理想很丰满，现实很骨感，常常以为实现一个算法不是很难，但在现实中场景更加复杂，硬件限制，速度要求，好吧，只能push LZ想方法解决，也就是说进入了PDCA（PLAN，DO，CHECK，ACTION）的模式。

下面介绍一个OpenCV的小接口，用来判断点和contour的位置关系

def pointPolygonTest(contour, pt, measureDist): # real signature unknown; restored from __doc__
    """
    pointPolygonTest(contour, pt, measureDist) -> retval
    .   @brief Performs a point-in-contour test.
    .   
    .   The function determines whether the point is inside a contour, outside, or lies on an edge (or
    .   coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
    .   value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
    .   Otherwise, the return value is a signed distance between the point and the nearest contour edge.
    .   
    .   See below a sample output of the function where each image pixel is tested against the contour:
    .   
    .   ![sample output](pics/pointpolygon.png)
    .   
    .   @param contour Input contour.
    .   @param pt Point tested against the contour.
    .   @param measureDist If true, the function estimates the signed distance from the point to the
    .   nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
    """
    pass

contour很好理解，使用接口cv2.findContours找到对应图片的contour，或者其他方式也可以

point，需要准备好需要的点，需要tuple类型

measureDist：measureDist如果为true，则该函数估计从点到最近的轮廓边缘的有符号距离。 否则，该功能仅检查该点是否在轮廓内。

这个按照我们的需要设置为False即可，当measureDist设置为false时，若返回值为+1，表示点在轮廓内部，返回值为-1，表示在轮廓外部，返回值为0，表示在轮廓上。

代码示例：

flag = cv2.pointPolygonTest(contours[i], test_point, False)

刚做完了一个demo，就整理一下啦_(≧▽≦)/啦啦啦