2D Numpy Array的边缘值

I have a 2D Numpy array of the following form (up to 5000 rows):

我有一个以下形式的2D Numpy数组（最多5000行）：

 [ 247.68512  182.67136]
 [ 248.71936  182.67136]
 [ 249.74336  182.67136]
 [ 253.85984  269.1072 ]
 [ 254.89408  269.1072 ]
 [ 255.91808  269.1072 ]
 [ 249.74336  182.67136]
 [ 250.7776   182.67136]
 [ 251.8016   182.67136]
 ...

Where column 0 corresponds to x, and column 1 to y.

其中第0列对应x，第1列对应y。

When plotted the data should resemble a blob shape.

绘制时，数据应类似于blob形状。

How can I reduce this data to just have the edge or contour around the blob?

如何将这些数据减少到只有blob周围的边缘或轮廓？

I have looked at some of the skimage edge detection functions but I think there is a pure numpy solution out there.

我已经看过一些skimage边缘检测功能，但我认为那里有一个纯粹的numpy解决方案。

Any help? If edge detection is the way to go what's the best tool?

有帮助吗？如果边缘检测是最好的工具吗？

Thanks

谢谢

--- Edit ---- The data is also unsorted, but I can sort around the orgin of the blob with some existing code I have. Not sure if that helps.

---编辑----数据也是未分类的，但我可以用我现有的一些代码对blob的orgin进行排序。不确定这是否有帮助。

--- Edit 2 --- Stumbled upon this beautiful solution by K.D.

---编辑2 ---偶然发现K.D.的这个美丽的解决方案。

I think this should work perfectly for this application. Will try this.

我认为这应该适合这个应用程序。会尝试这个。

1 个解决方案

#1

It depends on the topology of the blob. If the blob is smooth and has no voids inside you can do this: approach blob from far. Scan across blob location. When you hit a point - check neighboring grid. If you have high enough point density set isOnBorder = true. Now, for each neighboring point check if it is such that it has both empty and filled neighbors. If yes - this is a border point - add it to list. Repeat procedure until you get full border. If you have voids you would have to do grid inside and check for inner borders as well.

它取决于blob的拓扑结构。如果blob是光滑的并且内部没有空隙，你可以这样做：从远处接近blob。扫描blob位置。当你到达一个点 - 检查相邻的网格。如果你有足够高的点密度，则设置isOnBorder = true。现在，对于每个相邻点，检查它是否具有空的和填充的邻居。如果是 - 这是边界点 - 将其添加到列表中。重复此过程，直到获得完整边框。如果你有空洞，你必须在里面做网格并检查内部边界。

1 个解决方案

#1

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