如何使用 Opencv 从 Harris Corner Detector 中提取关键点

Question

• 首先我会使用cv::cornerHarris() 来检测角落（我可以轻松做到）。
• 其次，我想从 Harris 检测器中提取关键点并将它们存储在 std::vector<KeyPoint> 中（我不知道该怎么做）。稍后我将使用它来计算描述符并匹配它们。

• 我可以很容易地使用 SURF 来完成它们，但我想使用 Harris 角检测器来完成。

  /// Detecting corners
  cv::cornerHarris(leftRoi, dst, blockSize, apertureSize, k, BORDER_DEFAULT);
  
  /// Normalizing
  normalize(dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat());
  convertScaleAbs(dst_norm, dst_norm_scaled);
  
  /// Drawing a circle around corners
  for (int j = 0; j < dst_norm.rows; j++)
  {
      for (int i = 0; i < dst_norm.cols; i++)
      {
          if ((int)dst_norm.at<float>(j, i) > 165)
          {
  
              circle(dst_norm_scaled, Point(i, j), 5, Scalar(0), 2, 8, 0);
          }
      }
  }
  /// Showing the result
  namedWindow("corners_window", CV_WINDOW_AUTOSIZE);
  imshow("corners_window", dst_norm_scaled);
  

-这部分有问题（如何从哈里斯检测器上方提取关键点）

    std::vector<KeyPoint> keypoints;

Answer 1

Python

这是我用 Python 写的：

# convert coordinates to Keypoint type
eye_corner_keypoints = [cv2.KeyPoint(crd[0], crd[1], 13) for crd in eye_corner_coordinates]

# compute SIFT descriptors from corner keypoints
sift = cv2.xfeatures2d.SIFT_create()
eye_corner_descriptors = [sift.compute(gray,[kp])[1] for kp in eye_corner_keypoints]

C++

查看 OpenCV reference documentation 中 KeyPoint 类的构造函数签名：

KeyPoint (float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)

看起来您可以遍历您的坐标点并在每次迭代时（大致）实例化您的 KeyPoint 对象，如下所示：

for (int i = 0; i < num_points; i++) {
    KeyPoint kp(points_x[i], points_y[i], points_size[i]);
    /* ... */

警告：代码未经测试，我不是 C++ 程序员。

Answer 2

Try my code:
import cv2
import numpy as np
from skimage.io import imread
from skimage.color import rgb2gray
import matplotlib.pyplot as plt

img = imread('images/box.jpg')
img_gray = rgb2gray(img)


img_gray = np.float32(img_gray)
#cv2.imshow("Image",img)
#cv2.imshow("Gray Image",img_gray)
#Ix = cv2.Sobel(img_gray,cv2.CV_64F,1,0,ksize=5)
#Iy = cv2.Sobel(img_gray,cv2.CV_64F,0,1,ksize=5)
kernel_x = np.array([[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]])
kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
Ix = cv2.filter2D(img_gray,-1,kernel_x)
Iy = cv2.filter2D(img_gray,-1,kernel_y)
Ixx = Ix**2
Ixy = Ix*Iy
Iyy = Iy**2
#cv2.imshow("Ixx",Ixx)
#cv2.imshow("Iyy Image",Iyy)
#cv2.imshow("Ixy Image",Ixy)

# Loop through image and find our corners
k = 0.05

height = img_gray.shape[0]
width = img_gray.shape[1]
harris_response = []
window_size = 6
offset = int(window_size/2)
for y in range(offset, height-offset):
    for x in range(offset, width-offset):
        Sxx = np.sum(Ixx[y-offset:y+1+offset, x-offset:x+1+offset])
        Syy = np.sum(Iyy[y-offset:y+1+offset, x-offset:x+1+offset])
        Sxy = np.sum(Ixy[y-offset:y+1+offset, x-offset:x+1+offset])

        # Find determinant and trace, use to get corner response
        det = (Sxx * Syy) - (Sxy ** 2)
        trace = Sxx + Syy
        r = det - k * (trace ** 2)

        harris_response.append([x, y, r])
img_copy = np.copy(img)
thresh = 500
#sift = cv2.xfeatures2d.SIFT_create()
#kp,dc = sift.compute(img,None)
for response in harris_response:
    x, y, r = response
    if r > thresh:
        img_copy[y, x] = [255, 0, 0]

plt.imshow(img_copy)
cv2.waitKey(0)
plt.show()