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这篇文章主要介绍了python中Harris角点检测的示例分析，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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1、基本思想
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。
3、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)感谢你能够认真阅读完这篇文章，希望小编分享的“python中Harris角点检测的示例分析”这篇文章对大家有帮助，同时也希望大家多多支持创新互联，关注创新互联行业资讯频道，更多相关知识等着你来学习!