- Use the OpenCV functions @ref cv::HoughLines and @ref cv::HoughLinesP to detect lines in an
- Use the OpenCV functions **HoughLines()** and **HoughLinesP()** to detect lines in an
image.
Theory
...
...
@@ -79,54 +82,93 @@ 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 \f$(\theta, r_{\theta})\f$
- In OpenCV it is implemented with the function @ref cv::HoughLines
- In OpenCV it is implemented with the function **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 \f$(x_{0}, y_{0}, x_{1}, y_{1})\f$
- In OpenCV it is implemented with the function @ref cv::HoughLinesP
- In OpenCV it is implemented with the function **HoughLinesP()**
### What does this program do?
- Loads an image
- Applies a *Standard Hough Line Transform* and a *Probabilistic Line Transform*.
- Display the original image and the detected line in three windows.
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://github.com/opencv/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](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp).
@include samples/cpp/houghlines.cpp
@add_toggle_cpp
The sample code that we will explain can be downloaded from
