def draw3d_reconstracted(self):
self.ax_rcn.cla()
# カメラ行列（内部・外部）
M1 = self.K.dot(self.P1)
M2 = self.K.dot(self.P2)
# 画像座標 ( sx, sy, s )
x1p = M1.dot(self.Xpt)
x2p = M2.dot(self.Xpt)
# sで正規化 (x, y, 1)
for i in range(3):
x1p[i] /= x1p[2]
x2p[i] /= x2p[2]
# 三角測量と正規化 (X,Y,Z,1)
X = sfm.triangulate(x1p, x2p, M1, M2)
self.ax_rcn.set_xlabel("x_wld_rcn")
self.ax_rcn.set_ylabel("y_wld_rcn")
self.ax_rcn.set_zlabel("z_wld_rcn")
self.ax_rcn.set_xlim([-5, 5])
self.ax_rcn.set_ylim([-5, 5])
self.ax_rcn.set_zlim([-5, 5])
self.ax_rcn.set_title("obj wld rcn")
for l in self.ll:
x = np.array([X[0][l[0]], X[0][l[1]]])
y = np.array([X[1][l[0]], X[1][l[1]]])
z = np.array([X[2][l[0]], X[2][l[1]]])
self.ax_rcn.plot(x, y, z, marker='.', markersize=5, color='red')
x2n = np.dot(np.linalg.inv(K), x2)
# estimate E with RANSAC
model = sfm.RansacModel()
E, inliers = sfm.F_from_ransac(x1n, x2n, model)
# compute camera matrices (P2 will be list of four solutions)
P1 = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
P2 = sfm.compute_P_from_essential(E)
# pick the solution with points in front of cameras
ind = 0
maxres = 0
for i in range(4):
# triangulate inliers and compute depth for each camera
X = sfm.triangulate(x1n[:, inliers], x2n[:, inliers], P1, P2[i])
d1 = np.dot(P1, X)[2]
d2 = np.dot(P2[i], X)[2]
if np.sum(d1 > 0) + np.sum(d2 > 0) > maxres:
maxres = np.sum(d1 > 0) + np.sum(d2 > 0)
ind = i
infront = (d1 > 0) & (d2 > 0)
# triangulate inliers and remove points not in front of both cameras
X = sfm.triangulate(x1n[:, inliers], x2n[:, inliers], P1, P2[ind])
X = X[:, infront]
# 3D plot
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure()
from pylab import *
import sfm
execfile('load_vggdata.py')
# Points in first 2 views.
ndx = (corr[:, 0] >= 0) & (corr[:, 1] >= 0)
x1 = points2D[0][:, corr[ndx, 0]]
x1 = numpy.vstack( (x1, numpy.ones(x1.shape[1])) )
x2 = points2D[1][:, corr[ndx, 1]]
x2 = numpy.vstack( (x2, numpy.ones(x2.shape[1])) )
Xtrue = points3D[:, ndx]
Xtrue = numpy.vstack( (Xtrue, numpy.ones(Xtrue.shape[1])) )
Xest = sfm.triangulate(x1, x2, P[0].P, P[1].P)
# Check 3 points:
print Xest[:, :3]
print Xtrue[:, :3]
from mpl_toolkits.mplot3d import axes3d
fig = figure()
ax = fig.gca(projection='3d')
ax.plot(Xest[0], Xest[1], Xest[2], 'ko')
ax.plot(Xtrue[0], Xtrue[1], Xtrue[2], 'b.')
axis('equal')
show()
ndx2 = [int(matches[i]) for i in ndx]
x2 = homography.make_homog(l2[ndx2,:2].T)
x1n = dot(inv(K),x1)
x2n = dot(inv(K),x2)
print "estimate E with RANSAC"
model = sfm.RansacModel()
E,inliers = sfm.F_from_ransac(x1n,x2n,model)
print "compute camera matrices (P2 will be list of four solutions)"
P1 = array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])
P2 = sfm.compute_P_from_essential(E)
print "pick the solution with points in front of cameras"
ind = 0
maxres = 0
for i in range(4):
# triangulate inliers and compute depth for each camera
X = sfm.triangulate(x1n[:,inliers],x2n[:,inliers],P1,P2[i])
d1 = dot(P1,X)[2]
d2 = dot(P2[i],X)[2]
if sum(d1>0)+sum(d2>0) > maxres:
maxres = sum(d1>0)+sum(d2>0)
ind = i
infront = (d1>0) & (d2>0)
# triangulate inliers and remove points not in front of both cameras
X = sfm.triangulate(x1n[:,inliers],x2n[:,inliers],P1,P2[ind])
X = X[:,infront]
print "3D plot"
from mpl_toolkits.mplot3d import axes3d
fig = figure()
ax = fig.gca(projection='3d')
ax.plot(-X[0],X[1],X[2],'k.')
axis('off')
# %matplotlib notebook # 最初の２枚の画像の点のインデックス番号 ndx = (corr[:, 0] >= 0) & (corr[:, 1] >= 0) # 座標値を取得し、同座標にする x1 = points2D[0][:, corr[ndx, 0]] x1 = homography.make_homog(x1) x2 = points2D[1][:, corr[ndx, 1]] x2 = homography.make_homog(x2) Xtrue = points3D[:, ndx] Xtrue = homography.make_homog(Xtrue) #最初の3点を調べる Xest = sfm.triangulate(x1, x2, P[0].P, P[1].P) print(Xest[:, :3]) print(Xtrue[:, :3]) #描写する fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.plot(Xest[0], Xest[1], Xest[2], 'ko') ax.plot(Xtrue[0], Xtrue[1], Xtrue[2], 'r.') plt.show # + # %matplotlib inline corr1 = corr[:, 0] #第一視点のインデックスを取り出す。 ndx3D = np.where(corr1 >= 0)[0] ndx2D = corr1[ndx3D]
import sfm
execfile('load_vggdata.py')
# Points in first 2 views.
ndx = (corr[:, 0] >= 0) & (corr[:, 1] >= 0)
x1 = points2D[0][:, corr[ndx, 0]]
x1 = numpy.vstack((x1, numpy.ones(x1.shape[1])))
x2 = points2D[1][:, corr[ndx, 1]]
x2 = numpy.vstack((x2, numpy.ones(x2.shape[1])))
Xtrue = points3D[:, ndx]
Xtrue = numpy.vstack((Xtrue, numpy.ones(Xtrue.shape[1])))
Xest = sfm.triangulate(x1, x2, P[0].P, P[1].P)
# Check 3 points:
print Xest[:, :3]
print Xtrue[:, :3]
from mpl_toolkits.mplot3d import axes3d
fig = figure()
ax = fig.gca(projection='3d')
ax.plot(Xest[0], Xest[1], Xest[2], 'ko')
ax.plot(Xtrue[0], Xtrue[1], Xtrue[2], 'r.')
axis('equal')
show()
def processInput():
print ""
if inputArgs.left == "" or inputArgs.right == "":
print "Missing images!"
quit()
# here we go ...
# load image pair - we might need them for later
img_l = cv2.imread(inputArgs.left)
img_r = cv2.imread(inputArgs.right)
if img_l == None or img_r == None:
print "Missing images!"
quit()
### git them points - calibration
mkp_l = []
mkp_r = []
file_kp = open(inputArgs.nameCalibration, 'r')
if file_kp == None:
print "Missing matching points file"
quit()
for line in file_kp:
l_r = line.split(';')
mkp_l.append(
[float(l_r[0].split(',')[0]),
float(l_r[0].split(',')[1])])
mkp_r.append(
[float(l_r[1].split(',')[0]),
float(l_r[1].split(',')[1])])
file_kp.close()
mkp_l = np.float32(mkp_l)
mkp_r = np.float32(mkp_r)
### git them points - reconstruction
mkp_l_p = []
mkp_r_p = []
file_kp = open(inputArgs.namePoints, 'r')
if file_kp == None:
print "Missing matching points file"
quit()
for line in file_kp:
l_r = line.split(';')
mkp_l_p.append(
[float(l_r[0].split(',')[0]),
float(l_r[0].split(',')[1])])
mkp_r_p.append(
[float(l_r[1].split(',')[0]),
float(l_r[1].split(',')[1])])
file_kp.close()
mkp_l_p = np.float32(mkp_l_p)
mkp_r_p = np.float32(mkp_r_p)
### git them calibrations - left
K_l = []
file_c_l = open(inputArgs.leftCalibration, 'r')
if file_c_l == None:
print "Missing left calibration file"
quit()
for line in file_c_l:
c_l = line.split(' ')
K_l.append([float(c_l[0]), float(c_l[1]), float(c_l[2])])
file_c_l.close()
K_l = np.float32(K_l)
### git them calibrations - right
K_r = []
file_c_r = open(inputArgs.rightCalibration, 'r')
if file_c_r == None:
print "Missing right calibration file"
quit()
for line in file_c_r:
c_l = line.split(' ')
K_r.append([float(c_l[0]), float(c_l[1]), float(c_l[2])])
file_c_r.close()
K_r = np.float32(K_r)
### ok, now we start work
# make homogeneous and normalize with inv(K)
x1 = homography.make_homog(mkp_l_p.T)
x2 = homography.make_homog(mkp_r_p.T)
x1n = dot(inv(K_l), x1)
x2n = dot(inv(K_r), x2)
# compute E (E = (K_r)T * F * K_l)
#F, mask = cv2.findFundamentalMat(mkp_l, mkp_r, cv2.FM_8POINT)
F, mask = cv2.findFundamentalMat(mkp_l, mkp_r, cv2.FM_RANSAC, 1, 0.99)
# we select only inlier points - most pf the time this makes it worse
#mkp_l = mkp_l[mask.ravel()==1]
#mkp_r = mkp_r[mask.ravel()==1]
E = K_r.transpose()
E = E.dot(F)
E = E.dot(K_l)
# compute camera matrices (P2 will be list of four solutions)
P1 = array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
P2 = sfm.compute_P_from_essential(E)
# pick the solution with points in front of cameras
ind = 0
maxres = 0
for i in range(4):
# triangulate inliers and compute depth for each camera
X = sfm.triangulate(x1n, x2n, P1, P2[i])
d1 = dot(P1, X)[2]
d2 = dot(P2[i], X)[2]
if sum(d1 > 0) + sum(d2 > 0) > maxres:
maxres = sum(d1 > 0) + sum(d2 > 0)
ind = i
infront = (d1 > 0) & (d2 > 0)
# triangulate inliers and remove points not in front of both cameras
X = sfm.triangulate(x1n, x2n, P1, P2[ind])
X = X[:, infront]
# visualization
if inputArgs.debug == 1:
# draw points
img_tmp = img_l.copy()
for kp in mkp_l_p:
x = int(kp[0])
y = int(kp[1])
cv2.circle(img_tmp, (x, y), 3, (0, 0, 255))
cv2.imwrite(inputArgs.namePoints + ".jpg", img_tmp)
# 3D plot
out_points = []
out_colors = []
for i in range(len(X[0])):
out_points.append([X[0][i], X[1][i], X[2][i]])
#out_colors.append(img_l[int(x1[1][i])][int(x1[0][i])])
out_colors.append([
img_l[int(x1[1][i])][int(x1[0][i])][2],
img_l[int(x1[1][i])][int(x1[0][i])][1],
img_l[int(x1[1][i])][int(x1[0][i])][0]
])
out_points = np.float32(out_points)
out_colors = np.float32(out_colors)
write_ply(inputArgs.namePoints + ".ply", out_points, out_colors)
def draw3d_reconstracted_estP(self):
self.ax_rcn_est.cla()
# カメラ行列（内部・外部）
M1 = self.K.dot(self.P1)
M2 = self.K.dot(self.P2)
# 画像座標 ( sx, sy, s )
x1p = M1.dot(self.Xpt)
x2p = M2.dot(self.Xpt)
# sで正規化 (x, y, 1)
for i in range(3):
x1p[i] /= x1p[2]
x2p[i] /= x2p[2]
# pts1 = []
# pts2 = []
# for i in range(len(x1p[0])):
# pts1.append([x1p[0][i], x1p[1][i]])
# pts2.append([x2p[0][i], x2p[1][i]])
# pts1 = np.int32(pts1)
# pts2 = np.int32(pts2)
# 画像1, 画像2の特徴点を対応付ける行列Fを計算
# F, mask = cv2.findFundamentalMat(pts1, pts2, cv2.FM_RANSAC)
# mask = np.reshape(mask, (1, len(mask)))[0]
# idx_mask = np.arange(len(mask))
# idx_mask = idx_mask[mask == 1]
x1n = np.dot(inv(self.K), x1p)
x2n = np.dot(inv(self.K), x2p)
# RANSACでEを推定
# Method_EstE = "RAN"
Method_EstE = ""
E = ""
inliers = ""
if Method_EstE is "RAN":
model = sfm.RansacModel()
E, inliers = sfm.F_from_ransac(x1n, x2n, model)
else:
E = sfm.compute_fundamental(x1n, x2n)
inliers = [True for i in range(len(x1n[0, :]))]
print("E: ", E)
# # カメラ行列を計算する（P2は4つの解のリスト）
P1 = array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
P2, S, R1, R2, U, V = sfm.compute_P_from_essential_mod(E)
print("S: ", S)
print("S fro: ", np.linalg.norm(S, "fro"))
print("R1: ", R1)
print("R2: ", R2)
print("R1 deg: ", calcRmatDeg(R1))
print("R2 deg: ", calcRmatDeg(R2))
P2_R = self.P2[:, 0:3]
print("P2_R: ", P2_R)
# print("U: ", U)
# print("V: ", V)
# print("S*R1: ", S.dot(R1))
# print("S*R2: ", S.dot(R2))
# 2つのカメラの前に点のある解を選ぶ
ind = 0
maxres = 0
for i in range(4):
# triangulate inliers and compute depth for each camera
# インライアを三角測量し各カメラからの奥行きを計算する
X = sfm.triangulate(x1n[:, inliers], x2n[:, inliers], P1, P2[i])
d1 = np.dot(P1, X)[2]
d2 = np.dot(P2[i], X)[2]
if sum(d1 > 0) + sum(d2 > 0) > maxres:
maxres = sum(d1 > 0) + sum(d2 > 0)
ind = i
infront = (d1 > 0) & (d2 > 0)
#全表示用
M1 = self.K.dot(P1)
M2_est = self.K.dot(P2[ind])
X = sfm.triangulate(x1p, x2p, M1, M2_est)
# print("P2 est:", P2[ind])
# print("P2: ", self.P2)
# for i in range(4):
# print("P2 est[ ", i, " ]: ", P2[i])
#
# M2 = self.K.dot(self.P2)
# print("M2: ", M2)
self.ax_rcn_est.set_xlabel("x_wld_rcn")
self.ax_rcn_est.set_ylabel("y_wld_rcn")
self.ax_rcn_est.set_zlabel("z_wld_rcn")
# self.ax_rcn_est.set_xlim([-5, 5])
# self.ax_rcn_est.set_ylim([-5, 5])
# self.ax_rcn_est.set_zlim([-5, 5])
self.ax_rcn_est.set_title("obj wld rcn est")
for l in self.ll:
x = np.array([X[0][l[0]], X[0][l[1]]])
y = np.array([X[1][l[0]], X[1][l[1]]])
z = np.array([X[2][l[0]], X[2][l[1]]])
self.ax_rcn_est.plot(x,
y,
z,
marker='.',
markersize=5,
color='red')
# # 対応が見つからなかった点
# x = X[0,[ not val for val in mask]]
# y = X[1,[ not val for val in mask]]
# z = X[2,[ not val for val in mask]]
# self.ax_rcn_est.plot(x, y, z, marker='.', markersize=5, color='black', linestyle = "")
# カメラより奥にあると推定された点
# X = sfm.triangulate(x1[:, idx_mask], x2[:, idx_mask], P1, P2[i])
x = X[0, [not val for val in infront]]
y = X[1, [not val for val in infront]]
z = X[2, [not val for val in infront]]
self.ax_rcn_est.plot(x,
y,
z,
marker='.',
markersize=5,
color='blue',
linestyle="")
