CPDA角点检测算法

CPDA角点检测算法的python实现
CSS角点检测算法的python实现

使用Python实现CPDA算法的代码

import cv2
import numpy as np

def cpda(image):
    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Calculate the gradient in x and y direction using Sobel filter
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
    
    # Calculate the magnitude of the gradient
    mag = np.sqrt(sobelx**2 + sobely**2)
    
    # Threshold the magnitude image to obtain binary image
    threshold = np.max(mag) * 0.1
    binary = np.zeros_like(mag)
    binary[mag > threshold] = 1
    
    # Apply morphological operations to remove noise
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
    closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
    opened = cv2.morphologyEx(closed, cv2.MORPH_OPEN, kernel)
    
    # Find the corners using the Harris corner detector
    corners = cv2.cornerHarris(opened, blockSize=2, ksize=3, k=0.04)
    corners = cv2.dilate(corners, None)
    
    # Threshold the corner image to obtain binary image
    threshold = np.max(corners) * 0.1
    binary = np.zeros_like(corners)
    binary[corners > threshold] = 1
    
    # Return the corner locations as a list of (x,y) tuples
    return list(zip(*np.nonzero(binary)[::-1]))

# Load an image
image = cv2.imread("image.jpg")

# Detect the corners using CPDA algorithm
corners = cpda(image)

# Draw the corners on the image
for x, y in corners:
    cv2.circle(image, (x,y), 5, (0,0,255), -1)

# Display the image
cv2.imshow("Corners", image)
cv2.waitKey(0)
cv2.destroyAllWindows()


```python

```