def compute_and_save_K_Rt():
H = compute_homography()
K = camera.my_calibration((2048, 1152))
cam1, box = compute_camera_matrix_1(K, H)
cam2 = compute_camera_matrix_2(cam1, K, box, H)
with open('../../data/ar_camera_mag.pkl', 'wb') as f:
pickle.dump(K, f)
pickle.dump(dot(linalg.inv(K), cam2.P), f)
def compute_and_save_K_Rt():
H = compute_homography()
K = camera.my_calibration((2048, 1152))
cam1, box = compute_camera_matrix_1(K, H)
cam2 = compute_camera_matrix_2(cam1, K, box, H)
with open('../../data/ar_camera_mag.pkl','wb') as f:
pickle.dump(K,f)
pickle.dump(dot(linalg.inv(K),cam2.P),f)
def get_krt(im1, im2):
ims = [im1, im2]
sifts = []
for x in range(2):
sifts.append(ims[x][:-3]+"sift")
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
sift.process_image(ims[0],sifts[0])
l0,d0 = sift.read_features_from_file(sifts[0])
#sift.process_image('../../data/book_perspective.JPG','../../data/im1.sift')
sift.process_image(ims[1],sifts[1])
l1,d1 = sift.read_features_from_file(sifts[1])
# match features and estimate homography
matches = sift.match_twosided(d0,d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx,:2].T)
ndx2 = [int(matches[i]) for i in ndx]
print len(ndx2)
tp = homography.make_homog(l1[ndx2,:2].T)
model = homography.RansacModel()
H,ransac_data = homography.H_from_ransac(fp,tp,model)
# camera calibration
#K = camera.my_calibration((747,1000))
K = camera.my_calibration((Image.open(im2).size))
# 3D points at plane z=0 with sides of length 0.2
box = cube.cube_points([0,0,0.1],0.1)
# project bottom square in first image
cam1 = camera.Camera( hstack((K,dot(K,array([[0],[0],[-1]])) )) )
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:,:5]))
# use H to transfer points to the second image
print dot(H,box_cam1)
box_trans = homography.normalize(dot(H,box_cam1))
# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H,cam1.P))
A = dot(linalg.inv(K),cam2.P[:,:3])
A = array([A[:,0],A[:,1],cross(A[:,0],A[:,1])]).T
cam2.P[:,:3] = dot(K,A)
# project with the second camera
box_cam2 = cam2.project(homography.make_homog(box))
# test: projecting point on z=0 should give the same
point = array([1,1,0,1]).T
print homography.normalize(dot(dot(H,cam1.P),point))
print cam2.project(point)
import pickle
with open('%s.pkl' % ims[1][:-4],'w') as f:
pickle.dump(K,f)
pickle.dump(dot(linalg.inv(K),cam2.P),f)
sys.stderr.write("K and Rt dumped to %s.pkl\n" % ims[1][:-4])
def get_krt(im1, im2):
ims = [im1, im2]
sifts = []
for x in range(2):
sifts.append(ims[x][:-3] + "sift")
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
sift.process_image(ims[0], sifts[0])
l0, d0 = sift.read_features_from_file(sifts[0])
#sift.process_image('../../data/book_perspective.JPG','../../data/im1.sift')
sift.process_image(ims[1], sifts[1])
l1, d1 = sift.read_features_from_file(sifts[1])
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
print len(ndx2)
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, ransac_data = homography.H_from_ransac(fp, tp, model)
# camera calibration
#K = camera.my_calibration((747,1000))
K = camera.my_calibration((Image.open(im2).size))
# 3D points at plane z=0 with sides of length 0.2
box = cube.cube_points([0, 0, 0.1], 0.1)
# project bottom square in first image
cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]])))))
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:, :5]))
# use H to transfer points to the second image
print dot(H, box_cam1)
box_trans = homography.normalize(dot(H, box_cam1))
# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H, cam1.P))
A = dot(linalg.inv(K), cam2.P[:, :3])
A = array([A[:, 0], A[:, 1], cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = dot(K, A)
# project with the second camera
box_cam2 = cam2.project(homography.make_homog(box))
# test: projecting point on z=0 should give the same
point = array([1, 1, 0, 1]).T
print homography.normalize(dot(dot(H, cam1.P), point))
print cam2.project(point)
import pickle
with open('%s.pkl' % ims[1][:-4], 'w') as f:
pickle.dump(K, f)
pickle.dump(dot(linalg.inv(K), cam2.P), f)
sys.stderr.write("K and Rt dumped to %s.pkl\n" % ims[1][:-4])
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]
x2 = homography.make_homog(l[1][ndx2, :2].T)
print '{} matches'.format(len(ndx))
image = [numpy.array(Image.open(name)) for name in imname]
# calibration (FIXME?)
K = camera.my_calibration(image[0].shape[:2])
# Normalize with inv(K) (allows metric reconstruction).
x1n = numpy.dot(numpy.linalg.inv(K), x1)
x2n = numpy.dot(numpy.linalg.inv(K), x2)
tic.k('normalized')
# Estimate E.
model = sfm.RansacModel()
# Note that x2n is passed as first parameter, since F_from_ransac() and friends
# compute the F matrix mapping from the 2nd parameter to the first, and the
# code below gives camera 1 the identity transform.
E, inliers = sfm.F_from_ransac(x2n, x1n, model)
tic.k('ransacd')
#sift.plot_matches(im0, im1, l0, l1, matches, show_below=False) #show() ndx = matches.nonzero()[0] fp = homography.make_homog(l0[ndx, :2].T) ndx2 = [int(matches[i]) for i in ndx] tp = homography.make_homog(l1[ndx2, :2].T) #model = homography.RansacModel() #H = homography.H_from_ransac(fp, tp, model) # Not enough matches for H_from_ransac(), but all matches happen to be correct # anyways, so no need for that. H = homography.H_from_points(fp, tp) K = camera.my_calibration(im0.shape[:2]) # How big this appears depends on the z translation in cam1 in the cam1 line. box = cube_points([0, 0, 0.1], 0.1) # project bottom square in first image cam1 = camera.Camera(numpy.hstack((K, numpy.dot(K, array([[0], [0], [-1]]))))) box_cam1 = cam1.project(homography.make_homog(box[:, :5])) # transfer points to second image box_trans = homography.normalize(numpy.dot(H, box_cam1)) # compute second camera matrix cam2 = camera.Camera(numpy.dot(H, cam1.P)) # H * P transforms points in z = 0 correctly, so its first two colums are
#gray() #sift.plot_matches(im0, im1, l0, l1, matches, show_below=False) #show() ndx = matches.nonzero()[0] fp = homography.make_homog(l0[ndx, :2].T) ndx2 = [int(matches[i]) for i in ndx] tp = homography.make_homog(l1[ndx2, :2].T) #model = homography.RansacModel() #H = homography.H_from_ransac(fp, tp, model) # Not enough matches for H_from_ransac(), but all matches happen to be correct # anyways, so no need for that. H = homography.H_from_points(fp, tp) K = camera.my_calibration(im0.shape[:2]) # How big this appears depends on the z translation in cam1 in the cam1 line. box = cube_points([0, 0, 0.1], 0.1) # project bottom square in first image cam1 = camera.Camera(numpy.hstack((K, numpy.dot(K, array([[0], [0], [-1]]))))) box_cam1 = cam1.project(homography.make_homog(box[:, :5])) # transfer points to second image box_trans = homography.normalize(numpy.dot(H, box_cam1)) # compute second camera matrix cam2 = camera.Camera(numpy.dot(H, cam1.P)) # H * P transforms points in z = 0 correctly, so its first two colums are
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]
x2 = homography.make_homog(l[1][ndx2, :2].T)
print '{} matches'.format(len(ndx))
image = [numpy.array(Image.open(name)) for name in imname]
# calibration (FIXME?)
K = camera.my_calibration(image[0].shape[:2])
# Normalize with inv(K) (allows metric reconstruction).
x1n = numpy.dot(numpy.linalg.inv(K), x1)
x2n = numpy.dot(numpy.linalg.inv(K), x2)
tic.k('normalized')
# Estimate E.
model = sfm.RansacModel()
# Note that x2n is passed as first parameter, since F_from_ransac() and friends
# compute the F matrix mapping from the 2nd parameter to the first, and the
# code below gives camera 1 the identity transform.
E, inliers = sfm.F_from_ransac(x2n, x1n, model)
tic.k('ransacd')
