How to detect simple geometric shapes using OpenCV

Question

I have this project where I need  on iOS  to detect simple geometric shapes inside an image     After searching the internet I have concluded that the best tool for this is OpenCV  The thing is that up until two hours ago I had no idea what OpenCV is and I have never even remotely did anything involving image processing  My main experience is JS HTML C  SQL Objective-C     Where do I start with this   I have found this answer that I was able to digest and by reading already other stuff  I understand that OpenCV should return an Array of shapes with the points corners  is this true  Also how will it represent a circle or a half circle   Also what about the shape orientation   Do you know of any Demo iOS project that can demonstrate a similar functionality

User · Accepted Answer

If you have only these regular shapes  there is a simple procedure as follows     Find Contours in the image   image should be binary as given in your question  Approximate each contour using approxPolyDP function  First  check number of elements in the approximated contours of all the shapes  It is to recognize the shape  For eg  square will have 4  pentagon will have 5  Circles will have more  i don t know  so we find it    I got 16 for circle and 9 for half-circle   Now assign the color  run the code for your test image  check its number  fill it with corresponding colors    Below is my example in Python   import numpy as np import cv2  img   cv2 imread  shapes png   gray   cv2 cvtColor img  cv2 COLOR BGR2GRAY   ret thresh   cv2 threshold gray 127 255 1   contours h   cv2 findContours thresh 1 2   for cnt in contours      approx   cv2 approxPolyDP cnt 0 01 cv2 arcLength cnt True  True      print len approx      if len approx   5          print  pentagon          cv2 drawContours img  cnt  0 255 -1      elif len approx   3          print  triangle          cv2 drawContours img  cnt  0  0 255 0  -1      elif len approx   4          print  square          cv2 drawContours img  cnt  0  0 0 255  -1      elif len approx     9          print  half-circle          cv2 drawContours img  cnt  0  255 255 0  -1      elif len approx   gt  15          print  circle          cv2 drawContours img  cnt  0  0 255 255  -1   cv2 imshow  img  img  cv2 waitKey 0  cv2 destroyAllWindows     Below is the output     Remember  it works only for regular shapes   Alternatively to find circles  you can use houghcircles  You can find a tutorial here   Regarding iOS  OpenCV devs are developing some iOS samples this summer  So visit their site   www code opencv org and contact them   You can find slides of their tutorial here   http   code opencv org svn gsoc2012 ios trunk doc CVPR2012 OpenCV4IOS Tutorial pdf

User · Answer

You can also use template matching to detect shapes inside an image

User · Answer

The answer depends on the presence of other shapes  level of noise if any and invariance you want to provide for  e g  rotation  scaling  etc   These requirements will define not only the algorithm but also required pre-procesing stages to extract features   Template matching that was suggested above works well when shapes aren t rotated or scaled and when there are no similar shapes around  in other words  it finds a best translation in the image where template is located   double minVal  maxVal  Point minLoc  maxLoc  Mat image  template  result     template is your shape matchTemplate image  template  result  CV TM CCOEFF NORMED   minMaxLoc result   amp minVal   amp maxVal   amp minLoc   amp maxLoc      maxLoc is answer   Geometric hashing is a good method to get invariance in terms of rotation and scaling  this method would require extraction of some contour points   Generalized Hough transform can take care of invariance  noise and would have minimal pre-processing but it is a bit harder to implement than other methods  OpenCV has such transforms for lines and circles   In the case when number of shapes is limited calculating moments or counting convex hull vertices may be the easiest solution  openCV structural analysis

[opencv] How to detect simple geometric shapes using OpenCV

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