Canny Edge Detector {#tutorial_canny_detector}
===================

@tableofcontents

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|    |    |
| -: | :- |
| Original author | Ana Huamán |
| Compatibility | OpenCV >= 3.0 |

Goal
----

In this tutorial you will learn how to:

-   Use the OpenCV function @ref cv::Canny to implement the Canny Edge Detector.

Theory
------

The *Canny Edge detector* @cite Canny86 was developed by John F. Canny in 1986. Also known to many as the
*optimal detector*, the Canny algorithm aims to satisfy three main criteria:
-   **Low error rate:** Meaning a good detection of only existent edges.
-   **Good localization:** The distance between edge pixels detected and real edge pixels have
    to be minimized.
-   **Minimal response:** Only one detector response per edge.

### Steps

-#  Filter out any noise. The Gaussian filter is used for this purpose. An example of a Gaussian
    kernel of \f$size = 5\f$ that might be used is shown below:

    \f[K = \dfrac{1}{159}\begin{bmatrix}
              2 & 4 & 5 & 4 & 2 \\
              4 & 9 & 12 & 9 & 4 \\
              5 & 12 & 15 & 12 & 5 \\
              4 & 9 & 12 & 9 & 4 \\
              2 & 4 & 5 & 4 & 2
                      \end{bmatrix}\f]

-#  Find the intensity gradient of the image. For this, we follow a procedure analogous to Sobel:
    -#  Apply a pair of convolution masks (in \f$x\f$ and \f$y\f$ directions:
        \f[G_{x} = \begin{bmatrix}
        -1 & 0 & +1  \\
        -2 & 0 & +2  \\
        -1 & 0 & +1
        \end{bmatrix}\f]\f[G_{y} = \begin{bmatrix}
        -1 & -2 & -1  \\
        0 & 0 & 0  \\
        +1 & +2 & +1
        \end{bmatrix}\f]

    -#  Find the gradient strength and direction with:
        \f[\begin{array}{l}
        G = \sqrt{ G_{x}^{2} + G_{y}^{2} } \\
        \theta = \arctan(\dfrac{ G_{y} }{ G_{x} })
        \end{array}\f]
        The direction is rounded to one of four possible angles (namely 0, 45, 90 or 135)

-#  *Non-maximum* suppression is applied. This removes pixels that are not considered to be part of
    an edge. Hence, only thin lines (candidate edges) will remain.
-#  *Hysteresis*: The final step. Canny does use two thresholds (upper and lower):

    -#  If a pixel gradient is higher than the *upper* threshold, the pixel is accepted as an edge
    -#  If a pixel gradient value is below the *lower* threshold, then it is rejected.
    -#  If the pixel gradient is between the two thresholds, then it will be accepted only if it is
        connected to a pixel that is above the *upper* threshold.

    Canny recommended a *upper*:*lower* ratio between 2:1 and 3:1.

-#  For more details, you can always consult your favorite Computer Vision book.

Code
----

@add_toggle_cpp
-   The tutorial code's is shown lines below. You can also download it from
    [here](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp)
    @include samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp
@end_toggle

@add_toggle_java
-   The tutorial code's is shown lines below. You can also download it from
    [here](https://github.com/opencv/opencv/tree/4.x/samples/java/tutorial_code/ImgTrans/canny_detector/CannyDetectorDemo.java)
    @include samples/java/tutorial_code/ImgTrans/canny_detector/CannyDetectorDemo.java
@end_toggle

@add_toggle_python
-   The tutorial code's is shown lines below. You can also download it from
    [here](https://github.com/opencv/opencv/tree/4.x/samples/python/tutorial_code/ImgTrans/canny_detector/CannyDetector_Demo.py)
    @include samples/python/tutorial_code/ImgTrans/canny_detector/CannyDetector_Demo.py
@end_toggle

-   **What does this program do?**
    -   Asks the user to enter a numerical value to set the lower threshold for our *Canny Edge
        Detector* (by means of a Trackbar).
    -   Applies the *Canny Detector* and generates a **mask** (bright lines representing the edges
        on a black background).
    -   Applies the mask obtained on the original image and display it in a window.

Explanation (C++ code)
----------------------

-#  Create some needed variables:
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp variables

    Note the following:

    -#  We establish a ratio of lower:upper threshold of 3:1 (with the variable *ratio*).
    -#  We set the kernel size of \f$3\f$ (for the Sobel operations to be performed internally by the
        Canny function).
    -#  We set a maximum value for the lower Threshold of \f$100\f$.

-#  Loads the source image:
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp load

-#  Create a matrix of the same type and size of *src* (to be *dst*):
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp create_mat
-#  Convert the image to grayscale (using the function @ref cv::cvtColor ):
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp convert_to_gray
-#  Create a window to display the results:
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp create_window
-#  Create a Trackbar for the user to enter the lower threshold for our Canny detector:
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp create_trackbar
    Observe the following:

    -#  The variable to be controlled by the Trackbar is *lowThreshold* with a limit of
        *max_lowThreshold* (which we set to 100 previously)
    -#  Each time the Trackbar registers an action, the callback function *CannyThreshold* will be
        invoked.

-#  Let's check the *CannyThreshold* function, step by step:
    -#  First, we blur the image with a filter of kernel size 3:
        @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp reduce_noise
    -#  Second, we apply the OpenCV function @ref cv::Canny :
        @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp canny
        where the arguments are:

        -   *detected_edges*: Source image, grayscale
        -   *detected_edges*: Output of the detector (can be the same as the input)
        -   *lowThreshold*: The value entered by the user moving the Trackbar
        -   *highThreshold*: Set in the program as three times the lower threshold (following
            Canny's recommendation)
        -   *kernel_size*: We defined it to be 3 (the size of the Sobel kernel to be used
            internally)

-#  We fill a *dst* image with zeros (meaning the image is completely black).
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp fill
-#  Finally, we will use the function @ref cv::Mat::copyTo to map only the areas of the image that are
    identified as edges (on a black background).
    @ref cv::Mat::copyTo copy the *src* image onto *dst*. However, it will only copy the pixels in the
    locations where they have non-zero values. Since the output of the Canny detector is the edge
    contours on a black background, the resulting *dst* will be black in all the area but the
    detected edges.
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp copyto
-#  We display our result:
    @snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp display

Result
------

-   After compiling the code above, we can run it giving as argument the path to an image. For
    example, using as an input the following image:

    ![](images/Canny_Detector_Tutorial_Original_Image.jpg)

-   Moving the slider, trying different threshold, we obtain the following result:

    ![](images/Canny_Detector_Tutorial_Result.jpg)

-   Notice how the image is superposed to the black background on the edge regions.
