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# -*- coding: utf-8 -*-
#
# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
# acting on behalf of its Max Planck Institute for Intelligent Systems and the
# Max Planck Institute for Biological Cybernetics. All rights reserved.
#
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights
# on this computer program. You can only use this computer program if you have closed a license agreement
# with MPG or you get the right to use the computer program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and liable to prosecution.
# Contact: [email protected]
#
#
# If you use this code in a research publication please consider citing the following:
#
# Expressive Body Capture: 3D Hands, Face, and Body from a Single Image <https://arxiv.org/abs/1904.05866>
#
#
# Code Developed by:
# Nima Ghorbani <https://nghorbani.github.io/>
#
# 2020.12.12
def pyrenderer(imw=2048, imh=2048):
from body_visualizer.mesh.mesh_viewer import MeshViewer
import cv2
import numpy as np
import trimesh
try:
mv = MeshViewer(width=imw, height=imh, use_offscreen=True)
except:
import os
os.environ['PYOPENGL_PLATFORM'] = 'egl'
os.environ['EGL_DEVICE_ID'] = os.environ['GPU_DEVICE_ORDINAL'].split(',')[0]
mv = MeshViewer(width=imw, height=imh, use_offscreen=True)
mv.set_cam_trans([0, -0.5, 2.])
def render_an_image(meshes):
n_all = len(meshes)
nc = int(np.sqrt(n_all))
out_image = np.zeros([1, 1, 1, mv.width, mv.height, 4])
scale_percent = 100./nc
width = int(mv.width * scale_percent / 100)
height = int(mv.height * scale_percent / 100)
dim = (width, height)
for rId in range(nc):
for cId in range(nc):
i = (nc*rId) + cId
if i>len(meshes): break
mesh = meshes[i]
# mesh.apply_transform(trimesh.transformations.rotation_matrix(np.radians(-90), (1, 0, 0)))
mesh.vertices -= np.median(np.array(mesh.vertices), axis=0)
mv.set_dynamic_meshes([mesh])
img = mv.render(render_wireframe=False, RGBA=True)
img_resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
out_image[0, 0, 0, (rId*width):((rId+1)*width), (cId*height):((cId+1)*height)] = cv2.cvtColor(img_resized, cv2.COLOR_BGRA2RGBA)
return out_image.astype(np.uint8)
return render_an_image
def vposer_trainer_renderer(bm, num_bodies_to_display=5):
import numpy as np
import trimesh
import torch
from body_visualizer.tools.vis_tools import imagearray2file, colors
from human_body_prior.tools.omni_tools import copy2cpu as c2c
from human_body_prior.tools.omni_tools import makepath
from trimesh import Trimesh as Mesh
from trimesh.util import concatenate as mesh_cat
renderer = pyrenderer(1024, 1024)
faces = c2c(bm.f)
def render_once(body_parms, body_colors=[colors['grey'], colors['brown-light']], out_fname=None):
'''
:param body_parms: list of dictionaries of body parameters.
:param body_colors: list of np arrays of color rgb values
:param movie_outpath: a mp4 path
:return:
'''
if out_fname is not None: makepath(out_fname, isfile=True)
assert len(body_parms) <= len(body_colors), ValueError('Not enough colors provided for #{} body_parms'.format(len(body_parms)))
bs = body_parms[0]['pose_body'].shape[0]
body_ids = np.random.choice(bs, num_bodies_to_display)
body_evals = [c2c(bm(root_orient=v['root_orient'].view(bs, -1) if 'root_orient' in v else torch.zeros(bs, 3).type_as(v['pose_body']),
pose_body=v['pose_body'].contiguous().view(bs, -1)).v) for v in body_parms]
num_verts = body_evals[0].shape[1]
render_meshes = []
for bId in body_ids:
concat_cur_meshes = None
for body, body_color in zip(body_evals, body_colors):
cur_body_mesh = Mesh(body[bId], faces, vertex_colors=np.ones([num_verts, 3]) * body_color)
concat_cur_meshes = cur_body_mesh if concat_cur_meshes is None else mesh_cat(concat_cur_meshes, cur_body_mesh)
render_meshes.append(concat_cur_meshes)
img = renderer(render_meshes)
if out_fname is not None: imagearray2file(img, out_fname, fps=10)
return
return render_once
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