import glob
import os
import homography
import sift
import tic
import warp
imname = glob.glob('out_Photos/IMG_*.jpg')
siftname = [os.path.splitext(im)[0] + '.sift' for im in imname]
tic.k('start')
l, d = {}, {}
for i in range(len(imname)):
l[i], d[i] = sift.read_or_compute(imname[i], siftname[i])
tic.k('loaded')
matches = {}
if not os.path.exists('out_ch03_pano.pickle'):
for i in range(len(imname) - 1):
matches[i] = sift.match(d[i + 1], d[i])
# Slightly better matches, but ransac can handle the worse quality:
#matches[i] = sift.match_twosided(d[i + 1], d[i])
pickle.dump(matches, open('out_ch03_pano.pickle', 'wb'))
matches = pickle.load(open('out_ch03_pano.pickle', 'rb'))
tic.k('matched')
p.append([c[0] + wid, c[1] - wid, c[2] + wid])
p.append([c[0] - wid, c[1] - wid, c[2] + wid])
# sides
p.append([c[0] - wid, c[1] - wid, c[2] + wid])
p.append([c[0] - wid, c[1] + wid, c[2] + wid])
p.append([c[0] - wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] - wid])
return numpy.array(p).T
l0, d0 = sift.read_or_compute('out_ch4pics/h_template.jpg',
'out_ch4pics/h_template.sift')
l1, d1 = sift.read_or_compute('out_ch4pics/h_image.jpg',
'out_ch4pics/h_image.sift')
#figure()
#gray()
im0 = array(Image.open('out_ch4pics/h_template.jpg'))
#sift.plot_features(im0, l0, circle=True)
#
#figure()
#gray()
im1 = array(Image.open('out_ch4pics/h_image.jpg'))
#sift.plot_features(im1, l1, circle=True)
#show()
if not os.path.exists('out_ch04_markerpose.pickle'):
imname = glob.glob('out_corner/IMG_*.jpg')
#imname = glob.glob('out_alcatraz/*.jpg')
siftname = [os.path.splitext(im)[0] + '.sift' for im in imname]
tic.k('start')
# For out_corner, this increases feature count from 4k to 16k and matches from
# 100 to 170 (which helps quality, but also slows down the program a lot, from
# from 20s to 60s):
# (with twosided matching, matches go from 85 to 113 for out_corner)
# NOTE: delete caches after changing this!
histeq = False
l, d = {}, {}
for i in range(len(imname)):
l[i], d[i] = sift.read_or_compute(imname[i], siftname[i], histeq)
tic.k('loaded sifts')
print '{} / {} features'.format(len(d[0]), len(d[1]))
if not os.path.exists('out_ch05_recover_match.pickle'):
#matches = sift.match(d[0], d[1])
matches = sift.match_twosided(d[0], d[1])
pickle.dump(matches, open('out_ch05_recover_match.pickle', 'wb'))
matches = pickle.load(open('out_ch05_recover_match.pickle', 'rb'))
tic.k('matched')
ndx = matches.nonzero()[0]
x1 = homography.make_homog(l[0][ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
p.append([c[0] + wid, c[1] - wid, c[2] + wid])
p.append([c[0] - wid, c[1] - wid, c[2] + wid])
# sides
p.append([c[0] - wid, c[1] - wid, c[2] + wid])
p.append([c[0] - wid, c[1] + wid, c[2] + wid])
p.append([c[0] - wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] - wid])
return numpy.array(p).T
l0, d0 = sift.read_or_compute('out_ch4pics/h_template.jpg',
'out_ch4pics/h_template.sift')
l1, d1 = sift.read_or_compute('out_ch4pics/h_image.jpg',
'out_ch4pics/h_image.sift')
#figure()
#gray()
im0 = array(Image.open('out_ch4pics/h_template.jpg'))
#sift.plot_features(im0, l0, circle=True)
#
#figure()
#gray()
im1 = array(Image.open('out_ch4pics/h_image.jpg'))
#sift.plot_features(im1, l1, circle=True)
#show()
if not os.path.exists('out_ch04_markerpose.pickle'):