SUSAN算子python实现

好的，以下是一个简单的SUSAN算子的Python实现：

import cv2
import numpy as np

def susan_corner_detection(image, threshold=27):
    # 定义SUSAN算子中的常量参数
    mask = np.array([[1,1,1],
                     [1,1,1],
                     [1,1,1]], dtype=np.uint8)
    threshold_difference = 27
    max_corners = 200
    radius = 3

    # 对图像进行灰度化和高斯平滑
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (3,3), 0)

    # 使用SUSAN算子检测角点
    corners = cv2.cornerHarris(gray, 2, 3, 0.04)
    corners[corners < threshold] = 0

    # 使用SUSAN算子检测角点的邻域点
    susan_corners = cv2.filter2D(gray, -1, mask)
    susan_corners = np.logical_and(susan_corners < gray - threshold_difference, gray > threshold_difference)
    susan_corners = np.sum(susan_corners, axis=2)

    # 合并角点和邻域点
    corners = corners * susan_corners
    corners = np.array(corners)
    corners = corners.astype(np.uint8)

    # 获取前N个最强的角点
    corner_points = []
    for i in range(max_corners):
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(corners)
        if max_val == 0:
            break
        corners[max_loc[1]-radius:max_loc[1]+radius+1, max_loc[0]-radius:max_loc[0]+radius+1] = 0
        corner_points.append(max_loc)

    return corner_points

该算法使用 OpenCV 库实现，输入参数为一幅彩色图像和一个阈值，输出为检测到的角点坐标列表。其中SUSAN算子中的常量参数可以根据需要进行调整。