Added Hough Lines Tutorial in reST

doc/conf.py

@@ -313,7 +313,10 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
             'laplacian': ('http://opencv.willowgarage.com/documentation/cpp/image_filtering.html#cv-laplacian%s', None),
 	    'canny': ('http://opencv.willowgarage.com/documentation/cpp/imgproc_feature_detection.html?#Canny%s', None),
 	    'copy_to': ('http://opencv.willowgarage.com/documentation/cpp/core_basic_structures.html?#Mat::copyTo%s', None), 	
-            'opencv_group' : ('http://tech.groups.yahoo.com/group/OpenCV/%s', None)
+            'opencv_group' : ('http://tech.groups.yahoo.com/group/OpenCV/%s', None),
+	    'hough_lines' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_feature_detection.html?#cv-houghlines%s', None),
+	    'hough_lines_p' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_feature_detection.html?#cv-houghlinesp%s', None),
+	    'hough_circles' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_feature_detection.html?#cv-houghcircles%s', None)
            }
 
 

doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.rst

@@ -0,0 +1,78 @@
+.. _hough_circle:
+
+Hough Circle Transform
+***********************
+
+Goal
+=====
+In this tutorial you will learn how to:
+
+* Use the OpenCV functions :hough_circles:`HoughCircles <>` to detect circles in an image.
+
+Code
+======
+
+#. **What does this program do?**
+ 
+   * Loads an image and blur it to reduce the noise
+   * Applies the *Hough Circle Transform* to the blurred image . 
+   * Display the detected circle in a window.
+
+#. The sample code that we will explain can be downloaded from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/houghlines.cpp>`_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found  `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp>`_
+
+.. code-block:: cpp 
+
+   #include "opencv2/highgui/highgui.hpp"
+   #include "opencv2/imgproc/imgproc.hpp"
+   #include <iostream>
+   #include <stdio.h>
+
+   using namespace cv;
+
+   /** @function main */
+   int main(int argc, char** argv)
+   {
+     Mat src, src_gray;
+
+     /// Read the image
+     src = imread( argv[1], 1 );
+
+     if( !src.data )
+       { return -1; }
+
+     /// Convert it to gray 
+     cvtColor( src, src_gray, CV_BGR2GRAY );
+
+     /// Reduce the noise so we avoid false circle detection
+     GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
+
+     vector<Vec3f> circles;
+
+     /// Apply the Hough Transform to find the circles
+     HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );
+
+     /// Draw the circles detected
+     for( size_t i = 0; i < circles.size(); i++ )
+     {
+         Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
+         int radius = cvRound(circles[i][2]);
+         // circle center
+         circle( src, center, 3, Scalar(0,255,0), -1, 8, 0 );
+         // circle outline
+         circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );
+      }
+
+     /// Show your results 
+     namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );
+     imshow( "Hough Circle Transform Demo", src );
+
+     waitKey(0);
+     return 0;
+   }
+
+Result
+=======
+ 
+.. image:: images/Hough_Circle_Tutorial_Result.jpg
+   :alt: Result of detecting circles with Hough Transform
+   :align: center 

doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.rst

@@ -0,0 +1,289 @@
+.. _hough_lines:
+
+Hough Line Transform
+*********************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+* Use the OpenCV functions :hough_lines:`HoughLines <>` and :hough_lines_p:`HoughLinesP <>` to detect lines in an image.
+  
+Theory
+=======
+
+.. note::
+   The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.
+
+Hough Line Transform
+---------------------
+#. The Hough Line Transform is a transform used to detect straight lines. 
+#. To apply the Transform, first an edge detection pre-processing is desirable.
+
+How does it work?
+^^^^^^^^^^^^^^^^^^
+
+#. As you know, a line in the image space can be expressed with two variables. For example:
+ 
+   a. In the **Cartesian coordinate system:**  Parameters: :math:`(m,b)`.
+   b. In the **Polar coordinate system:** Parameters: :math:`(r,\theta)`
+
+   .. image:: images/Hough_Lines_Tutorial_Theory_0.jpg
+      	   :alt: Line variables
+	   :height: 200pt
+   	   :align: center 
+
+   For Hough Transforms, we will express lines in the *Polar system*. Hence, a line equation can be written as: 
+
+   .. math::
+
+      y = \left ( -\dfrac{\cos \theta}{\sin \theta} \right ) x + \left ( \dfrac{r}{\sin \theta} \right ) 
+
+  Arranging the terms: :math:`r = x \cos \theta + y \sin \theta`
+
+#. In general for each point :math:`(x_{0}, y_{0})`, we can define the family of lines that goes through that point as:
+
+   .. math::
+   
+      r_{\theta} = x_{0} \cdot \cos \theta  + y_{0} \cdot \sin \theta
+
+   Meaning that each pair :math:`(r_{\theta},\theta)` represents each line that passes by :math:`(x_{0}, y_{0})`. 
+
+#. If for a given :math:`(x_{0}, y_{0})` we plot the family of lines that goes through it, we get a sinusoid. For instance, for :math:`x_{0} = 8` and :math:`y_{0} = 6` we get the following plot (in a plane :math:`\theta` - :math:`r`):
+
+   .. image:: images/Hough_Lines_Tutorial_Theory_1.jpg
+      	   :alt: Polar plot of a the family of lines of a point
+	   :height: 200pt
+   	   :align: center 
+
+   We consider only points such that :math:`r > 0` and :math:`0< \theta < 2 \pi`. 
+
+#. We can do the same operation above for all the points in an image. If the curves of two different points intersect in the plane :math:`\theta` - :math:`r`, that means that both points belong to a same line. For instance, following with the example above and drawing the plot for two more points: :math:`x_{1} = 9`, :math:`y_{1} = 4` and :math:`x_{2} = 12`, :math:`y_{2} = 3`, we get:
+
+   .. image:: images/Hough_Lines_Tutorial_Theory_2.jpg
+      	   :alt: Polar plot of the family of lines for three points
+	   :height: 200pt
+   	   :align: center 
+   The three plots intersect in one single point :math:`(0.925, 9.6)`, these coordinates are the parameters (:math:`\theta, r`) or the line in which :math:`(x_{0}, y_{0})`, :math:`(x_{1}, y_{1})` and :math:`(x_{2}, y_{2})` lay. 
+
+#. What does all the stuff above mean? It means that in general, a line can be *detected* by finding the number of intersections between curves.The more curves intersecting means that the line represented by that intersection have more points. In general, we can define a *threshold* of the minimum number of intersections needed to *detect* a line.
+ 
+#. This is what the Hough Line Transform does. It keeps track of the intersection between curves of every point in the image. If the number of intersections is above some *threshold*, then it declares it as a line with the parameters :math:`(\theta, r_{\theta})` of the intersection point.
+
+Standard and Probabilistic Hough Line Transform
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+OpenCV implements two kind of Hough Line Transforms: 
+
+a. **The Standard Hough Transform**
+
+  * It consists in pretty much what we just explained in the previous section. It gives you as result a vector of couples :math:`(\theta, r_{\theta})`
+
+  * In OpenCV it is implemented with the function :hough_lines:`HoughLines <>`
+
+b. **The Probabilistic Hough Line Transform**
+
+  * A more efficient implementation of the Hough Line Transform. It gives as output the extremes of the detected lines :math:`(x_{0}, y_{0}, x_{1}, y_{1})`
+
+  * In OpenCV it is implemented with the function :hough_lines_p:`HoughLinesP <>`
+
+Code
+======
+
+#. **What does this program do?**
+ 
+   * Loads an image
+   * Applies either a *Standard Hough Line Transform* or a *Probabilistic Line Transform*. 
+   * Display the original image and the detected line in two windows.
+
+#. The sample code that we will explain can be downloaded from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/houghlines.cpp>`_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found  `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp>`_
+
+.. code-block:: cpp 
+
+   #include "opencv2/highgui/highgui.hpp"
+   #include "opencv2/imgproc/imgproc.hpp"
+
+   #include <iostream>
+
+   using namespace cv;
+   using namespace std;
+
+   void help()
+   {
+    cout << "\nThis program demonstrates line finding with the Hough transform.\n"
+            "Usage:\n"
+            "./houghlines <image_name>, Default is pic1.png\n" << endl;
+   }
+
+   int main(int argc, char** argv)
+   {
+    const char* filename = argc >= 2 ? argv[1] : "pic1.png";
+
+    Mat src = imread(filename, 0);
+    if(src.empty())
+    {
+        help();
+        cout << "can not open " << filename << endl;
+        return -1;
+    }
+
+    Mat dst, cdst;
+    Canny(src, dst, 50, 200, 3);
+    cvtColor(dst, cdst, CV_GRAY2BGR);
+
+    #if 0
+     vector<Vec2f> lines;
+     HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );
+
+     for( size_t i = 0; i < lines.size(); i++ )
+     {
+        float rho = lines[i][0], theta = lines[i][1];
+        Point pt1, pt2;
+        double a = cos(theta), b = sin(theta);
+        double x0 = a*rho, y0 = b*rho;
+        pt1.x = cvRound(x0 + 1000*(-b));
+        pt1.y = cvRound(y0 + 1000*(a));
+        pt2.x = cvRound(x0 - 1000*(-b));
+        pt2.y = cvRound(y0 - 1000*(a));
+        line( cdst, pt1, pt2, Scalar(0,0,255), 3, CV_AA);
+     }
+    #else
+     vector<Vec4i> lines;
+     HoughLinesP(dst, lines, 1, CV_PI/180, 50, 50, 10 );
+     for( size_t i = 0; i < lines.size(); i++ )
+     {
+       Vec4i l = lines[i];
+       line( cdst, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, CV_AA);
+     }
+    #endif
+    imshow("source", src);
+    imshow("detected lines", cdst);
+
+    waitKey();
+
+    return 0;
+   }
+
+Explanation
+=============
+
+#. Load an image
+
+   .. code-block:: cpp
+
+      Mat src = imread(filename, 0);
+      if(src.empty())
+      {
+        help();
+        cout << "can not open " << filename << endl;
+        return -1;
+      }
+
+#. Detect the edges of the image by using a Canny detector
+
+   .. code-block:: cpp
+
+      Canny(src, dst, 50, 200, 3);
+
+   Now we will apply the Hough Line Transform. We will explain how to use both OpenCV functions available for this purpose:
+
+#. **Standard Hough Line Transform**
+
+   a. First, you apply the Transform:
+
+      .. code-block:: cpp
+
+         vector<Vec2f> lines;
+         HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );
+
+      with the following arguments:
+
+      * *dst*: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)
+      * *lines*: A vector that will store the parameters :math:`(r,\theta)` of the detected lines
+      * *rho* : The resolution of the parameter :math:`r` in pixels. We use **1** pixel.
+      * *theta*: The resolution of the parameter :math:`\theta` in radians. We use **1 degree** (CV_PI/180)
+      * *threshold*: The minimum number of intersections to "*detect*" a line
+      * *srn* and *stn*: Default parameters to zero. Check OpenCV reference for more info. 
+
+   b. And then you display the result by drawing the lines. 
+
+      .. code-block:: cpp
+
+         for( size_t i = 0; i < lines.size(); i++ )
+         {
+           float rho = lines[i][0], theta = lines[i][1];
+           Point pt1, pt2;
+           double a = cos(theta), b = sin(theta);
+           double x0 = a*rho, y0 = b*rho;
+           pt1.x = cvRound(x0 + 1000*(-b));
+           pt1.y = cvRound(y0 + 1000*(a));
+           pt2.x = cvRound(x0 - 1000*(-b));
+           pt2.y = cvRound(y0 - 1000*(a));
+           line( cdst, pt1, pt2, Scalar(0,0,255), 3, CV_AA);
+         }
+
+#. **Probabilistic Hough Line Transform**
+
+   a. First you apply the transform:
+
+      .. code-block:: cpp
+
+         vector<Vec4i> lines;
+         HoughLinesP(dst, lines, 1, CV_PI/180, 50, 50, 10 );
+
+      with the arguments:
+ 
+      * *dst*: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)
+      * *lines*: A vector that will store the parameters :math:`(x_{start}, y_{start}, x_{end}, y_{end})` of the detected lines
+      * *rho* : The resolution of the parameter :math:`r` in pixels. We use **1** pixel.
+      * *theta*: The resolution of the parameter :math:`\theta` in radians. We use **1 degree** (CV_PI/180)
+      * *threshold*: The minimum number of intersections to "*detect*" a line
+      * *minLinLength*: The minimum number of points that can form a line. Lines with less than this number of points are disregarded. 
+      * *maxLineGap*: The maximum gap between two points to be considered in the same line. 
+
+   b. And then you display the result by drawing the lines.
+
+      .. code-block:: cpp
+
+         for( size_t i = 0; i < lines.size(); i++ )
+         {
+           Vec4i l = lines[i];
+           line( cdst, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, CV_AA);
+         }
+
+
+#. Display the original image and the detected lines: 
+
+   .. code-block:: cpp
+
+      imshow("source", src);
+      imshow("detected lines", cdst);
+
+#. Wait until the user exits the program
+
+   .. code-block:: cpp
+
+      waitKey();
+
+
+Result
+=======
+
+.. note::
+  
+   The results below are obtained using the slightly fancier version we mentioned in the *Code* section. It still implements the same stuff as above, only adding the Trackbar for the Threshold.
+
+Using an input image such as:
+
+.. image:: images/Hough_Lines_Tutorial_Original_Image.jpg
+        :alt: Result of detecting lines with Hough Transform
+        :align: center 
+ 
+We get the following result by using the Probabilistic Hough Line Transform:
+
+.. image:: images/Hough_Lines_Tutorial_Result.jpg
+        :alt: Result of detecting lines with Hough Transform
+        :align: center 
+
+You may observe that the number of lines detected vary while you change the *threshold*. The explanation is sort of evident: If you establish a higher threshold, fewer lines will be detected (since you will need more points to declare a line detected). 
+

doc/tutorials/imgproc/pyramids/pyramids.rst

@@ -10,8 +10,8 @@ In this tutorial you will learn how to:
 
 * Use the OpenCV functions :pyr_up:`pyrUp <>` and :pyr_down:`pyrDown <>` to downsample  or upsample a given image.
   
-Cool Theory
-============
+Theory
+=======
 
 .. note::
    The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.

doc/tutorials/imgproc/table_of_content_imgproc/table_of_content_imgproc.rst

@@ -196,3 +196,39 @@ In this section you will learn about the image processing (manipulation) functio
                            :height: 100pt
                            :width:  100pt
 
+
+* :ref:`hough_lines`
+
+  ===================== ==============================================
+   |HoughLines|         *Title:* **Hough Line Transform**
+
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+
+                        Where we learn how to detect lines
+
+  ===================== ==============================================
+  
+  .. |HoughLines| image:: images/imgtrans/Hough_Lines_Tutorial_Cover.jpg
+                        :height: 100pt
+                        :width:  100pt
+
+
+* :ref:`hough_circle`
+
+  ===================== ==============================================
+   |HoughCircle|        *Title:* **Hough Circle Transform**
+
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+
+                        Where we learn how to detect circles
+
+  ===================== ==============================================
+  
+  .. |HoughCircle| image:: images/imgtrans/Hough_Circle_Tutorial_Cover.jpg
+                         :height: 100pt
+                         :width:  100pt
+