<iframetitle="File Input and Output using XML and YAML files in OpenCV"width="560"height="349"src="http://www.youtube.com/embed/A4yqVnByMMM?rel=0&loop=1"frameborder="0"allowfullscreenalign="middle"></iframe>
Below is the output of the program. Use the first image as the input. For the DEM model, download the SRTM file located at the USGS here. `http://dds.cr.usgs.gov/srtm/version2_1/SRTM1/Region_04/N37W123.hgt.zip <http://dds.cr.usgs.gov/srtm/version2_1/SRTM1/Region_04/N37W123.hgt.zip>`_
- For sake of efficiency, OpenCV implements a detection method slightly trickier than the standard
Hough Transform: *The Hough gradient method*, which is made up of two main stages. The first
@ -34,82 +34,35 @@ Theory
Code
----
1. **What does this program do?**
-# **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.
2. The sample code that we will explain can be downloaded from
|TutorialHoughCirclesSimpleDownload|_. A slightly fancier version (which shows trackbars for
changing the threshold values) can be found |TutorialHoughCirclesFancyDownload|_.
@code{.cpp}
#include "opencv2/highgui.hpp"
#include "opencv2/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; }
-# The sample code that we will explain can be downloaded from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/houghcircles.cpp).
A slightly fancier version (which shows trackbars for
changing the threshold values) can be found [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp).
@includelineno samples/cpp/houghcircles.cpp
/// Convert it to gray
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Reduce the noise so we avoid false circle detection
The three plots intersect in one single point \f$(0.925, 9.6)\f$, these coordinates are the
parameters (\f$\theta, r\f$) or the line in which \f$(x_{0}, y_{0})\f$, \f$(x_{1}, y_{1})\f$ and
\f$(x_{2}, y_{2})\f$ lay.
4. What does all the stuff above mean? It means that in general, a line can be *detected* by
-# 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.
5. This is what the Hough Line Transform does. It keeps track of the intersection between curves of
-# 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 \f$(\theta, r_{\theta})\f$ of the intersection point.
@ -86,83 +90,20 @@ b. **The Probabilistic Hough Line Transform**
Code
----
1. **What does this program do?**
-# **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.
2. The sample code that we will explain can be downloaded from here_. A slightly fancier version
-# The sample code that we will explain can be downloaded from [here](https://github.com/Itseez/opencv/tree/master/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_.
@code{.cpp}
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include<iostream>
values) can be found [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp).
@includelineno samples/cpp/houghlines.cpp
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.jpg\n" <<endl;
* And here are two snapshots of the display window. The first picture shows the output after using the operator **Opening** with a cross kernel. The second picture (right side, shows the result of using a **Blackhat** operator with an ellipse kernel.
SourceForge](http://sourceforge.net/projects/opencvlibrary/files/opencv-android/) and download
the latest available version. Currently it's [OpenCV-2.4.9-android-sdk.zip](http://sourceforge.net/projects/opencvlibrary/files/opencv-android/2.4.9/OpenCV-2.4.9-android-sdk.zip/download).
2. Create a new folder for Android with OpenCV development. For this tutorial we have unpacked
-# Create a new folder for Android with OpenCV development. For this tutorial we have unpacked
OpenCV SDK to the `C:\Work\OpenCV4Android\` directory.
@note Better to use a path without spaces in it. Otherwise you may have problems with ndk-build.
3. Unpack the SDK archive into the chosen directory.
-# Unpack the SDK archive into the chosen directory.
You can unpack it using any popular archiver (e.g with 7-Zip_):
You can unpack it using any popular archiver (e.g with 7-Zip):
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In the previous tutorial @ref tutorial_introduction_to_svm there is an explanation of the atributes of the
@ -162,11 +161,12 @@ Explanation
better insight of the problem by making adjustments to this parameter.
@note Here there are just very few points in the overlapping region between classes, giving a smaller value to **FRAC_LINEAR_SEP** the density of points can be incremented and the impact of the parameter **CvSVM::C_SVC** explored deeply.
- *Termination Criteria of the algorithm*. The maximum number of iterations has to be
increased considerably in order to solve correctly a problem with non-linearly separable
training data. In particular, we have increased in five orders of magnitude this value.
3. **Train the SVM**
-# **Train the SVM**
We call the method @ref cv::ml::SVM::train to build the SVM model. Watch out that the training
process may take a quite long time. Have patiance when your run the program.
Maksim Shabunin
10 years ago
