import sift from PIL import Image import numpy as np from pylab import * import imtools close('all') imname = 'lena.png' im1 = np.array(Image.open(imname).convert('L')) im2 = np.array(Image.open(imname).convert('L')) sift.process_image(imname,'lena1.sift') l1,d1 = sift.read_features_from_file('lena1.sift') figure() gray() sift.plot_features(im1,l1,circle=True) show() ######################### figure() sift.process_image(imname,'lena2.sift') l2,d2 = sift.read_features_from_file('lena2.sift') print 'strarting matching' matches = sift.match_twosided(d1,d2) gray() imtools.plot_matches(im1,im2,l1, l2, matches) show()
im = np.array(Image.open('lena.png').convert('L')) harrisim = imtools.compute_harris_response(im) filtered_coords = imtools.get_harris_points(harrisim,6,threshold = 0.05) imtools.plot_harris_points(im, filtered_coords) ## matching figure() wid = 5 im1 = np.array(Image.open('lena.png').convert('L')) im2 = np.array(Image.open('lena.png').convert('L')) harrisim = imtools.compute_harris_response(im1,5) filtered_coords1 = imtools.get_harris_points(harrisim,wid+1) d1 = imtools.get_descriptors(im1,filtered_coords1,wid) harrisim = imtools.compute_harris_response(im2,5) filtered_coords2 = imtools.get_harris_points(harrisim,wid+1) d2 = imtools.get_descriptors(im2,filtered_coords2,wid) print 'strarting matching' matches = imtools.match_twosided(d1,d2) gray() imtools.plot_matches(im1,im2,filtered_coords1, filtered_coords2, matches[:100]) show()