def main():
np.random.seed(1234)
if len(sys.argv) < 2:
print("Usage: $python3 run_reconstruction.py <path to PLY file>")
exit(0)
filename = sys.argv[1]
# Camera intrinsic matrix
# In this case, the image coordinate is represented in a non-homogeneous 2D
# vector, therefore the intrinsic matrix is represented in a 2x3 matrix.
# In this script, we assume the orthogonal projection as a camera
# projection model
intrinsic_parameters = np.array([[1, 0, 0], [0, 1, 0]])
# Load the 3D object from the file
X_true = read_object(filename)
X_true = normalize_object_size(X_true)
# Number of viewpoints to be used for reconstruction
n_views = 128
# Standard deviation of noise
noise_std = 0.0
target_object = Object3D(X_true) # Create the target object
camera = Camera(
intrinsic_parameters) # Camera object to observe the target
# The ground truth object `X_true` is passed to the TomasiKanade method,
# though, this is used only for the evaluation, not reconstruction
tomasi_kanade = TomasiKanade(X_eval=X_true, learning_rate=0.0027)
for i in range(n_views):
# Generate a random camera pose
R = rigid_motion.random_rotation_matrix_3d()
t = rigid_motion.random_vector_3d()
camera.set_pose(R, t)
# Observe the 3D object by projecting it onto the image plane
image_points = take_picture(target_object, camera, noise_std)
tomasi_kanade.add_image_points(image_points)
# Run reconstruction
# M is a stacked motion matrices
# X contains the reconstructed object
M, X = tomasi_kanade.run()
V = to_viewpoints(M)
plot3d(X, azim=180, elev=90)
plot_result(X, V)
plt.show()
var = pd.DataFrame(test_rmse)
var.to_csv(path+'/test_rmse.csv',index = False,header = False)
#var = pd.DataFrame(alpha1)
#var.to_csv(path+'/alpha.csv',index = False, header = False)
#print('min_rmse:%r'%(np.min(test_rmse)),
# 'min_mae:%r'%(np.min(test_mae)),
# 'max_acc:%r'%(np.max(test_acc)))
index = test_rmse.index(np.min(test_rmse))
#test_mae.index(np.min(test_mae))
#test_acc.index(np.max(test_acc))
test_result = test_pred[index]
var = pd.DataFrame(test_result)
var.to_csv(path+'/test_result.csv',index = False,header = False)
plot_result(test_result,test_label1,path)
plot_error(train_rmse,train_loss,test_rmse,test_acc,test_mae,path)
fig1 = plt.figure(figsize=(7,3))
ax1 = fig1.add_subplot(1,1,1)
plt.plot(np.sum(alpha1,0))
plt.savefig(path+'/alpha.jpg',dpi=500)
plt.show()
plt.imshow(np.mat(np.sum(alpha1,0)))
plt.savefig(path+'/alpha11.jpg',dpi=500)
plt.show()
print('min_rmse:%r'%(np.min(test_rmse)),
'min_mae:%r'%(test_mae[index]),