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_H(im0_path, im1_path):
"""
Get the Homography matrix.
"""
# compute features
sift.process_image(im0_path, 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image(im1_path, 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# 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]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H = homography.H_from_ransac(fp, tp, model)[0]
return H
def compute_homography():
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
im1 = '../../data/space_front.jpg'
im2 = '../../data/space_perspective.jpg'
im1 = '../../data/mag_front.jpg'
im2 = '../../data/mag_perspective.jpg'
ims = [im1, im2]
sifts = []
for k in range(2):
sifts.append(ims[k][:-4] + ".sift")
l0, d0 = sift.read_features_from_file(sifts[0])
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]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, ransac_data = homography.H_from_ransac(fp, tp, model)
return H
#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')
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
return fp, tp
model = homography.RansacModel()
fp, tp = convert_points(1)
H_12 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('12 homogd')
fp, tp = convert_points(0)
H_01 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('01 homogd')
tp, fp = convert_points(2) # Note: Reversed.
H_32 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('32 homogd')
tp, fp = convert_points(3) # Note: Reversed.
H_43 = homography.H_from_ransac(fp, tp, model)[0]
m_ORB = cv2.ORB_create()
kp1, des1 = m_ORB.detectAndCompute(G_img1, None)
kp2, des2 = m_ORB.detectAndCompute(G_img2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(
matches,
key=lambda x: x.distance) # Sort them in the order of their distance.
obj1, obj2 = Cli.Manage_data(matches, kp1, kp2)
model = h**o.RansacModel()
H = h**o.H_from_ransac(obj1, obj2, model)
# # Draw first 60 matches.
# img3 = img2
# img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:60], img3)
# cv2.imshow('result', img3)
# cv2.imwrite('result.jpg', img3)
# cv2.waitKey()
# 位于边长为0.2， z = 0平面上底部的正方形
Box = cube_points([0, 0, 0.1], 0.1)
cam1 = Cli.Camera(
