import torch import matplotlib.pyplot as plt import numpy as np import io import os import matplotlib from PIL import Image from mpl_toolkits.mplot3d.art3d import Poly3DCollection import mpl_toolkits.mplot3d.axes3d as p3 from textwrap import wrap import imageio def plot_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) init() # ax.lines = [] # ax.collections = [] ax.clear() # Clear the axes instead of directly setting attributes ax.view_init(elev=110, azim=-90) ax.dist = 7.5 # ax = plot_xzPlane(MINS[0] - trajec[index, 0], MAXS[0] - trajec[index, 0], 0, MINS[2] - trajec[index, 1], MAXS[2] - trajec[index, 1]) # ax.scatter(data[index, :22, 0], data[index, :22, 1], data[index, :22, 2], color='black', s=3) 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') # ax = plot_xzPlane(ax, MINS[0], MAXS[0], 0, MINS[2], MAXS[2]) for i, (chain, color) in enumerate(zip(smpl_kinetic_chain, colors)): # print(color) if i < 5: linewidth = 4.0 else: linewidth = 2.0 ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth, color=color) # print(trajec[:index, 0].shape) plt.axis('off') ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_zticklabels([]) io_buf = io.BytesIO() plt.savefig(io_buf, format='png', dpi=96) io_buf.seek(0) img = Image.open(io_buf) frame = np.array(img.convert('RGB'), dtype=np.uint8) io_buf.close() plt.close(fig) return frame out = [] for i in range(frame_number): frame = update(i) if frame.ndim == 3 and frame.shape[2] == 3: out.append(frame) else: print(f"Frame {i} has incorrect shape or channels: {frame.shape}") return out def draw_to_batch(smpl_joints_batch, title_batch=None, outname=None): batch_size = len(smpl_joints_batch) frames = [] for i in range(batch_size): frames.extend(plot_3d_motion([smpl_joints_batch[i], None, title_batch[i] if title_batch is not None else None])) if frames: gif_bytes = io.BytesIO() imageio.mimsave(gif_bytes, frames, format='GIF', fps=120) gif_bytes.seek(0) gif_data = gif_bytes.getvalue() print(f"Generated GIF for batch size {batch_size}, size: {len(gif_data)} bytes") return gif_data else: print("No valid frames to generate GIF.") return None # 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) # # print(fig.bbox.bounds) # 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 draw_to_batch(smpl_joints_batch, title_batch=None, outname=None) : # batch_size = len(smpl_joints_batch) # out = [] # for i in range(batch_size) : # out.append(plot_3d_motion([smpl_joints_batch[i], None, title_batch[i] if title_batch is not None else None])) # if outname is not None: # imageio.mimsave(outname[i], np.array(out[-1]), fps=20) # out = torch.stack(out, axis=0) # return out