MotionGPT / mGPT /render /matplot /plot_3d_global.py
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import torch
import matplotlib.pyplot as plt
import numpy as np
import io
import matplotlib
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
title_sp = title.split(' ')
if len(title_sp) > 20:
title = '\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:20]), ' '.join(title_sp[20:])])
elif len(title_sp) > 10:
title = '\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:])])
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_xlim3d([-radius / 2, radius / 2])
ax.set_ylim3d([0, radius])
ax.set_zlim3d([0, radius])
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)
# fig.tight_layout()
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()
# ax.lines = []
# ax.collections = []
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([])
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]), duration=50)
out = torch.stack(out, axis=0)
return out