Python pymesh.load_mesh方法代碼示例

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def load_point_input(self, path):
        ext = path.split('.')[-1]
        if ext == "npy":
            points = np.load(path)
        elif ext == "ply" or ext == "obj":
            import pymesh
            points = pymesh.load_mesh(path).vertices
        else:
            print("invalid file extension")

        points = torch.from_numpy(points).float()
        operation = pointcloud_processor.Normalization(points, keep_track=True)
        if self.opt.normalization == "UnitBall":
            operation.normalize_unitL2ball()
        elif self.opt.normalization == "BoundingBox":
            operation.normalize_bounding_box()
        else:
            pass
        return_dict = {
            'points': points,
            'operation': operation,
            'path': path,
        }
        return return_dict 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def isolate_files():
    """
    Utility fonction to generate the metro_file archive. Useless to all users but the author.
    """
    with open('./dataset/data/metro_files/files-metro.txt', 'r') as file:
        files = file.read().split('\n')
    for file in files:
        if file[-3:] == "ply":
            cat = file.split('/')[0]
            name = file.split('/')[1][:-4]
            path_points = '/'.join(['.', 'dataset', 'data', 'ShapeNetV1PointCloud', cat, name + '.points.ply.npy'])
            path_png = '/'.join(['.', 'dataset', 'data', 'ShapeNetV1Renderings', cat, name, "rendering", '00.png'])

            path_obj = '/'.join(['', 'home', 'thibault', 'hdd', 'data', 'ShapeNetCore.v1', cat, name, 'model.obj'])
            mesh = pymesh.load_mesh(path_obj)
            points = np.load((path_points))
            if not exists('/'.join(['.', 'dataset', 'data', 'metro_files', cat])):
                os.mkdir('/'.join(['.', 'dataset', 'data', 'metro_files', cat]))

            pymesh.save_mesh('/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.ply']), mesh, ascii=True)
            np.save('/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.npy']), points)
            copy(path_png, '/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.png'])) 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def shuffle_pc(file, output_path):
    """
    Function to shuffle a point cloud produced by virtual scanner.
    """
    mesh = pymesh.load_mesh(file)
    vertices = copy.deepcopy(mesh.vertices)
    permutation = np.random.permutation(len(vertices))
    vertices = vertices[permutation]
    new_mesh = pymesh.meshio.form_mesh(vertices, mesh.faces)
    new_mesh.add_attribute("vertex_nx")
    new_mesh.set_attribute("vertex_nx", mesh.get_vertex_attribute("vertex_nx")[permutation])
    new_mesh.add_attribute("vertex_ny")
    new_mesh.set_attribute("vertex_ny", mesh.get_vertex_attribute("vertex_ny")[permutation])
    new_mesh.add_attribute("vertex_nz")
    new_mesh.set_attribute("vertex_nz", mesh.get_vertex_attribute("vertex_nz")[permutation])
    pymesh.save_mesh(output_path, new_mesh, ascii=True, anonymous=True, use_float=True, *new_mesh.get_attribute_names()) 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def read_trimesh(path, normal=False, clean=True):
    mesh = pymesh.load_mesh(path)
    if clean:
        mesh, info = pymesh.remove_isolated_vertices(mesh)
        print("Removed {} isolated vertices".format(info["num_vertex_removed"]))
        mesh, info = pymesh.remove_duplicated_vertices(mesh)
        print("Merged {} duplicated vertices".format(info["num_vertex_merged"]))
        mesh, info = pymesh.remove_degenerated_triangles(mesh)
        mesh = pymesh.form_mesh(mesh.vertices, mesh.faces)

    vertices = mesh.vertices
    if normal:
        mesh.add_attribute("vertex_normal")
        vertex_normals = mesh.get_attribute("vertex_normal").reshape(-1, 3)
        vertices = np.concatenate([vertices, vertex_normals], axis=-1)
    return vertices, mesh.faces 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def downsample_pointcloud(pc_path, point_num):
    mesh_raw = pymesh.load_mesh(pc_path)
    points_raw = mesh_raw.vertices
    if points_raw.shape[0] > point_num:
        point_subset = np.random.choice(points_raw.shape[0], point_num, replace=False)
        points = points_raw[point_subset]
    else:
        points = points_raw
    mesh = pymesh.form_mesh(points, np.ones((0, 3)))
    pymesh.save_mesh(join(dirname(pc_path), 'compressed.ply'), mesh) 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def __init__(self, mesh_file):
        super(MeshView, self).__init__();
        self.mesh = pymesh.load_mesh(mesh_file);
        self.__init_mesh(); 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def create_from_setting(cls, setting):
        """ Syntax:
        {
            "type": "boundary",
            "mesh": mesh_file,
            "color": color_name,
            "radius": radius
        }
        """
        mesh = pymesh.load_mesh(setting["mesh"]);
        vertices = mesh.vertices;
        bd_edges = mesh.boundary_edges;
        vertices, bd_edges, __ = pymesh.remove_isolated_vertices_raw(vertices, bd_edges);
        wires = pymesh.wires.WireNetwork();
        if len(bd_edges) > 0:
            wires.load(vertices, bd_edges);
        else:
            logger = logging.getLogger(__name__);
            logger.warning("Mesh ({}) contains no boundary.".format(
                setting["mesh"]));

        tmp_dir = tempfile.gettempdir();
        stamp = datetime.datetime.now().isoformat();
        wire_file = os.path.join(tmp_dir, "{}.wire".format(stamp));
        wires.write_to_file(wire_file);

        wire_setting = {
                "type": "wire_network",
                "wire_network": wire_file,
                "color": setting.get("color", None),
                "radius": setting.get("radius", 0.1),
                "bbox": mesh.bbox,
                };
        return WireView.create_from_setting(wire_setting); 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def shuffle_pc(file, output_path):
    mesh = pymesh.load_mesh(file)
    vertices = copy.deepcopy(mesh.vertices)
    permutation = np.random.permutation(len(vertices))
    vertices = vertices[permutation]
    new_mesh = pymesh.meshio.form_mesh(vertices, mesh.faces)
    new_mesh.add_attribute("vertex_nx")
    new_mesh.set_attribute("vertex_nx", mesh.get_vertex_attribute("vertex_nx")[permutation])
    new_mesh.add_attribute("vertex_ny")
    new_mesh.set_attribute("vertex_ny", mesh.get_vertex_attribute("vertex_ny")[permutation])
    new_mesh.add_attribute("vertex_nz")
    new_mesh.set_attribute("vertex_nz", mesh.get_vertex_attribute("vertex_nz")[permutation])
    pymesh.save_mesh(output_path, new_mesh, ascii=True, anonymous=True, use_float=True, *new_mesh.get_attribute_names()) 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def remesh(path1):
    """
    This function takes a path to the orginal shapenet model and subsample it nicely
    """
    obj1 = pymesh.load_mesh(path1)
    obj1, info = pymesh.remove_isolated_vertices(obj1)
    print("Removed {} isolated vertices".format(info["num_vertex_removed"]))
    obj1, info = pymesh.remove_duplicated_vertices(obj1)
    print("Merged {} duplicated vertices".format(info["num_vertex_merged"]))
    obj1, _ = pymesh.remove_degenerated_triangles(obj1)
    if len(obj1.vertices)<5000:
        while len(obj1.vertices)<5000:
            obj1 = pymesh.subdivide(obj1)
    obj1 = pymesh.form_mesh(obj1.vertices, obj1.faces)
    return obj1 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def read_pointcloud(model_path, point_num, rotation=0):
    """
    Read point cloud from the target path
    :param model_path: file path for the point cloud
    :param point_num: input point number of the point cloud
    :param rotation: randomization with respect to the canonical view in term of azimuth
    :return: shape tensor
    """
    # read in original point cloud
    point_cloud_raw = pymesh.load_mesh(model_path).vertices

    # randomly select a fix number of points on the surface
    point_subset = np.random.choice(point_cloud_raw.shape[0], point_num, replace=False)
    point_cloud = point_cloud_raw[point_subset]

    # apply the random rotation on the point cloud
    if rotation != 0:
        alpha = math.radians(rotation)
        rot_matrix = np.array([[np.cos(alpha), -np.sin(alpha), 0.],
                               [np.sin(alpha), np.cos(alpha), 0.],
                               [0., 0., 1.]])
        point_cloud = np.matmul(point_cloud, rot_matrix.transpose())

    point_cloud = torch.from_numpy(point_cloud.transpose()).float()

    # normalize the point cloud into [0, 1]
    point_cloud = point_cloud - torch.min(point_cloud)
    point_cloud = point_cloud / torch.max(point_cloud)

    return point_cloud


# ================================================= #
# Datasets used for training
# ================================================= # 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def read_ply(filename):
    # Read a ply file from disk using pymesh and load the attributes used by MaSIF. 
    # filename: the input ply file. 
    # returns data as tuple.
    mesh = pymesh.load_mesh(filename)

    attributes = mesh.get_attribute_names()
    if "vertex_nx" in attributes:
        nx = mesh.get_attribute("vertex_nx")
        ny = mesh.get_attribute("vertex_ny")
        nz = mesh.get_attribute("vertex_nz")

        normals = numpy.column_stack((nx, ny, nz))
    else:
        normals = None
    if "vertex_charge" in attributes:
        charge = mesh.get_attribute("vertex_charge")
    else:
        charge = numpy.array([0.0] * len(mesh.vertices))

    if "vertex_cb" in attributes:
        vertex_cb = mesh.get_attribute("vertex_cb")
    else:
        vertex_cb = numpy.array([0.0] * len(mesh.vertices))

    if "vertex_hbond" in attributes:
        vertex_hbond = mesh.get_attribute("vertex_hbond")
    else:
        vertex_hbond = numpy.array([0.0] * len(mesh.vertices))

    if "vertex_hphob" in attributes:
        vertex_hphob = mesh.get_attribute("vertex_hphob")
    else:
        vertex_hphob = numpy.array([0.0] * len(mesh.vertices))

    return (
        mesh.vertices,
        mesh.faces,
        normals,
        charge,
        vertex_cb,
        vertex_hbond,
        vertex_hphob,
    ) 

# 需要導入模塊: import pymesh [as 別名]
# 或者: from pymesh import load_mesh [as 別名]
def getAnItem(self, index):
        """
        returns:
            points      : float (N,6) positions and normals
            cat         : string category
            file_path   : string
        """
        # ----------------------------------------------------------#
        points, cat, file_path = self.datas[index]
        file_name = os.path.splitext(os.path.basename(file_path))[0]
        if points is None or not self.use_preprocessed:
            found = glob(os.path.splitext(file_path)[0]+".*")
            if len(found) == 0:
                # use mesh
                mesh_path = os.path.join(self.mesh_dir, self.namecat2numbercat[cat], file_name, "model.obj")
                mesh = pymesh.load_mesh(mesh_path)
                mesh = pymesh.split_long_edges(mesh, 0.02)[0]
                points = mesh.vertices
            else:
                points = pc_utils.load(found[0])

            points = self.normalize(points[:,:3])
            points = torch.from_numpy(points).float()

        points = points.clone()
        # Resample
        if self.sample:
            choice = np.random.choice(points.size(0), self.npoints, replace=True)
            points = points[choice, :]

        rot_matrix = pc_utils.uniform_rotation_axis_matrix(axis=1, range_rot=self.data_augmentation_Z_rotation_range)
        if self.data_augmentation_Z_rotation:
            # Uniform random Rotation of axis Y
            points, rot_matrix = pc_utils.uniform_rotation_axis(points, axis=1, normals=self.normal,
                                                                        range_rot=self.data_augmentation_Z_rotation_range)
        if self.anisotropic_scaling:
            # Data augmentation : anisotropic scaling
            points[:, :3] = pc_utils.anisotropic_scaling(points[:, :3]).contiguous()
            points[:, :3] = self.normalize(points[:, :3])

        if self.data_augmentation_3D_rotation:
            #  Uniform random 3D rotation of the sphere.
            points, rot_matrix = pc_utils.uniform_rotation_sphere(points, normals=self.normal)
        # Remark : if input of data_augmentation is normalized to unit ball and centered, the output rotated is as well.
        if self.random_translation:
            points = pc_utils.add_random_translation(points, scale=0.03)
        # also load mesh during test
        if self.phase == "test" and self.mesh_dir is not None:
            mesh_path = os.path.join(self.mesh_dir, self.namecat2numbercat[cat], file_name, "model.obj")
            V_mesh, F_mesh = read_trimesh(mesh_path)
            V_mesh = V_mesh[:,:3]
            F_mesh = F_mesh[:,:3]
            # V_mesh = (V_mesh - center)/scale
            V_mesh = self.normalize(V_mesh)

            V_mesh = torch.from_numpy(V_mesh).to(dtype=torch.float)
            F_mesh = torch.from_numpy(F_mesh).to(dtype=torch.int64)
            return points, V_mesh, F_mesh, cat, file_name
        else:
            return points, cat, file_name
        # ----------------------------------------------------------#