python培训之Harris角点检测

　　python中Harris角点检测

　　基本思想

　　1、选择在图像上任意方向的固定窗口进行滑动，如果灰度变化较大，则认为该窗口内部存在角点。

　　2、步骤，读图并将其转换为灰度图。估计响应函数。根据响应值选择角度。画出原始图上的检测角点。

　　实例

　　from pylab import *

　　from numpy import *

　　from scipy.ndimage import filters

　　def compute_harris_response(im,sigma=3):

　　""" Compute the Harris corner detector response function

　　for each pixel in a graylevel image. """

　　# derivatives

　　imx = zeros(im.shape)

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

　　imy = zeros(im.shape)

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

　　# compute components of the Harris matrix

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

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

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

　　# determinant and trace

　　Wdet = Wxx*Wyy - Wxy**2

　　Wtr = Wxx + Wyy

　　return Wdet / Wtr

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

　　""" Return corners from a Harris response image

　　min_dist is the minimum number of pixels separating

　　corners and image boundary. """

　　# find top corner candidates above a threshold

　　corner_threshold = harrisim.max() * threshold

　　harrisim_t = (harrisim > corner_threshold) * 1

　　# get coordinates of candidates

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

　　# ...and their values

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

　　# sort candidates (reverse to get descending order)

　　index = argsort(candidate_values)[::-1]

　　# store allowed point locations in array

　　allowed_locations = zeros(harrisim.shape)

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

　　# select the best points taking min_distance into account

　　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

　　return filtered_coords

　　def plot_harris_points(image,filtered_coords):

　　""" Plots corners found in image. """

　　figure()

　　gray()

　　imshow(image)

　　plot([p[1] for p in filtered_coords],

　　[p[0] for p in filtered_coords],'*')

　　axis('off')

　　show()

　　from PIL import Image

　　from numpy import *

　　# 这就是为啥上述要新建一个的原因，因为现在就可以import

　　import Harris_Detector

　　from pylab import *

　　from scipy.ndimage import filters

　　# filename

　　im = array(Image.open(r" ").convert('L'))

　　harrisim=Harris_Detector.compute_harris_response(im)

　　filtered_coords=Harris_Detector.get_harris_points(harrisim)

　　Harris_Detector.plot_harris_points(im,filtered_coords)

　　以上就是python中Harris角点检测的方法，希望对大家有所帮助。更多Python学习指路：请关注多测师。https://www.e70w.com/xwzx/