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8b3f571 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | import sys
sys.path.append('/mnt/shaoguan1cephfs/capybarali/codes/any2motion')
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
import imageio
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)
# imageio.mimsave(outname[i], np.array(out[-1]), fps=fps)
# data = np.load('/mnt/shaoguan1cephfs/capybarali/data/MotionMillion/motion_272rpr/Mirror_MotionGV/folder0/001111.npy') # n, 272
# pos = recover_from_local_position(data, njoint=22) # n, 22, 3
# draw_to_batch(pos[None], None, [f'a.gif'], fps=30)
if __name__ == '__main__':
motion_dict = torch.load('data/bedlam_processed_145/motions/0000300.pt')
vel_xy = motion_dict['velocity_xy']
zeros_trans = torch.zeros((motion_dict['root_orient'].shape[0], 3))
trans_xy = torch.cumsum(vel_xy, dim=0)
trans_xy[1:] = trans_xy[:-1].clone()
trans = torch.cat([trans_xy, motion_dict['trans_z']], dim=-1)
vis_motion_joints_in_video(
rotation_6d_to_axis_angle(motion_dict['root_orient']),
rotation_6d_to_axis_angle(motion_dict['body_pose'].reshape(trans.shape[0], -1, 6)).flatten(1, 2),
trans, motion_dict['betas'],
motion_dict['cam_intrinsics'], motion_dict['cam_extrinsics'], motion_dict['img_paths'],
save_path='vis.mp4', vis_length=100)
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