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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
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