def ViewPLY(path):
# 読み込み
pointcloud = open3d.read_point_cloud(path)
# ダウンサンプリング
# 法線推定できなくなるので不採用
pointcloud = open3d.voxel_down_sample(pointcloud, 10)
# 法線推定
open3d.estimate_normals(pointcloud,
search_param=open3d.KDTreeSearchParamHybrid(
radius=20, max_nn=30))
# 法線の方向を視点ベースでそろえる
open3d.orient_normals_towards_camera_location(pointcloud,
camera_location=np.array(
[0., 10., 10.],
dtype="float64"))
# 可視化
open3d.draw_geometries([pointcloud])
# numpyに変換
points = np.asarray(pointcloud.points)
normals = np.asarray(pointcloud.normals)
print("points:{}".format(points.shape[0]))
X, Y, Z = Disassemble(points)
# OBB生成
_, _, length = buildOBB(points)
print("OBB_length: {}".format(length))
return points, X, Y, Z, normals, length
def NormalEstimate(points):
""" 法線推定 """
#点群をnp配列⇒open3d形式に
pointcloud = open3d.PointCloud()
pointcloud.points = open3d.Vector3dVector(points)
# 法線推定
open3d.estimate_normals(pointcloud,
search_param=open3d.KDTreeSearchParamHybrid(
radius=5, max_nn=30))
# 法線の方向を視点ベースでそろえる
open3d.orient_normals_towards_camera_location(pointcloud,
camera_location=np.array(
[0., 10., 10.],
dtype="float64"))
#nキーで法線表示
#open3d.draw_geometries([pointcloud])
#法線をnumpyへ変換
normals = np.asarray(pointcloud.normals)
return normals
def NormalEstimate(points):
#objファイルから点群取得
# points = loadOBJ(path)
print("points:{}".format(points.shape[0]))
#点群をnp配列⇒open3d形式に
pointcloud = open3d.PointCloud()
pointcloud.points = open3d.Vector3dVector(points)
# 法線推定
open3d.estimate_normals(pointcloud,
search_param=open3d.KDTreeSearchParamHybrid(
radius=5, max_nn=30))
# 法線の方向を視点ベースでそろえる
open3d.orient_normals_towards_camera_location(pointcloud,
camera_location=np.array(
[0., 10., 10.],
dtype="float64"))
#nキーで法線表示
#open3d.draw_geometries([pointcloud])
#法線をnumpyへ変換
normals = np.asarray(pointcloud.normals)
#OBB生成
#(最適化の条件にも使いたい)
# _, _, length = buildOBB(points)
# print("OBB_length: {}".format(length))
return normals
def preprocess_point_cloud(pointcloud, voxel_size):
keypoints = open3d.voxel_down_sample(pointcloud, voxel_size)
radius_normal = voxel_size * 2
view_point = np.array([0., 10., 10.], dtype="float64")
open3d.estimate_normals(keypoints,
search_param=open3d.KDTreeSearchParamHybrid(
radius=radius_normal, max_nn=30))
open3d.orient_normals_towards_camera_location(keypoints,
camera_location=view_point)
radius_feature = voxel_size * 5
fpfh = open3d.compute_fpfh_feature(
keypoints,
search_param=open3d.KDTreeSearchParamHybrid(radius=radius_feature,
max_nn=100))
return keypoints, fpfh
def PreProcess2():
#自作した点群を読み込み
points, X, Y, Z = MakePoints(sample_plane,
bbox=(-1.5, 1),
grid_step=80,
epsilon=0.03,
down_rate=0.5)
print("points:{}".format(len(X)))
#点群をnp配列⇒open3d形式に
pointcloud = open3d.PointCloud()
pointcloud.points = open3d.Vector3dVector(points)
# 法線推定
open3d.estimate_normals(pointcloud,
search_param=open3d.KDTreeSearchParamHybrid(
radius=1, max_nn=30))
# 法線の方向を視点ベースでそろえる
open3d.orient_normals_towards_camera_location(pointcloud,
camera_location=np.array(
[0., 10., 10.],
dtype="float64"))
#nキーで法線表示
#open3d.draw_geometries([pointcloud])
#法線をnumpyへ変換
normals = np.asarray(pointcloud.normals)
#OBB生成
#(最適化の条件にも使いたい)
_, _, length = buildOBB(points)
print("OBB_length: {}".format(length))
return points, X, Y, Z, normals, length
noise = np.array([[Random(xmin, xmax),
Random(ymin, ymax),
Random(zmin, zmax)] for i in range(n_size)])
points = np.concatenate([points, noise])
size = points.shape[0]
#点群をnp配列⇒open3d形式に
pointcloud = open3d.PointCloud()
pointcloud.points = open3d.Vector3dVector(points)
# color
colors3 = np.asarray(pointcloud.colors)
print(colors3.shape)
colors3 = np.array([[255, 130, 0] if i < p_size else [0, 0, 255]
for i in range(size)]) / 255
pointcloud.colors = open3d.Vector3dVector(colors3)
# 法線推定
open3d.estimate_normals(pointcloud,
search_param=open3d.KDTreeSearchParamHybrid(
radius=5, max_nn=100))
# 法線の方向を視点ベースでそろえる
open3d.orient_normals_towards_camera_location(pointcloud,
camera_location=np.array(
[0., 10., 10.],
dtype="float64"))
#nキーで法線表示
open3d.draw_geometries([pointcloud])
maxR = 0.55
maxitteration = 1000
threshould = 0.05
start = time.time()
for i in range(1):
pcd = o3d.io.read_point_cloud("stright.ply")
#pcd=o3d.io.read_point_cloud("cylinderPLY/"+str(i+1)+"cylinder2m.ply")
o3d.geometry.estimate_normals(
pcd,
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.5,
max_nn=10000))
#法線ベクトルを視点方向に揃える
o3d.orient_normals_towards_camera_location(pcd,
camera_location=np.array(
[0., 0., 1000.],
dtype="float64"))
#o3d.visualization.draw_geometries([pcd])
point = np.asarray(pcd.points)
normal = np.asarray(pcd.normals)
maxinliner = 0
itteration = 0
#RANSAC
while itteration < maxitteration:
sample = random.sample(range(len(point)), 2)
a = sample[0]
b = sample[1]
p1 = np.array(pcd.points[a])
