
        
                    $split = $i;
                    $dmax  = $d;
                }
            }
    
            if ($tmp) {
        //如果存在点到首位点连成直线的距离大于max的,即需要再次划分
                $child1 = $this->sparePoints(array_slice($points, 0, $split + 1), $max);//按split分成左边一份，递归
                $child2 = $this->sparePoints(array_slice($points, $split), $max);//按split分成右边一份，递归
                //因为child1的最后一个点和child2的第一个点,肯定是同一个（即为分割点）,合并的时候,需要注意一下
                $ret = array_merge($ret, $child1, array_slice($child2, 1));
                return $ret;
            } else {
        //如果不存在点到直线的距离大于阈值的,那么就直接是首尾点了
                return [$points[0], end($points)];
            }
        }

## 效果 ##

随机找了一段路，根据高德地图的驾驶导航提示，选取了80个经纬度（因为这些点都是高德算出来的重要点位，经过了一定的稀疏），如下：

$points = [
                "117.212448,39.133785",
                "117.212669,39.133667",
                "117.213165,39.133297",
                "117.213203,39.13327",
                "117.213554,39.133099",
                "117.213669,39.13295",
                "117.213921,39.132462",
                "117.214088,39.132126",
                "117.214142,39.131962",
                "117.214188,39.13176",
                "117.214233,39.131397",
                "117.21418,39.13055",
                "117.214279,39.130459",
                "117.214539,39.130375",
                "117.214874,39.130188",
                "117.216881,39.128716",
                "117.217598,39.127995",
                "117.217972,39.12759",
                "117.218338,39.127178",
                "117.218407,39.127071",
                "117.218567,39.126911",
                "117.219704,39.125702",
                "117.219795,39.12561",
                "117.220284,39.125114",
                "117.220619,39.124802",
                "117.221046,39.124348",
                "117.221138,39.124245",
                "117.221268,39.124092",
                "117.222321,39.122955",
                "117.222824,39.122406",
                "117.222916,39.122311",
                "117.223663,39.121544",
                "117.2239,39.121452",
                "117.224113,39.12159",
                "117.224251,39.121677",
                "117.225136,39.122208",
                "117.225281,39.122292",
                "117.225319,39.122311",
                "117.226273,39.122875",
                "117.226685,39.123127",
                "117.227371,39.12352",
                "117.227806,39.123779",
                "117.228477,39.124134",
                "117.228531,39.124161",
                "117.228531,39.124161",
                "117.228668,39.124187",
                "117.228897,39.124325",
                "117.229767,39.12479",
                "117.230927,39.12545",
                "117.231186,39.12561",
                "117.231659,39.125908",
                "117.231834,39.126026",
                "117.232018,39.126186",
                "117.232185,39.126362",
                "117.232353,39.126583",
                "117.232658,39.126972",
                "117.232658,39.126972",
                "117.233124,39.12748",
                "117.233253,39.127609",
                "117.233368,39.127689",
                "117.233513,39.127762",
                "117.233665,39.127823",
                "117.233734,39.127846",
                "117.233833,39.127865",
                "117.233994,39.127888",
                "117.234138,39.127892",
                "117.234329,39.127884",
                "117.234612,39.127838",
                "117.234955,39.127754",
                "117.235252,39.12767",
                "117.236282,39.12738",
                "117.237137,39.127129",
                "117.237671,39.126961",
                "117.237953,39.126949",
                "117.238213,39.126865",
                "117.238472,39.126793",
                "117.2397,39.126434",
                "117.242233,39.125698",
                "117.243538,39.12532",
                "117.243645,39.125298",
            ];
    
            $data = sparePoints($points, "0.00001");

运行结果

[
            "117.212448,39.133785", 
            "117.212669,39.133667", 
            "117.213203,39.13327", 
            "117.213554,39.133099", 
            "117.213669,39.13295", 
            "117.214088,39.132126", 
            "117.214188,39.13176", 
            "117.214233,39.131397", 
            "117.21418,39.13055", 
            "117.214279,39.130459", 
            "117.214539,39.130375", 
            "117.214874,39.130188", 
            "117.216881,39.128716", 
            "117.217598,39.127995", 
            "117.218338,39.127178", 
            "117.218407,39.127071", 
            "117.219704,39.125702", 
            "117.220284,39.125114", 
            "117.220619,39.124802", 
            "117.222824,39.122406", 
            "117.223663,39.121544", 
            "117.2239,39.121452", 
            "117.225136,39.122208", 
            "117.227806,39.123779", 
            "117.228531,39.124161", 
            "117.228668,39.124187", 
            "117.228897,39.124325", 
            "117.229767,39.12479", 
            "117.230927,39.12545", 
            "117.231659,39.125908", 
            "117.231834,39.126026", 
            "117.232018,39.126186", 
            "117.232185,39.126362", 
            "117.232658,39.126972", 
            "117.233253,39.127609", 
            "117.233368,39.127689", 
            "117.233513,39.127762", 
            "117.233734,39.127846", 
            "117.233994,39.127888", 
            "117.234329,39.127884", 
            "117.234612,39.127838", 
            "117.234955,39.127754", 
            "117.236282,39.12738", 
            "117.237671,39.126961", 
            "117.237953,39.126949", 
            "117.243645,39.125298"
        ]

前后图对比，图一为稀疏前，图二为稀疏后的

![稀疏前][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4NjAyOTU3_size_16_color_FFFFFF_t_70 1]

![稀疏后][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4NjAyOTU3_size_16_color_FFFFFF_t_70 2]

由于测试给的阈值为0.0001，可以看到地图点位由80个直接减少到46个点，并且保留了较多地图拐点细节。

因为通过高德拿到的路径经纬度本身就是稀疏过的，这算是进行了二次稀疏，能有这个效果，已经很不错了，在实际中，因为点数会更多，调整合适的阈值，稀疏效果会非常理想。

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4NjAyOTU3_size_16_color_FFFFFF_t_70]: /images/20220219/50e3ee4093ee457eb08d14a502aa1bf7.png
[2019041618451547.png]: /images/20220219/3699e68845324aa0b9be5961fb97df1c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4NjAyOTU3_size_16_color_FFFFFF_t_70 1]: /images/20220219/219c78b41d1c42a39a1b3c6c27b67114.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4NjAyOTU3_size_16_color_FFFFFF_t_70 2]: /images/20220219/551dd6cdec9548c2adf81110eb572a3a.png