Ejemplo n.º 1
0
def make_rigid_body(sample,
                    init_pos,
                    orientation,
                    linear_velocity,
                    angular_velocity,
                    device,
                    target_vertices=None):
    # Load a triangle mesh obj file
    if target_vertices is not None:
        tmp_obj_file = '/tmp/obj.obj'
        from blender_process import Process
        p = Process(sample.obj, target_vertices, tmp_obj_file)
        mesh = TriangleMesh.from_obj(tmp_obj_file)
    else:
        mesh = TriangleMesh.from_obj(sample.obj)
    vertices = (meshutils.normalize_vertices(
        mesh.vertices).unsqueeze(0).to(device))
    faces = mesh.faces.unsqueeze(0).to(device)
    textures = torch.cat(
        (
            sample.color[0] / 255 * torch.ones(
                1, faces.shape[1], 2, 1, dtype=torch.float32, device=device),
            sample.color[1] / 255 * torch.ones(
                1, faces.shape[1], 2, 1, dtype=torch.float32, device=device),
            sample.color[2] / 255 * torch.ones(
                1, faces.shape[1], 2, 1, dtype=torch.float32, device=device),
        ),
        dim=-1,
    )
    # (Uniform) Masses
    masses = (sample.mass / vertices.shape[-2]) * torch.nn.Parameter(
        torch.ones(vertices.shape[-2], dtype=vertices.dtype, device=device),
        requires_grad=True,
    )
    # Body
    body = RigidBody(
        vertices[0],
        position=torch.tensor(init_pos, dtype=torch.float32, device=device),
        masses=masses,
        orientation=torch.tensor(orientation).type(torch.float32).to(device),
        friction_coefficient=sample.fric,
        # linear_velocity=torch.tensor(linear_velocity).type(torch.float32).to(device)
        # angular_veloctiy=torch.tensor(angular_velocity).type(torch.float32).to(device)
    )
    return body, vertices, faces, textures
Ejemplo n.º 2
0
    if args.compare_every >= args.simsteps:
        raise ValueError(
            f"Arg --compare-every cannot be greater than or equal to {args.simsteps}."
        )

    # Seed RNG for repeatability
    torch.manual_seed(args.seed)

    # Device the tensors are all stored on.
    device = "cuda:0"

    # Load the template sphere mesh (for the bob)
    # sphere = TriangleMesh.from_obj(args.template)
    sphere = kaolin.io.obj.import_mesh(args.template)
    vertices_gt = meshutils.normalize_vertices(
        sphere.vertices.unsqueeze(0), scale_factor=args.radius).to(device)
    faces_gt = sphere.faces.to(device).unsqueeze(0)
    textures = torch.cat(
        (
            torch.ones(
                1, faces_gt.shape[1], 2, 1, dtype=torch.float32,
                device=device),
            torch.zeros(
                1, faces_gt.shape[1], 2, 1, dtype=torch.float32,
                device=device),
            torch.zeros(
                1, faces_gt.shape[1], 2, 1, dtype=torch.float32,
                device=device),
        ),
        dim=-1,
    )
Ejemplo n.º 3
0
        # Initialize the renderer.
        renderer = SoftRenderer(image_size=image_size,
                                camera_mode=camera_mode,
                                device=device)
        renderer.set_eye_from_angles(camera_distance, elevation, azimuth)

        sim_duration = 2.0  # seconds
        fps = 30  # frames per second
        sim_steps = int((sim_duration * fps) / 2)

        # obj = get_primitive_obj(shape[0])
        obj = get_primitive_obj(INT_TO_PRIMITIVE[shape[0]])
        # print(obj)
        mesh = TriangleMesh.from_obj(obj)
        vertices = ((
            meshutils.normalize_vertices(mesh.vertices)  # + \
            # torch.from_numpy(init_pos[i]).float().unsqueeze(0)
        ).to(device).unsqueeze(0))
        faces = mesh.faces.unsqueeze(0).to(device)
        textures = torch.cat(
            (
                color[0][0] / 255.0 * torch.ones(1,
                                                 faces.shape[1],
                                                 2,
                                                 1,
                                                 dtype=torch.float32,
                                                 device=device),
                color[0][1] / 255.0 * torch.ones(1,
                                                 faces.shape[1],
                                                 2,
                                                 1,
Ejemplo n.º 4
0
    device = "cuda:0"

    logdir = os.path.join(args.logdir, args.expid)
    if args.log:
        os.makedirs(logdir, exist_ok=True)
        with open(os.path.join(logdir, "args.txt"), "w") as f:
            json.dump(args.__dict__, f, indent=2)

    sim_dt = (1.0 / args.physics_engine_rate) / args.sim_substeps
    sim_steps = int(args.sim_duration / sim_dt)
    sim_time = 0.0

    builder_gt = df.sim.ModelBuilder()
    obj = TriangleMesh.from_obj(args.mesh)
    vertices = meshutils.normalize_vertices(obj.vertices).to(device)
    points = vertices.detach().cpu().numpy()
    indices = list(obj.faces.numpy().reshape((-1)))

    mesh = df.sim.Mesh(points, indices)

    pos_gt = (0.0, 4.0, 0.0)
    rot_gt = df.quat_from_axis_angle((0.0, 0.0, 1.0), math.pi * 0.3)
    # rot_gt = (0.0, 0.0, 0.0, 0.0)
    vel_gt = (0.0, 2.0, 0.0)
    omega_gt = (0.0, 0.0, 0.0)
    scale_gt = (1.0, 1.0, 1.0)
    density_gt = 5.0
    ke_gt = 4900.0
    kd_gt = 15.0
    kf_gt = 990.0
Ejemplo n.º 5
0
from gradsim.simulator import Simulator
from gradsim.utils import meshutils

if __name__ == "__main__":

    # Device to store tensors on (MUST be CUDA-capable, for renderer to work).
    device = "cuda:0"

    # Output (gif) file path
    outfile = Path("cache/demorestitution.gif")

    # Load a body (from a triangle mesh obj file).
    mesh = TriangleMesh.from_obj(Path("sampledata/cube.obj"))
    # mesh = TriangleMesh.from_obj(Path("sampledata/sphere.obj"))
    # mesh = TriangleMesh.from_obj(Path("sampledata/banana.obj"))
    vertices = meshutils.normalize_vertices(mesh.vertices.unsqueeze(0)).to(device)
    faces = mesh.faces.to(device).unsqueeze(0)
    textures = torch.cat(
        (
            torch.ones(1, faces.shape[1], 2, 1, dtype=torch.float32, device=device),
            torch.ones(1, faces.shape[1], 2, 1, dtype=torch.float32, device=device),
            torch.zeros(1, faces.shape[1], 2, 1, dtype=torch.float32, device=device),
        ),
        dim=-1,
    )
    masses = torch.nn.Parameter(
        0.1 * torch.ones(vertices.shape[1], dtype=vertices.dtype, device=device),
        requires_grad=True,
    )
    position = torch.tensor([0.0, 4.0, 0.0], dtype=torch.float32, device=device)
    orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=device)