l2, d2 = sift.read_features_from_file('im2.sift')
# match features
matches = sift.match_twosided(d1, d2)
ndx = matches.nonzero()[0]
# make homogeneous and normalize with inv(K)
x1 = homography.make_homog(l1[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
x2 = homography.make_homog(l2[ndx2, :2].T)
x1n = np.dot(np.linalg.inv(K), x1)
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:
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="")