根据python使用harris写一个角点检测的findcorners代码
时间: 2024-05-02 07:18:03 浏览: 15
以下是使用Python实现Harris角点检测算法的示例代码:
```python
import cv2
import numpy as np
def find_corners(image):
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Calculate the Harris corner response function
harris = cv2.cornerHarris(gray, 2, 3, 0.04)
# Threshold the corner response function
threshold = 0.01 * harris.max()
harris[harris < threshold] = 0
# Dilate the corner points to make them more visible
kernel = np.ones((5,5), np.uint8)
harris = cv2.dilate(harris, kernel)
# Find the coordinates of the corners
corners = np.argwhere(harris != 0)
corners = np.float32(corners)
# Apply the Shi-Tomasi corner detection algorithm to refine the corners
corners = cv2.cornerSubPix(gray, corners, (5,5), (-1,-1), criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1))
# Draw circles around the detected corners
for corner in corners:
x, y = corner.ravel()
cv2.circle(image, (x, y), 3, (0,255,0), -1)
# Display the image with the detected corners
cv2.imshow('Corners', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Load an image and call the find_corners function
image = cv2.imread('image.jpg')
find_corners(image)
```
该函数将图像转换为灰度图像,然后使用cv2.cornerHarris计算Harris角点响应函数。接下来,该函数对角点响应函数进行阈值处理,并使用cv2.dilate函数将角点点扩张。然后,该函数使用cv2.cornerSubPix函数对角点进行微调,并在检测到的角点周围绘制圆圈。最后,使用cv2.imshow显示带有检测到的角点的图像。