首先，请检查/样品在你的OpenCV的分布/ C/ squares.c的。这个例子提供了一个方形的检测，如何检测角落类似的功能，它应该是一个不错的开始。然后，一起来看看在OpenCV的功能，导向功能，如cvCornerHarris（）和cvGoodFeaturesToTrack（）。



上述方法可以返回许多角落类似的功能 - 最不会“真正的角落”你正在寻找。在我的应用程序，我只好检测，旋转或倾斜的广场（透视）。我的检测流水线包括：



从RGB转换为灰度级（cvCvtColor）

平滑（cvSmooth）

阈值（cvThreshold）

检测边缘（cvCanny）

查找轮廓（cvFindContours的）

线性特征的大致轮廓与（cvApproxPoly）

查找“矩形”，这是结构：已经拥有4个点的polygonalized轮廓，有足够的面积，相邻的边〜 90度，“对面的”顶点之间的距离足够大，等等。



第7步是必要的，因为稍微嘈杂的图像可以产生许多结构出现矩形后polygonalization。在我的应用程序中，我也有处理方状结构，内出现，或重叠所需的平方。我发现轮廓的区域属性和重心是有帮助的挑剔适当的矩形。-First, check out /samples/c/squares.c in your OpenCV distribution. This example provides a square detector, and it should be a pretty good start on how to detect corner-like features. Then, take a look at OpenCV s feature-oriented functions like cvCornerHarris() and cvGoodFeaturesToTrack().



The above methods can return many corner-like features- most will not be the "true corners" you are looking for. In my application, I had to detect squares that had been rotated or skewed (due to perspective). My detection pipeline consisted of:



　　Convert from RGB to grayscale (cvCvtColor)

　　Smooth (cvSmooth)

　　Threshold (cvThreshold)

　　Detect edges (cvCanny)

　　Find contours (cvFindContours)

　　Approximate contours with linear features (cvApproxPoly)

　　Find "rectangles" which were structures that: had polygonalized contours possessing 4 points, were of sufficient area, had adjacent edges were ~90 degrees, had distance between "opposite" vertices was of sufficient size, etc.