Harris corner detector performs well in many cases, but it misses out on a few things. Around six years after the original paper by Harris and Stephens, Shi-Tomasi came up with a better corner detector. You can read the original paper at http://www.ai.mit.edu/courses/6.891/handouts/shi94good.pdf. They used a different scoring function to improve the overall quality. Using this method, we can find the 'N' strongest corners in the given image. This is very useful when we don't want to use every single corner to extract information from the image.

If you apply the Shi-Tomasi corner detector to the image shown earlier, you will see something like this:

Following is the code:

import cv2
import numpy as np

img = cv2.imread('box.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

corners = cv2.goodFeaturesToTrack(gray, 7, 0.05, 25)
corners = np.float32(corners)

for item in corners:
    x, y = item[0]
    cv2.circle(img, (x,y), 5, 255, -1)

cv2.imshow("Top 'k' features", img...