harris 算法python实现

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)

版权声明：本文博客原创文章，博客，未经同意，不得转载。