OpenCV角点检测的实现示例

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import filters

## 1.找出角点
### 1.1 步骤：img求导 --> imx ,imy 滤波-->Wxx ,Wxy,Wyy --> （Wxx*Wyy - Wxy**2）/（Wxx + Wyy）
def compute_harris_response(img,sigma=3):
    # 求梯度
    imgx,imgy  = np.zeros(img.shape),np.zeros(img.shape)
    filters.gaussian_filter(img,(sigma,sigma),(0,1),imgx)
    filters.gaussian_filter(img,(sigma,sigma),(1,0),imgy) # [260,263]
    # 对梯度进行高斯滤波
    wxx = filters.gaussian_filter(imgx**2,sigma)
    wyy = filters.gaussian_filter(imgy**2,sigma) # [260,263]
    wxy = filters.gaussian_filter(imgx*imgy,sigma)# [260,263]
    ## 求行列式和迹
    wdet = wxx*wyy -wxy**2
    wtr = wxx + wyy
    return  wdet/wtr

## 2 筛选角点
### 2.1 步骤：根据阈值筛选角点--> 取得角点的坐标 -->根据角点坐标得到角点所在的行  -->
# --> 在角点周围，删除掉其他角点
def get_harris_points(harri,min_dist=4,threshold=0.1):
    corner_thre = harri.max()*threshold  # 角点阈值
    mask = (harri > corner_thre)*1  # 取出大于阈值的点为候选角点
    cords = np.array(mask.nonzero()).T  # 取候选角点的坐标

    values = [harri[i[0],i[1]] for i in cords]  # 候选角点的值
    cls = np.argsort(values)     # 对角点排序得到排序后的序列号，序列号也是候选角点所在的行

    loc = np.zeros(harri.shape)   # 划出可行性区域
    loc[min_dist:-min_dist,min_dist:-min_dist] = 1

    re_cords = []
    for i in cls:  # 筛选角点。先取出角点，角点周围的点不再取出
        if loc[cords[i,0],cords[i,1]] == 1 :
            re_cords.append(cords[i])
            loc[cords[i,0]-min_dist:cords[i,0]+min_dist,cords[i,1]-min_dist:cords[i,1]+min_dist]=0
    return re_cords

def plot_harri(img,cords):
    plt.figure()
    plt.gray()
    plt.imshow(img)
    plt.plot([i[1] for i in cords],[i[0] for i in cords],'.')
    plt.axis('off')
    plt.show()

## 3 测试
if __name__ == '__main__':
    img = np.array(Image.open('luna.png').convert('L'))
    harri = compute_harris_response(img)
    re_cords = get_harris_points(harri)
    plot_harri(img,re_cords)