Harris Corner Detection {#tutorial_py_features_harris}
=======================

Goal
----

In this chapter,

-   We will understand the concepts behind Harris Corner Detection.
-   We will see the following functions: **cv.cornerHarris()**, **cv.cornerSubPix()**

Theory
------

In the last chapter, we saw that corners are regions in the image with large variation in intensity in
all the directions. One early attempt to find these corners was done by **Chris Harris & Mike
Stephens** in their paper **A Combined Corner and Edge Detector** in 1988, so now it is called
the Harris Corner Detector. He took this simple idea to a mathematical form. It basically finds the
difference in intensity for a displacement of \f$(u,v)\f$ in all directions. This is expressed as below:

\f[E(u,v) = \sum_{x,y} \underbrace{w(x,y)}_\text{window function} \, [\underbrace{I(x+u,y+v)}_\text{shifted intensity}-\underbrace{I(x,y)}_\text{intensity}]^2\f]

The window function is either a rectangular window or a Gaussian window which gives weights to pixels
underneath.

We have to maximize this function \f$E(u,v)\f$ for corner detection. That means we have to maximize the
second term. Applying Taylor Expansion to the above equation and using some mathematical steps (please
refer to any standard text books you like for full derivation), we get the final equation as:

\f[E(u,v) \approx \begin{bmatrix} u & v \end{bmatrix} M \begin{bmatrix} u \\ v \end{bmatrix}\f]

where

\f[M = \sum_{x,y} w(x,y) \begin{bmatrix}I_x I_x & I_x I_y \\
                                     I_x I_y & I_y I_y \end{bmatrix}\f]

Here, \f$I_x\f$ and \f$I_y\f$ are image derivatives in x and y directions respectively. (These can be easily found
using **cv.Sobel()**).

Then comes the main part. After this, they created a score, basically an equation, which
determines if a window can contain a corner or not.

\f[R = det(M) - k(trace(M))^2\f]

where
    -   \f$det(M) = \lambda_1 \lambda_2\f$
    -   \f$trace(M) = \lambda_1 + \lambda_2\f$
    -   \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigenvalues of M

So the magnitudes of these eigenvalues decide whether a region is a corner, an edge, or flat.

-   When \f$|R|\f$ is small, which happens when \f$\lambda_1\f$ and \f$\lambda_2\f$ are small, the region is
    flat.
-   When \f$R<0\f$, which happens when \f$\lambda_1 >> \lambda_2\f$ or vice versa, the region is edge.
-   When \f$R\f$ is large, which happens when \f$\lambda_1\f$ and \f$\lambda_2\f$ are large and
    \f$\lambda_1 \sim \lambda_2\f$, the region is a corner.

It can be represented in a nice picture as follows:

![image](images/harris_region.jpg)

So the result of Harris Corner Detection is a grayscale image with these scores. Thresholding for a
suitable score gives you the corners in the image. We will do it with a simple image.

Harris Corner Detector in OpenCV
--------------------------------

OpenCV has the function **cv.cornerHarris()** for this purpose. Its arguments are:

-   **img** - Input image. It should be grayscale and float32 type.
-   **blockSize** - It is the size of neighbourhood considered for corner detection
-   **ksize** - Aperture parameter of the Sobel derivative used.
-   **k** - Harris detector free parameter in the equation.

See the example below:
@code{.py}
import numpy as np
import cv2 as cv

filename = 'chessboard.png'
img = cv.imread(filename)
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)

gray = np.float32(gray)
dst = cv.cornerHarris(gray,2,3,0.04)

#result is dilated for marking the corners, not important
dst = cv.dilate(dst,None)

# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]

cv.imshow('dst',img)
if cv.waitKey(0) & 0xff == 27:
    cv.destroyAllWindows()
@endcode
Below are the three results:

![image](images/harris_result.jpg)

Corner with SubPixel Accuracy
-----------------------------

Sometimes, you may need to find the corners with maximum accuracy. OpenCV comes with a function
**cv.cornerSubPix()** which further refines the corners detected with sub-pixel accuracy. Below is
an example. As usual, we need to find the Harris corners first. Then we pass the centroids of these
corners (There may be a bunch of pixels at a corner, we take their centroid) to refine them. Harris
corners are marked in red pixels and refined corners are marked in green pixels. For this function,
we have to define the criteria when to stop the iteration. We stop it after a specified number of
iterations or a certain accuracy is achieved, whichever occurs first. We also need to define the size
of the neighbourhood it searches for corners.
@code{.py}
import numpy as np
import cv2 as cv

filename = 'chessboard2.jpg'
img = cv.imread(filename)
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)

# find Harris corners
gray = np.float32(gray)
dst = cv.cornerHarris(gray,2,3,0.04)
dst = cv.dilate(dst,None)
ret, dst = cv.threshold(dst,0.01*dst.max(),255,0)
dst = np.uint8(dst)

# find centroids
ret, labels, stats, centroids = cv.connectedComponentsWithStats(dst)

# define the criteria to stop and refine the corners
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria)

# Now draw them
res = np.hstack((centroids,corners))
res = np.int0(res)
img[res[:,1],res[:,0]]=[0,0,255]
img[res[:,3],res[:,2]] = [0,255,0]

cv.imwrite('subpixel5.png',img)
@endcode
Below is the result, where some important locations are shown in the zoomed window to visualize:

![image](images/subpixel3.png)

Additional Resources
--------------------

Exercises
---------
