def cof():
im1 = np.array(Image.open(inpfilename).convert('L'))
im2 = np.array(Image.open(inpfilename).convert('L'))
wid = 5
harrisim = harris.compute_harris_response(im1,5)
filtered_coords1 = harris.get_harris_points(harrisim, wid+1)
d1 = harris.get_descriptors(im1, filtered_coords1,wid)
harrisim = harris.compute_harris_response(im2,5)
filtered_coords2 = harris.get_harris_points(harrisim, wid+1)
d2 = harris.get_descriptors(im2, filtered_coords2,wid)
print 'starting matching'
matches = harris.match_twosided(d1,d2)
print "matches", matches
figure()
gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
print "show the image"
show()
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 15 11:19:46 2016
@author: user
"""
from PIL import Image
from pylab import *
import harris
im = array(Image.open('../ch1/baby_16.jpg').convert('L'))
harrisim = harris.compute_harris_response(im)
filtered_coords = harris.get_harris_points(harrisim, 6)
harris.plot_harris_points(im, filtered_coords)
imshow(im)
wid = 5
im1 = array(Image.open('../ch1/baby_1.jpg').convert('L'))
harrisim = harris.compute_harris_response(im1, 5)
filtered_coords1 = harris.get_harris_points(harrisim, wid + 1)
d1 = harris.get_descriptors(im1, filtered_coords1, wid)
im2 = array(Image.open('../ch1/baby_16.jpg').convert('L'))
harrisim = harris.compute_harris_response(im2, 5)
filtered_coords2 = harris.get_harris_points(harrisim, wid + 1)
d2 = harris.get_descriptors(im2, filtered_coords2, wid)
print 'starting matching'
matches = harris.match_twosided(d1, d2)
figure()
gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
show()
print 'matching one way'
matches_12 = match_features(desc1, desc2)
print 'matching other way'
matches_21 = match_features(desc2, desc1)
print 'culling matches'
idx_12 = matches_12.nonzero()[0]
for i in idx_12:
if matches_21[int(matches_12[i])] != i:
matches_12[i] = 0
return matches_12
if __name__ == '__main__':
im1, im2 = [
process_image('data/crans_%d_small.jpg' % i, 'output/im%d.sift' % i)
for i in (1, 2)
]
l1, d1 = read_features_from_file('output/im1.sift')
l2, d2 = read_features_from_file('output/im2.sift')
matches = match_features(d1, d2)
print matches[:50]
pylab.figure()
pylab.gray()
harris.plot_matches(im1, im2, l1[:, :2], l2[:, :2], matches)
pylab.show()
t = time.time()
filtered_coords1 = harris.get_harris_points(harrisim, wid + 1)
print "t for get_harris_points=", time.time() - t
t = time.time()
d1 = harris.get_descriptors(im1, filtered_coords1, wid)
print "t for get_descriptors=", time.time() - t
t = time.time()
harrisim = harris.compute_harris_response(im2, 5)
filtered_coords2 = harris.get_harris_points(harrisim, wid + 1)
d2 = harris.get_descriptors(im2, filtered_coords2, wid)
print "starting matching"
matches = harris.match_twosided(d1, d2)
figure()
gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
show()
"""
im = array(Image.open('empire.jpg').convert('L'))
harrisim = harris.compute_harris_response(im)
filtered_coords = harris.get_harris_points(harrisim,6)
harris.plot_harris_points(im, filtered_coords)
"""
im1 = array(Image.open(imname1).convert('L'))
sift.process_image(imname1,'ti1.sift')
l1,d1 = sift.read_features_file('./ti1.sift')
im2 = array(Image.open(imname2).convert('L'))
sift.process_image(imname2,'ti2.sift')
l2,d2 = sift.read_features_file('./ti2.sift')
locs1 = l1[:,:2]
locs2 = l2[:,:2]
matchscore = sift.match_twoside(d1,d2)
print(locs1.shape)
print(locs2.shape)
print(matchscore.shape)
print(matchscore.max())
print(locs1[24,1])
#figure()
#sift.plot_feature(im1,l1,circle = True)
#show()
figure()
harris.plot_matches(im1,im2,locs1,locs2,matchscore)
show()
print "Running Harris Corner detection for", image1, "and", image2 print "For the following number of iterations:", num_runs else: print 'No image specified' print 'Run as follows: serial_mapping.py <image1.png> <image2.jpg>\n' # Run Harris Get Corners for Image 1 Harris_Matrix_1, serial_points_1, parallel_points_1, output_times_openCL_1, \ output_times_serial_1 = harris_get_corners(num_runs, image1) # Run Harris Get Corners for Image 1 Harris_Matrix_2, serial_points_2, parallel_points_2, output_times_openCL_2, \ output_times_serial_2 = harris_get_corners(num_runs, image2) # Run Parallel Matching and Sort with Parallel Harris corners match_parallel = parallel_match_sort(Harris_Matrix_1, Harris_Matrix_2, \ parallel_points_1, parallel_points_2) # Run Parallel Matching and Sort with Parallel Harris corners match_serial = serial_matching(Harris_Matrix_1, Harris_Matrix_2, \ serial_points_1, serial_points_2) # Plot Parallel Matches plot_matches(image1, image2, parallel_points_1, parallel_points_2, \ match_parallel, 'parallel_match.jpg') # Plot Serial Matches plot_matches(image1, image2, serial_points_1, serial_points_2, \ match_serial, 'serial_match.jpg')
from pylab import *
import harris
#import imtools
im1 = array(Image.open('/Users/taronegeorage/Desktop/111.jpg').convert('L'))
im2 = array(Image.open('/Users/taronegeorage/Desktop/111.jpg').convert('L'))
harrisim1 = harris.compute_harris_response(im1)
filtered_coords1 = harris.get_harris_points(harrisim1)
patches1 = harris.get_descriptors(im1, filtered_coords1)
harris.plot_harris_points(im1, filtered_coords1)
harrisim2 = harris.compute_harris_response(im2)
filtered_coords2 = harris.get_harris_points(harrisim2)
patches2 = harris.get_descriptors(im2, filtered_coords2)
harris.plot_harris_points(im2, filtered_coords2)
matches = harris.match_twosided(patches1, patches2)
figure()
gray()
harris.plot_matches(im1,
im2,
filtered_coords1,
filtered_coords2,
matches,
show_below=False)
show()
from PIL import Image
from pylab import *
import harris
import imtools
im1 = array(Image.open('/Users/thakis/src/PCV/data/sf_view1.jpg').convert('L'))
im2 = array(Image.open('/Users/thakis/src/PCV/data/sf_view2.jpg').convert('L'))
harrisim1 = harris.compute_harris_response(im1)
filtered_coords1 = harris.get_harris_points(harrisim1)
patches1 = harris.get_descriptors(im1, filtered_coords1)
harris.plot_harris_points(im1, filtered_coords1)
harrisim2 = harris.compute_harris_response(im2)
filtered_coords2 = harris.get_harris_points(harrisim2)
patches2 = harris.get_descriptors(im2, filtered_coords2)
harris.plot_harris_points(im2, filtered_coords2)
matches = harris.match_twosided(patches1, patches2)
figure()
gray()
harris.plot_matches(im1, im2,
filtered_coords1, filtered_coords2,
matches, show_below=False)
show()
def match_twosided(desc1, desc2):
print 'matching one way'
matches_12 = match_features(desc1, desc2)
print 'matching other way'
matches_21 = match_features(desc2, desc1)
print 'culling matches'
idx_12 = matches_12.nonzero()[0]
for i in idx_12:
if matches_21[int(matches_12[i])] != i:
matches_12[i] = 0
return matches_12
if __name__ == '__main__':
im1, im2 = [
process_image('data/crans_%d_small.jpg' % i, 'output/im%d.sift' % i)
for i in (1, 2)
]
l1, d1 = read_features_from_file('output/im1.sift')
l2, d2 = read_features_from_file('output/im2.sift')
matches = match_features(d1, d2)
print matches[:50]
pylab.figure()
pylab.gray()
harris.plot_matches(im1, im2, l1[:,:2], l2[:,:2], matches)
pylab.show()
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from harris import compute_harris_response, get_harris_points, \
get_descriptors, match_two_sided, plot_matches, match
img1 = np.array(Image.open('images/house.jpg').convert('L'))
img2 = np.array(Image.open('images/house2.jpg').convert('L'))
wid = 100
harris_img = compute_harris_response(img1, 5)
filtered_coords1 = get_harris_points(harris_img, wid + 1)
d1 = get_descriptors(img1, filtered_coords1, wid)
harris_img = compute_harris_response(img2, 5)
filtered_coords2 = get_harris_points(harris_img, wid + 1)
d2 = get_descriptors(img2, filtered_coords2, wid)
print('[INFO] Starting Matching ')
matches = match(d1, d2)
print('[INFO] match complete')
plt.figure()
plt.gray()
plot_matches(img1, img2, filtered_coords1, filtered_coords2, matches)
plt.show()
