How to find corners on a Image using OpenCv

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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:

  1. Convert from RGB to grayscale (cvCvtColor)
  2. Smooth (cvSmooth)
  3. Threshold (cvThreshold)
  4. Detect edges (cvCanny)
  5. Find contours (cvFindContours)
  6. Approximate contours with linear features (cvApproxPoly)
  7. 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.

Step 7 was necessary because a slightly noisy image can yield many structures that appear rectangular after polygonalization. In my application, I also had to deal with square-like structures that appeared within, or overlapped the desired square. I found the contour’s area property and center of gravity to be helpful in discerning the proper rectangle.

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