import json import os from tabnanny import check import matplotlib.pyplot as plt import numpy as np import pytorch_kinematics as pk import torch import torch.nn import transforms3d import trimesh as tm import urdf_parser_py.urdf as URDF_PARSER from plotly import graph_objects as go from pytorch_kinematics.urdf_parser_py.urdf import (URDF, Box, Cylinder, Mesh, Sphere) from .rot6d import * from .utils_math import * import trimesh.sample # from kaolin.metrics.trianglemesh import point_to_mesh_distance # from kaolin.ops.mesh import check_sign, index_vertices_by_faces, face_normals # from utils.visualize_plotly import plot_mesh class HandModel: def __init__(self, robot_name, urdf_filename, mesh_path, batch_size=1, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'), mesh_nsp=128, hand_scale=2. ): self.device = device self.robot_name = robot_name self.batch_size = batch_size # prepare model self.robot = pk.build_chain_from_urdf(open(urdf_filename).read()).to(dtype=torch.float, device=self.device) self.robot_full = URDF_PARSER.URDF.from_xml_file(urdf_filename) # prepare contact point basis and surface point samples # self.no_contact_dict = json.load(open(os.path.join('data', 'urdf', 'intersection_%s.json'%robot_name))) # prepare geometries for visualization self.global_translation = None self.global_rotation = None self.softmax = torch.nn.Softmax(dim=-1) # prepare contact point basis and surface point samples #self.contact_point_dict = json.load(open(os.path.join("./hand_model", 'contact_%s.json' % robot_name))) self.contact_point_basis = {} self.contact_normals = {} self.surface_points = {} self.surface_points_normal = {} visual = URDF.from_xml_string(open(urdf_filename).read()) self.mesh_verts = {} self.mesh_faces = {} self.canon_verts = [] self.canon_faces = [] self.idx_vert_faces = [] self.face_normals = [] verts_bias = 0 for i_link, link in enumerate(visual.links): print(f"Processing link #{i_link}: {link.name}") # load mesh if len(link.visuals) == 0: continue if type(link.visuals[0].geometry) == Mesh: # print(link.visuals[0]) if robot_name == 'shadowhand' or robot_name == 'allegro' or robot_name == 'barrett': filename = link.visuals[0].geometry.filename.split('/')[-1] elif robot_name == 'allegro': filename = f"{link.visuals[0].geometry.filename.split('/')[-2]}/{link.visuals[0].geometry.filename.split('/')[-1]}" else: filename = link.visuals[0].geometry.filename mesh = tm.load(os.path.join(mesh_path, filename), force='mesh', process=False) elif type(link.visuals[0].geometry) == Cylinder: mesh = tm.primitives.Cylinder( radius=link.visuals[0].geometry.radius, height=link.visuals[0].geometry.length) elif type(link.visuals[0].geometry) == Box: mesh = tm.primitives.Box(extents=link.visuals[0].geometry.size) elif type(link.visuals[0].geometry) == Sphere: mesh = tm.primitives.Sphere( radius=link.visuals[0].geometry.radius) else: print(type(link.visuals[0].geometry)) raise NotImplementedError try: scale = np.array( link.visuals[0].geometry.scale).reshape([1, 3]) except: scale = np.array([[1, 1, 1]]) try: rotation = transforms3d.euler.euler2mat(*link.visuals[0].origin.rpy) translation = np.reshape(link.visuals[0].origin.xyz, [1, 3]) # print('---') # print(link.visuals[0].origin.rpy, rotation) # print('---') except AttributeError: rotation = transforms3d.euler.euler2mat(0, 0, 0) translation = np.array([[0, 0, 0]]) # Surface point # mesh.sample(int(mesh.area * 100000)) * scale # todo: marked original count is 128 if self.robot_name == 'shadowhand': pts, pts_face_index = trimesh.sample.sample_surface_even(mesh=mesh, count=64) pts_normal = np.array([mesh.face_normals[x] for x in pts_face_index], dtype=float) else: pts, pts_face_index = trimesh.sample.sample_surface_even(mesh=mesh, count=128) pts_normal = np.array([mesh.face_normals[x] for x in pts_face_index], dtype=float) if self.robot_name == 'barrett': if link.name in ['bh_base_link']: pts = trimesh.sample.volume_mesh(mesh=mesh, count=1024) pts_normal = np.array([[0., 0., 1.] for x in range(pts.shape[0])], dtype=float) if self.robot_name == 'ezgripper': if link.name in ['left_ezgripper_palm_link']: pts = trimesh.sample.volume_mesh(mesh=mesh, count=1024) pts_normal = np.array([[1., 0., 0.] for x in range(pts.shape[0])], dtype=float) if self.robot_name == 'robotiq_3finger': if link.name in ['gripper_palm']: pts = trimesh.sample.volume_mesh(mesh=mesh, count=1024) pts_normal = np.array([[0., 0., 1.] for x in range(pts.shape[0])], dtype=float) pts *= scale # pts = mesh.sample(128) * scale # print(link.name, len(pts)) # new if robot_name == 'shadowhand': pts = pts[:, [0, 2, 1]] pts_normal = pts_normal[:, [0, 2, 1]] pts[:, 1] *= -1 pts_normal[:, 1] *= -1 pts = np.matmul(rotation, pts.T).T + translation # pts_normal = np.matmul(rotation, pts_normal.T).T pts = np.concatenate([pts, np.ones([len(pts), 1])], axis=-1) pts_normal = np.concatenate([pts_normal, np.ones([len(pts_normal), 1])], axis=-1) self.surface_points[link.name] = torch.from_numpy(pts).to( device).float().unsqueeze(0).repeat(batch_size, 1, 1) self.surface_points_normal[link.name] = torch.from_numpy(pts_normal).to( device).float().unsqueeze(0).repeat(batch_size, 1, 1) # visualization mesh self.mesh_verts[link.name] = np.array(mesh.vertices) * scale if robot_name == 'shadowhand': self.mesh_verts[link.name] = self.mesh_verts[link.name][:, [0, 2, 1]] self.mesh_verts[link.name][:, 1] *= -1 self.mesh_verts[link.name] = np.matmul(rotation, self.mesh_verts[link.name].T).T + translation self.mesh_faces[link.name] = np.array(mesh.faces) # point and normal of palm center # contact point # if link.name in self.contact_point_dict: # # if link.name != 'index_1': continue # # new 1.11 # cpb = np.array(self.contact_point_dict[link.name]) # # print("cpb shape: ", cpb.shape, len(cpb.shape)) # if len(cpb.shape) > 1: # cpb = cpb[np.random.randint(cpb.shape[0], size=1)][0] # # print(link.name, cpb) # # go.Figure(data = [ # # go.Mesh3d(x=mesh.vertices[:,0], y=mesh.vertices[:,1], z=mesh.vertices[:,2], i=mesh.faces[:,0], j=mesh.faces[:,1], k=mesh.faces[:,2]), # # go.Scatter3d(x=mesh.vertices[cpb,0], y=mesh.vertices[cpb, 1], z=mesh.vertices[cpb,2])]).show() # # input() # cp_basis = mesh.vertices[cpb] * scale # # print(cpb, "cp_basis: ", cp_basis) # if robot_name == 'shadowhand': # cp_basis = cp_basis[:, [0, 2, 1]] # cp_basis[:, 1] *= -1 # cp_basis = np.matmul(rotation, cp_basis.T).T + translation # cp_basis = torch.cat([torch.from_numpy(cp_basis).to(device).float(), torch.ones([4, 1]).to(device).float()], dim=-1) # self.contact_point_basis[link.name] = cp_basis.unsqueeze( 0).repeat(batch_size, 1, 1) # v1 = cp_basis[1, :3] - cp_basis[0, :3] # v2 = cp_basis[2, :3] - cp_basis[0, :3] # v1 = v1 / torch.norm(v1) # v2 = v2 / torch.norm(v2) # self.contact_normals[link.name] = torch.cross(v1, v2).view([1, 3]) # self.contact_normals[link.name] = self.contact_normals[link.name].unsqueeze(0).repeat(batch_size, 1, 1) self.revolute_joints = [] for i in range(len(self.robot_full.joints)): if self.robot_full.joints[i].joint_type == 'revolute': self.revolute_joints.append(self.robot_full.joints[i]) self.revolute_joints_q_mid = [] self.revolute_joints_q_var = [] self.revolute_joints_q_upper = [] self.revolute_joints_q_lower = [] for i in range(len(self.robot.get_joint_parameter_names())): for j in range(len(self.revolute_joints)): if self.revolute_joints[j].name == self.robot.get_joint_parameter_names()[i]: joint = self.revolute_joints[j] assert joint.name == self.robot.get_joint_parameter_names()[i] self.revolute_joints_q_mid.append( (joint.limit.lower + joint.limit.upper) / 2) self.revolute_joints_q_var.append( ((joint.limit.upper - joint.limit.lower) / 2) ** 2) self.revolute_joints_q_lower.append(joint.limit.lower) self.revolute_joints_q_upper.append(joint.limit.upper) self.revolute_joints_q_lower = torch.Tensor( self.revolute_joints_q_lower).repeat([self.batch_size, 1]).to(device) self.revolute_joints_q_upper = torch.Tensor( self.revolute_joints_q_upper).repeat([self.batch_size, 1]).to(device) self.current_status = None self.scale = hand_scale def update_kinematics(self, q): self.global_translation = q[:, :3] self.global_rotation = robust_compute_rotation_matrix_from_ortho6d(q[:,3:9]) self.current_status = self.robot.forward_kinematics(q[:,9:]) def get_meshes_from_q(self, q=None, i=0): data = [] if q is not None: self.update_kinematics(q) for idx, link_name in enumerate(self.mesh_verts): trans_matrix = self.current_status[link_name].get_matrix() trans_matrix = trans_matrix[min(len(trans_matrix) - 1, i)].detach().cpu().numpy() v = self.mesh_verts[link_name] transformed_v = np.concatenate([v, np.ones([len(v), 1])], axis=-1) transformed_v = np.matmul(trans_matrix, transformed_v.T).T[..., :3] transformed_v = np.matmul(self.global_rotation[i].detach().cpu().numpy(), transformed_v.T).T + np.expand_dims( self.global_translation[i].detach().cpu().numpy(), 0) transformed_v = transformed_v * self.scale f = self.mesh_faces[link_name] data.append(tm.Trimesh(vertices=transformed_v, faces=f)) return data def get_plotly_data(self, q=None, i=0, color='lightblue', opacity=1.): data = [] if q is not None: self.update_kinematics(q) for idx, link_name in enumerate(self.mesh_verts): trans_matrix = self.current_status[link_name].get_matrix() trans_matrix = trans_matrix[min(len(trans_matrix) - 1, i)].detach().cpu().numpy() v = self.mesh_verts[link_name] transformed_v = np.concatenate([v, np.ones([len(v), 1])], axis=-1) transformed_v = np.matmul(trans_matrix, transformed_v.T).T[..., :3] transformed_v = np.matmul(self.global_rotation[i].detach().cpu().numpy(), transformed_v.T).T + np.expand_dims( self.global_translation[i].detach().cpu().numpy(), 0) transformed_v = transformed_v * self.scale f = self.mesh_faces[link_name] data.append( go.Mesh3d(x=transformed_v[:, 0], y=transformed_v[:, 1], z=transformed_v[:, 2], i=f[:, 0], j=f[:, 1], k=f[:, 2], color=color, opacity=opacity)) return data