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創(chuàng)新互聯(lián)Python教程：python中Harris角點(diǎn)檢測
基本思想
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1、選擇在圖像上任意方向的固定窗口進(jìn)行滑動(dòng)，如果灰度變化較大，則認(rèn)為該窗口內(nèi)部存在角點(diǎn)。
2、步驟，讀圖并將其轉(zhuǎn)換為灰度圖。估計(jì)響應(yīng)函數(shù)。根據(jù)響應(yīng)值選擇角度。畫出原始圖上的檢測角點(diǎn)。
實(shí)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 *
# 這就是為啥上述要新建一個(gè)的原因，因?yàn)楝F(xiàn)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角點(diǎn)檢測的方法，希望對大家有所幫助。更多Python學(xué)習(xí)指路：創(chuàng)新互聯(lián)python教程
本文教程操作環(huán)境：windows7系統(tǒng)、Python 3.9.1，DELL G3電腦。
本文名稱：創(chuàng)新互聯(lián)Python教程：python中Harris角點(diǎn)檢測
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