OpenCV notes (17)--Detecting lines using Hough transform

In general, when we detect the contour of the object, there will be a need to detect the line, then you can use the OPENCV in the Hough transform.

A brief description of the principle of Hough transform see: Http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html#hough-lines

To put it simply, for a single pixel, it could be a lot of straight lines, and we can construct countless straight lines with one point.

For a pixel position (x, y), from Cartesian coordinates and polar coordinates two angles, we can have

And then get

The upper-order (x, y) is an argument, R and Theta are dependent variables.

So for one (x, y), we have countless r and theta corresponding to it, see

For multiple (x, y), we draw their R and Theta:

Because their R curves have intersections, they can be theta by the same line at an angle, or understood to intersect their r-theta curves.

Let's add a limit to these points: they are adjacent points.

On the image, they are a straight line consisting of several adjacent points.

In OpenCV, the Hough transform is implemented in two ways: Houghlines () and Houghlinesp (). The latter is a more effective implementation.

void houghlines(inputarray image, Outputarray lines, double rho, double Theta, int threshold, double srn=0, double stn=0 )

void houghlinesp(inputarray image, Outputarray lines, double Rho , double theta, int threshold, double minlinelength=0, double maxlinegap =0 )

Images is a 8-bit single-channel graph, typically the output of the canny method (this is a contour map)

Lines is the line set of the output, typically the vector<vec2f> type.

Rho is generally 1. Not clear.

Theta is generally cv_pi/180. Not clear.

The threshold is the minimum number of intersecting bars of the R-theta curve we have described above, and it can also be understood as the least pixels that make up a line.

Paste a section of the OPENCV code using the Hough transform, you can see the use of houghlinesp more convenient:

1 voidStandard_hough (int,void* )2 {3Vector<vec2f>S_lines;4 Cvtcolor (edges, Standard_hough, CV_GRAY2BGR);5 6   ///1. Use standard Hough Transform7Houghlines (edges, S_lines,1, cv_pi/ the, Min_threshold + S_trackbar,0,0 );8 9   ///Show The resultTen    for(size_t i =0; I < s_lines.size (); i++ ) One      { A       floatr = s_lines[i][0], T = s_lines[i][1]; -       Doublecos_t = cos (t), sin_t =sin (t); -       Doublex0 = r*cos_t, y0 = r*sin_t; the       DoubleAlpha = +; -  -Point Pt1 (Cvround (x0 + alpha* (-sin_t)), Cvround (y0 + alpha*cos_t)); -Point Pt2 (Cvround (x0-alpha* (-sin_t)), Cvround (y0-alpha*cos_t)); +Line (Standard_hough, pt1, Pt2, Scalar (255,0,0),3, CV_AA); -      } +  A imshow (Standard_name, standard_hough); at } -  - /** - * @function Probabilistic_hough -  */ - voidProbabilistic_hough (int,void* ) in { -Vector<vec4i>P_lines; to Cvtcolor (edges, Probabilistic_hough, CV_GRAY2BGR); +  -   ///2. Use probabilistic Hough Transform theHoughlinesp (edges, P_lines,1, cv_pi/ the, Min_threshold + P_trackbar, -,Ten ); *  $   ///Show The resultPanax Notoginseng    for(size_t i =0; I < p_lines.size (); i++ ) -      { theVec4i L =P_lines[i]; +Line (Probabilistic_hough, point (l[0], l[1]), point (l[2], l[3]), Scalar (255,0,0),3, CV_AA); A      } the  + imshow (Probabilistic_name, probabilistic_hough); -}

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OpenCV notes (17)--Detecting lines using Hough transform