harris 最常用作特征检测算法。

第一个文件harris.py

<pre name="code" class="python">from scipy.ndimage import filters

from numpy import *

from pylab import *

def compute_harris_response(im,sigma=3):

    imx=zeros(im.shape)#计算导数

    filters.gaussian_filter(im,(sigma,sigma),(0,1),imx)

    imy=zeros(im.shape)

    filters.gaussian_filter(im,(sigma,sigma),(1,0),imy)

    Wxx=filters.gaussian_filter(imx*imx,sigma)

   #计算harris矩阵分量

    Wxy=filters.gaussian_filter(imx*imy,sigma)

    Wyy=filters.gaussian_filter(imy*imy,sigma)

    Wdet=Wxx*Wyy-Wxy**2    #计算矩阵的特征值和迹

    Wtr=Wxx+Wyy

    return  Wdet/Wtr

def get_harris_points(harrisim,min_dist=10,threshold=0.1):

    conner_threshold=harrisim.max()*threshold

    harrisim_t=(harrisim>conner_threshold)*1

    coords=array(harrisim_t.nonzero()).T

    candidate_values=[harrisim[c[0],c[1]] for c in coords]

    index=argsort(candidate_values)

    allowed_locations=zeros(harrisim.shape)

    allowed_locations[min_dist:-min_dist,min_dist:-min_dist]=1

    filtered_coords=[]

    for i in index:

        if allowed_locations[coords[i,0],coords[i,1]]==1:

            filtered_coords.append(coords[i])

            allowed_locations[(coords[i,0]-min_dist):(coords[i,0]+min_dist),(coords[i,1]-min_dist):(coords[i,1]+min_dist)]=0#此处保证min_dist*min_dist仅仅有一个harris特征点

    return filtered_coords

def plot_harris_points(image,filtered_coords):

    figure()

    gray()

    imshow(image)

    plot([p[1] for p in filtered_coords],[p[0]for p in filtered_coords],'+')

    axis('off')

    show()

第二个文件測试算法

from PIL import Image

from numpy import *

import harris

from pylab import *

from scipy.ndimage import filters

im=array(Image.open('33.jpg').convert('L'))

harrisim=harris.compute_harris_response(im)

filtered_coords=harris.get_harris_points(harrisim)

harris.plot_harris_points(im,filtered_coords)