code: def detect(self, img):
      # convert our input image to a floating point data type and then
      # compute the Harris corner matrix
      gray = np.float32(img)
      H = cv2.cornerHarris(
          gray, self.blockSize, self.apertureSize, self.k
      )
  
      # for every (x, y)-coordinate where the Harris value is above the
      # threshold, create a keypoint (the Harris detector returns
      # keypoint size a 3-pixel radius)
      kps = np.argwhere(H > self.T * H.max())
      kps = [cv2.KeyPoint(pt[1], pt[0], 3) for pt in kps]
  
      # return the Harris keypoints
      return kps
  
imports: import cv2
  import numpy
  from .helpers import corners_to_keypoints
label_desc: Use image node_id 2 for calling the HARRIS obj's underlying member method code with example usage: obj.detect(img) and returns: kps