| OpenCV 5.0.0alpha Open Source Computer Vision

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Harris Corner Detection

# 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 \((u,v)\) in all directions. This is expressed as below:

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

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

We have to maximize this function \(E(u,v)\) 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:

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

where

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

Here, \(I_x\) and \(I_y\) 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.

\[R = \det(M) - k(\operatorname{trace}(M))^2\]

where

- \(\det(M) = \lambda_1 \lambda_2\)
- \(\operatorname{trace}(M) = \lambda_1 + \lambda_2\)
- \(\lambda_1\) and \(\lambda_2\) are the eigenvalues of \(M\)

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

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

It can be represented in a nice picture as follows:

image

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:

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()
cv::imshow
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.
cv::destroyAllWindows
void destroyAllWindows()
Destroys all of the HighGUI windows.
cv::imread
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR_BGR)
Loads an image from a file.
cv::cvtColor
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0, AlgorithmHint hint=cv::ALGO_HINT_DEFAULT)
Converts an image from one color space to another.
cv::cornerHarris
void cornerHarris(InputArray src, OutputArray dst, int blockSize, int ksize, double k, int borderType=BORDER_DEFAULT)
Harris corner detector.
cv::dilate
void dilate(InputArray src, OutputArray dst, InputArray kernel, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
Dilates an image by using a specific structuring element.

Below are the three results:

image

# 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.

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)
cv::imwrite
CV_EXPORTS_W bool imwrite(const String &filename, InputArray img, const std::vector< int > &params=std::vector< int >())
Saves an image to a specified file.
cv::cornerSubPix
void cornerSubPix(InputArray image, InputOutputArray corners, Size winSize, Size zeroZone, TermCriteria criteria)
Refines the corner locations.
cv::threshold
double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type)
Applies a fixed-level threshold to each array element.
cv::connectedComponentsWithStats
int connectedComponentsWithStats(InputArray image, OutputArray labels, OutputArray stats, OutputArray centroids, int connectivity, int ltype, int ccltype)
computes the connected components labeled image of boolean image and also produces a statistics outpu...

Below is the result, where some important locations are shown in the zoomed window to visualize:

image

# Additional Resources

# Exercises