import cv2, io import numpy as np import torch import matplotlib.pyplot as plt import matplotlib from mpl_toolkits.mplot3d.art3d import Poly3DCollection import mpl_toolkits.mplot3d.axes3d as p3 from textwrap import wrap from ..utils.rotation_conversions import rotation_6d_to_matrix def _vis_3d_motion(args, figsize=(10, 10), fps=120, radius=4): matplotlib.use('Agg') joints, out_name, title = args data = joints.copy().reshape(len(joints), -1, 3) nb_joints = joints.shape[1] smpl_kinetic_chain = [ [0, 11, 12, 13, 14, 15], [0, 16, 17, 18, 19, 20], [0, 1, 2, 3, 4], [3, 5, 6, 7], [3, 8, 9, 10] ] if nb_joints == 21 else [ [0, 2, 5, 8, 11], [0, 1, 4, 7, 10], [0, 3, 6, 9, 12, 15], [9, 14, 17, 19, 21], [9, 13, 16, 18, 20] ] limits = 1000 if nb_joints == 21 else 2 MINS = data.min(axis=0).min(axis=0) MAXS = data.max(axis=0).max(axis=0) colors = ['red', 'blue', 'black', 'red', 'blue', 'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkred', 'darkred', 'darkred', 'darkred', 'darkred'] frame_number = data.shape[0] # print(data.shape) height_offset = MINS[1] data[:, :, 1] -= height_offset trajec = data[:, 0, [0, 2]] data[..., 0] -= data[:, 0:1, 0] data[..., 2] -= data[:, 0:1, 2] def update(index): def init(): ax.set_xlim(-limits, limits) ax.set_ylim(-limits, limits) ax.set_zlim(0, limits) ax.grid(b=False) def plot_xzPlane(minx, maxx, miny, minz, maxz): ## Plot a plane XZ verts = [ [minx, miny, minz], [minx, miny, maxz], [maxx, miny, maxz], [maxx, miny, minz] ] xz_plane = Poly3DCollection([verts]) xz_plane.set_facecolor((0.5, 0.5, 0.5, 0.5)) ax.add_collection3d(xz_plane) fig = plt.figure(figsize=(480/96., 320/96.), dpi=96) if nb_joints == 21 else plt.figure(figsize=(10, 10), dpi=96) if title is not None : wraped_title = '\n'.join(wrap(title, 40)) fig.suptitle(wraped_title, fontsize=16) ax = p3.Axes3D(fig, auto_add_to_figure=False) fig.add_axes(ax) init() for coll in ax.lines: coll.remove() for coll in ax.collections: coll.remove() ax.view_init(elev=110, azim=-90) ax.dist = 7.5 # type: ignore plot_xzPlane(MINS[0] - trajec[index, 0], MAXS[0] - trajec[index, 0], 0, MINS[2] - trajec[index, 1], MAXS[2] - trajec[index, 1]) if index > 1: ax.plot3D(trajec[:index, 0] - trajec[index, 0], np.zeros_like(trajec[:index, 0]), trajec[:index, 1] - trajec[index, 1], linewidth=1.0, color='blue') for i, (chain, color) in enumerate(zip(smpl_kinetic_chain, colors)): linewidth = 4.0 if i < 5 else 2.0 ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth, color=color) plt.axis('off') ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_zticklabels([]) # type: ignore if out_name is not None : plt.savefig(out_name, dpi=96) plt.close() else : io_buf = io.BytesIO() fig.savefig(io_buf, format='raw', dpi=96) io_buf.seek(0) arr = np.reshape(np.frombuffer(io_buf.getvalue(), dtype=np.uint8), newshape=(int(fig.bbox.bounds[3]), int(fig.bbox.bounds[2]), -1)) io_buf.close() plt.close() return arr out = [] for i in range(frame_number) : out.append(update(i)) out = np.stack(out, axis=0) return torch.from_numpy(out) def vis_3d_motion(smpl_joints_batch, title_batch=None, outname=None, fps=30) : out = [] for i in range(len(smpl_joints_batch)) : out.append(_vis_3d_motion([smpl_joints_batch[i], None, title_batch[i] if title_batch is not None else None])) if outname is not None: import imageio.v3 as iio images = np.array(out[-1]).astype(np.uint8)[..., :3] with iio.imopen(outname[i], "w", plugin="pyav") as writer: writer.init_video_stream("libx264", fps=int(fps)) writer._video_stream.options = {"crf": str(17)} writer.write(images) def accumulate_rotations(relative_rotations): R_total = [relative_rotations[0]] for R_rel in relative_rotations[1:]: R_total.append(np.matmul(R_rel, R_total[-1])) return np.array(R_total) def recover_from_local_position(final_x, njoint): if final_x.ndim == 3: bs, nfrm, _ = final_x.shape is_batched = True else: nfrm, _ = final_x.shape bs = 1 is_batched = False final_x = final_x.reshape(1, *final_x.shape) positions_no_heading = final_x[:,:,8:8+3*njoint].reshape(bs, nfrm, njoint, 3) velocities_root_xy_no_heading = final_x[:,:,:2] global_heading_diff_rot = final_x[:,:,2:8] positions_with_heading = [] for b in range(bs): global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot[b])).numpy()) inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1)) curr_pos_with_heading = np.matmul(np.repeat(inv_global_heading_rot[:, None,:, :], njoint, axis=1), positions_no_heading[b][...,None]).squeeze(-1) velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading[b].shape[0], 3)) velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[b, :, 0] velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[b, :, 1] velocities_root_xyz_no_heading[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz_no_heading[1:, :,None]).squeeze(-1) root_translation = np.cumsum(velocities_root_xyz_no_heading, axis=0) curr_pos_with_heading[:, :, 0] += root_translation[:, 0:1] curr_pos_with_heading[:, :, 2] += root_translation[:, 2:] positions_with_heading.append(curr_pos_with_heading) positions_with_heading = np.stack(positions_with_heading, axis=0) if not is_batched: positions_with_heading = positions_with_heading.squeeze(0) return positions_with_heading