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])
def get_camera_params(sz, H):
"""
Get camere parametes: K, Rts.
"""
# camera calibration
K = my_calibration((sz))
#print(K)
# 3D points at plane z=0 with sides of length 0.2
box = cube_points([0, 0, 0.1], 0.1)
# project bottom square in first image
cam1 = Camera(np.hstack((K, np.dot(K, np.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
box_trans = homography.normalize(np.dot(H, box_cam1))
# compute second camera metrx from cam1 and H
cam2 = Camera(np.dot(H, cam1.P))
A = np.dot(linalg.inv(K), cam2.P[:, :3])
A = np.array([A[:, 0], A[:, 1], np.cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = np.dot(K, A)
Rt = np.dot(linalg.inv(K), cam2.P)
return K, Rt
def compute_camera_matrix_1(K, H):
# 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
box_trans = homography.normalize(dot(H,box_cam1))
return cam1, box
def compute_camera_matrix_1(K, H):
# 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
box_trans = homography.normalize(dot(H, box_cam1))
return cam1, box
def compute_camera_matrix_2(cam1, K, box, H):
# 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))
#H is correct as seen by the plots but cam2.P does not eem to be correct
#test cam2.P
return cam2
def compute_camera_matrix_2(cam1, K, box, H):
# 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))
#H is correct as seen by the plots but cam2.P does not eem to be correct
#test cam2.P
return cam2
# 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 # correct. The upper left 3x3 submatrix should have orthogonal vectors, use # this fact to reconstruct the third (H contains a scale, so this is only # well-defined up to scale of the third axis. Fudge something up.) A = numpy.dot(numpy.linalg.inv(K), cam2.P[:, :3]) lenx = numpy.sqrt(numpy.dot(A[:, 0], A[:, 0])) leny = numpy.sqrt(numpy.dot(A[:, 1], A[:, 1])) #print lenx, leny # Should be similar. zscale = (lenx + leny) / (2 * lenx * leny) A = numpy.array([A[:, 0], A[:, 1], zscale * numpy.cross(A[:, 0], A[:, 1])]).T cam2.P[:, :3] = numpy.dot(K, A)
#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 # correct. The upper left 3x3 submatrix should have orthogonal vectors, use # this fact to reconstruct the third (H contains a scale, so this is only # well-defined up to scale of the third axis. Fudge something up.) A = numpy.dot(numpy.linalg.inv(K), cam2.P[:, :3]) lenx = numpy.sqrt(numpy.dot(A[:, 0], A[:, 0])) leny = numpy.sqrt(numpy.dot(A[:, 1], A[:, 1])) #print lenx, leny # Should be similar. zscale = (lenx + leny) / (2 * lenx * leny) A = numpy.array([A[:, 0], A[:, 1], zscale * numpy.cross(A[:, 0], A[:, 1])]).T cam2.P[:, :3] = numpy.dot(K, A)
p.append([c[0] + wid, c[1] - wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] - wid])
return np.array(p).T
#%%
box = cube_points([0, 0, 0.1], 0.2)
''' Camera 1 at position 0 '''
P1 = np.hstack((K, np.dot(K, np.array([[0], [0], [-1]]))))
cam1 = camera.Camera(P1)
''' Project cube (first points are the bottom square) '''
box_P1 = homography.make_homog(box[:, :5])
box_P1 = cam1.project(box_P1)
''' Transfer points from P1 to P2 using homography '''
box_trans = np.dot(H, box_P1)
box_trans = homography.normalize(box_trans)
''' Compute P2 '''
P2 = np.dot(H, cam1.P)
cam2 = camera.Camera(P2)
A = np.dot(linalg.inv(K), cam2.P[:, :3])
A = np.array([A[:, 0], A[:, 1], np.cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = np.dot(K, A)
''' Project with the second camera '''
box_P2 = homography.make_homog(box)
box_P2 = cam2.project(box_P2)
#%%
''' Plot projections '''
# 2D projection of bottom square
plt.figure(1)
plt.imshow(im1)
model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model) #删除误匹配
# camera calibration
K = my_calibration((747, 1000)) #相机焦距光圈初始化
# 3D points at plane z=0 with sides of length 0.2
box = 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]]))))) #相机初始化
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:, :5])) #在相机中的坐标
# use H to transfer points to the second image
box_trans = homography.normalize(dot(H, box_cam1)) #单应性矩阵的变换
# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H, cam1.P)) #相机的外参数
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) #照相机矩阵的变换
# project with the second camera
box_cam2 = cam2.project(
homography.make_homog(box)) #得到cube在第二幅图中的形状 通过照相机矩阵的变换
# plotting
im0 = array(Image.open('book_frontal.JPG'))
im1 = array(Image.open('book_perspective.JPG'))
figure()
# 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(
numpy.hstack((K, numpy.dot(K, numpy.array([[0], [0], [-1]])))))
box_cam1 = cam1.project(h**o.make_homog(Box[:, :5]))
# 使用H将点变换到第二幅图像中
box_trans = h**o.normalize(numpy.dot(H[0], box_cam1))
# 从cam1和H中计算第二个照相机矩阵
cam2 = Cli.Camera(numpy.dot(H[0], cam1.P))
A = numpy.dot(numpy.linalg.inv(K), cam2.P[:, :3])
A = numpy.array([A[:, 0], A[:, 1], numpy.cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = numpy.dot(K, A)
# 使用第二个照相机矩阵投影
box_cam2 = cam2.project(h**o.make_homog(Box))
# 测试：将点投影在Z=0上，应该能够得到相同点
point = numpy.array([[1, 1, 0, 1]]).T
print h**o.normalize(numpy.dot(numpy.dot(H[0], cam1.P), point)).T
print h**o.normalize(numpy.dot(H[0], cam1.project(point))).T
print cam2.project(point).T
# 最初の点群は、底面の正方形
pts = homography.make_homog(box[:, :5])
box_cam1 = cam1.project(pts)
# -
box_cam1
img_for_edit=img_query.copy()
for i in range(5):
circle_pt=tuple([int(pt) for pt in box_cam1[:2,i]])
cv2.circle(img_for_edit,circle_pt,50,(255,255,255),thickness=-1)
ip.show_img(img_for_edit,show_axis=True)
# Hを使って第2の画像に点を変換する
box_trans = homography.normalize(homology_matrix @ box_cam1)
img_for_edit=img_train.copy()
for i in range(5):
circle_pt=tuple([int(pt) for pt in box_trans[:2,i]])
cv2.circle(img_for_edit,circle_pt,50,(255,255,255),thickness=-1)
ip.show_img(img_for_edit,show_axis=True)
# cam1とHから第2のカメラ行列を計算する
P2=homology_matrix @ cam1.P
cam2 = camera.Camera(P2)
cam2.P[:,:3]
# P=K (R|t)なので
#P[:,:3]は K R
# Note: always resize image to 747 x 1000 or change the K below
# camera calibration
K = my_calibration((480,640))
# 3D points at plane z=0 with sides of length 0.2
box = 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
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))
print(box_cam2)
delta_x = box_cam2[0][0] - px
import homography
import numpy as np
fp = []
tp = []
f = open("corresponding_points.txt")
for l in f:
if l.strip() == "":
continue
points = l.strip().split(",")
points = map(int, points)
fp.append([points[0], points[1], 1])
tp.append([points[2], points[3], 1])
fp = np.array(fp).transpose()
tp = np.array(tp).transpose()
H = homography.H_from_points(fp, tp)
print H
for i in xrange(len(fp.transpose())):
f = fp.transpose()[i]
transformed = H * np.matrix(f).transpose()
transformed = homography.normalize(transformed)
print[int(j) for j in transformed.flat]
print[int(j) for j in tp.transpose()[i].flat]
print "==================="
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, inliers = homography.H_from_ransac(fp, tp, model)
# camera calibration
K = my_calibration((747, 1000))
# 3D points at plane z=0 with sides of length 0.2
box = 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
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)
# plotting
im0 = array(Image.open('../pcv_data/data/book_frontal.JPG'))
im1 = array(Image.open('../pcv_data/data/book_frontal.JPG'))
# カメラのキャリブレーション K = my_calibration((747,1000)) # z=0の平面上の辺の長さ0.2の立方体の3Dの点 box = cube_points([0,0,0.1],0.1) # 第一の画像の底面の正方形を射影する cam1 = camera.Camera( np.hstack((K,np.dot(K,np.array([[0],[0],[-1]])) )) ) # 最初の点群は、底面の正方形 box_cam1 = cam1.project(homography.make_homog(box[:,:5])) # Hをつかって第2の画像に点を変換する box_trans = homography.normalize(np.dot(H,box_cam1)) # cam1とHから第2のカメラ行列を計算する cam2 = camera.Camera(np.dot(H,cam1.P)) A = np.dot(np.linalg.inv(K),cam2.P[:,:3]) A = np.array([A[:,0],A[:,1],np.cross(A[:,0],A[:,1])]).T cam2.P[:,:3] = np.dot(K,A) #第2のカメラ行列を使って射影する box_cam2 = cam2.project(homography.make_homog(box)) # テスト : z=0上の点において射影を変換すると同じになるはず point = np.array([1,1,0,1]).T print homography.normalize(np.array([np.dot(np.dot(H,cam1.P),point)]).T) print cam2.project(point)
model = homography.RansacModel() H = homography.H_from_ransac(fp, tp, model) # 计算相机标定矩阵 K = my_calibration((747, 1000)) # 位于边长为0.2, z=0平面上的三维点 box = cube_points([0, 0, 0.1], 0.1) # 投影第一幅图像上底部的正方形 cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]]))))) # 底部正方形上的点 box_cam1 = cam1.project(homography.make_homog(box[:, :5])) # 使用H将点变换到第二幅图像上 box_trans = homography.normalize(dot(H, box_cam1)) # 从cam1和H中计算第二个照相机矩阵 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) # 使用第二个照相机矩阵投影 box_cam2 = cam2.project(homography.make_homog(box)) # 测试: 将点投影在z=0上, 应该能够得到相同的点 point = array([1, 1, 0, 1]).T print homography.normalize(dot(dot(H, cam1.P), point)) print cam2.project(point)
#projection cube on the first bottom
cam1 = cam.Camera( hstack((K,dot(K,array([[0],[0],[-1]])) )) )
#points up the bottom
box_trans = homography,mormalize(dot(H,box_cam1))
#compute the second matrix from the cam1 and H
cam2 = cam.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)
#use the second matrix projection
box_cam2 = cam2.project(homography.make_homog(box))
#test:to projection on z =0 ,to get the same point
point = array([1,1,0,1]).T
print homography.normalize(dot(dot(H,cam1.P),point))
print cam2.project(point)
im0 = array(Image.open('/home/wangkai/Pictures/book_frontal.jpg'))
im1 = array(Image.open('/home/wangkai/Pictures/book_prespective.jpg'))
#bottom cube 2D projection
figure()
imshow(im0)
plot(box_cam1[0,:],box_cam1[1,:],linewidht=3)
#use H to trans the 2D projection
figure()
imshow(im1)
plot(box_trans[0,:], box_trans[1,:],linewidth=3)
#3D cube
figure()
