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release_renderer_kinect.py
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import os
import sys
rootPath = '../'
sys.path.append(rootPath)
import os.path as osp
import cv2
import numpy as np
import json
import trimesh
import argparse
# os.environ["PYOPENGL_PLATFORM"] = "egl"
# os.environ["PYOPENGL_PLATFORM"] = "osmesa"
import pyrender
import PIL.Image as pil_img
import pickle
import smplx
import torch
from tqdm import tqdm
from os.path import basename
from utils import *
import pandas as pd
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def main(args):
print('[INFO] recording_name:', args.recording_name)
print('[INFO] view: ', args.view)
calib_trans_dir = os.path.join(args.release_data_root, 'calibrations', args.recording_name) # extrinsics
camera_params_dir = os.path.join(args.release_data_root, 'kinect_cam_params') # intrinsics
color_dir = osp.join(args.release_data_root, 'kinect_color', args.recording_name, args.view)
data_split_info = pd.read_csv(os.path.join(args.release_data_root, 'data_splits.csv'))
train_split_list = list(data_split_info['train'])
val_split_list = list(data_split_info['val'])
test_split_list = list(data_split_info['test'])
if args.recording_name in train_split_list:
split = 'train'
elif args.recording_name in val_split_list:
split = 'val'
elif args.recording_name in test_split_list:
split = 'test'
else:
print('Error: {} not in all splits.'.format(args.recording_name))
eixt()
if args.model_type == 'smplx':
fitting_root_interactee = osp.join(args.release_data_root, 'smplx_interactee_{}'.format(split), args.recording_name)
fitting_root_camera_wearer = osp.join(args.release_data_root, 'smplx_camera_wearer_{}'.format(split), args.recording_name)
elif args.model_type == 'smpl':
fitting_root_interactee = osp.join(args.release_data_root, 'smpl_interactee_{}'.format(split), args.recording_name)
fitting_root_camera_wearer = osp.join(args.release_data_root, 'smpl_camera_wearer_{}'.format(split), args.recording_name)
else:
print('Error: body model type error!')
exit()
########## load calibration from sub kinect to main kinect (between color cameras)
# master: kinect 12, sub_1: kinect 11, sub_2: kinect 13, sub_3, kinect 14, sub_4: kinect 15
if args.view == 'sub_1':
trans_subtomain_path = osp.join(calib_trans_dir, 'cal_trans', 'kinect_11to12_color.json')
elif args.view == 'sub_2':
trans_subtomain_path = osp.join(calib_trans_dir, 'cal_trans', 'kinect_13to12_color.json')
elif args.view == 'sub_3':
trans_subtomain_path = osp.join(calib_trans_dir, 'cal_trans', 'kinect_14to12_color.json')
elif args.view == 'sub_4':
trans_subtomain_path = osp.join(calib_trans_dir, 'cal_trans', 'kinect_15to12_color.json')
if args.view != 'master':
if not os.path.exists(trans_subtomain_path):
print('[ERROR] {} does not have the view of {}!'.format(args.recording_name, args.view))
exit()
with open(osp.join(trans_subtomain_path), 'r') as f:
trans_subtomain = np.asarray(json.load(f)['trans'])
trans_maintosub = np.linalg.inv(trans_subtomain)
################################################ read body idx info
df = pd.read_csv(os.path.join(args.release_data_root, 'data_info_release.csv')) # todo
recording_name_list = list(df['recording_name'])
start_frame_list = list(df['start_frame'])
end_frame_list = list(df['end_frame'])
body_idx_fpv_list = list(df['body_idx_fpv'])
gender_0_list = list(df['body_idx_0'])
gender_1_list = list(df['body_idx_1'])
body_idx_fpv_dict = dict(zip(recording_name_list, body_idx_fpv_list))
gender_0_dict = dict(zip(recording_name_list, gender_0_list))
gender_1_dict = dict(zip(recording_name_list, gender_1_list))
start_frame_dict = dict(zip(recording_name_list, start_frame_list))
end_frame_dict = dict(zip(recording_name_list, end_frame_list))
######## get body idx for camera wearer/second person
interactee_idx = int(body_idx_fpv_dict[args.recording_name].split(' ')[0])
camera_wearer_idx = 1 - interactee_idx
######### get gender for camera weearer/second person
interactee_gender = body_idx_fpv_dict[args.recording_name].split(' ')[1]
if camera_wearer_idx == 0:
camera_wearer_gender = gender_0_dict[args.recording_name].split(' ')[1]
elif camera_wearer_idx == 1:
camera_wearer_gender = gender_1_dict[args.recording_name].split(' ')[1]
###########################################
if args.rendering_mode == '3d' or args.rendering_mode == 'both':
scene_dir = os.path.join(os.path.join(args.release_data_root, 'scene_mesh'), args.scene_name)
static_scene = trimesh.load(osp.join(scene_dir, args.scene_name + '.obj'))
cam2world_dir = os.path.join(calib_trans_dir, 'cal_trans/kinect12_to_world') # transformation from master camera to scene mesh
with open(os.path.join(cam2world_dir, args.scene_name + '.json'), 'r') as f:
trans = np.array(json.load(f)['trans'])
trans = np.linalg.inv(trans)
static_scene.apply_transform(trans)
if args.view != 'master':
static_scene.apply_transform(trans_maintosub)
body_scene_rendering_dir = os.path.join(args.save_root, 'renderings_kinect_3d')
if not osp.exists(body_scene_rendering_dir):
os.mkdir(body_scene_rendering_dir)
body_scene_rendering_dir = os.path.join(body_scene_rendering_dir, args.recording_name)
if not osp.exists(body_scene_rendering_dir):
os.mkdir(body_scene_rendering_dir)
body_scene_rendering_dir = os.path.join(body_scene_rendering_dir, args.view)
if not osp.exists(body_scene_rendering_dir):
os.mkdir(body_scene_rendering_dir)
if args.rendering_mode == 'body' or args.rendering_mode == 'both':
rendering_dir = os.path.join(args.save_root, 'renderings_kinect_img')
if not osp.exists(rendering_dir):
os.mkdir(rendering_dir)
rendering_dir = os.path.join(rendering_dir, args.recording_name)
if not osp.exists(rendering_dir):
os.mkdir(rendering_dir)
rendering_dir = os.path.join(rendering_dir, args.view)
if not osp.exists(rendering_dir):
os.mkdir(rendering_dir)
########## read kinect color camera intrinsics
with open(osp.join(camera_params_dir, 'kinect_{}'.format(args.view), 'Color.json'), 'r') as f:
color_cam = json.load(f)
[f_x, f_y] = color_cam['f']
[c_x, c_y] = color_cam['c']
########## create render camera
camera_pose = np.eye(4)
camera_pose = np.array([1.0, -1.0, -1.0, 1.0]).reshape(-1, 1) * camera_pose
camera = pyrender.camera.IntrinsicsCamera(
fx=f_x, fy=f_y,
cx=c_x, cy=c_y)
light = pyrender.DirectionalLight(color=np.ones(3), intensity=4.0)
material_interactee = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.0,
alphaMode='OPAQUE',
baseColorFactor=(1.0, 193 / 255, 193 / 255, 1.0) # pink, interactee
# baseColorFactor = (70 / 255, 130 / 255, 180 / 255, 1.0)
)
material_camera_wearer = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.0,
alphaMode='OPAQUE',
baseColorFactor=(70 / 255, 130 / 255, 180 / 255, 1.0) # blue, camera_wearer
)
######## create smplx/smpl body models
if args.model_type == 'smplx':
model_interactee = smplx.create(os.path.join(args.model_folder, 'smplx_model'), model_type='smplx',
gender=interactee_gender, ext='npz', num_pca_comps=args.num_pca_comps,
create_global_orient=True, create_transl=True, create_body_pose=True,
create_betas=True,
create_left_hand_pose=True, create_right_hand_pose=True,
create_expression=True, create_jaw_pose=True, create_leye_pose=True,
create_reye_pose=True).to(device)
model_camera_wearer = smplx.create(os.path.join(args.model_folder, 'smplx_model'), model_type='smplx',
gender=camera_wearer_gender, ext='npz', num_pca_comps=args.num_pca_comps,
create_global_orient=True, create_transl=True, create_body_pose=True,
create_betas=True,
create_left_hand_pose=True, create_right_hand_pose=True,
create_expression=True, create_jaw_pose=True, create_leye_pose=True,
create_reye_pose=True).to(device)
elif args.model_type == 'smpl':
model_interactee = smplx.create(args.model_folder, model_type='smpl', gender=interactee_gender).to(device)
model_camera_wearer = smplx.create(args.model_folder, model_type='smpl', gender=camera_wearer_gender).to(device)
for i_frame in tqdm(range(start_frame_dict[args.recording_name], end_frame_dict[args.recording_name]+1)[args.start::args.step]):
frame_id = 'frame_{}'.format("%05d"%i_frame)
if not osp.exists(osp.join(fitting_root_interactee, 'body_idx_{}'.format(interactee_idx), 'results', frame_id, '000.pkl')):
print('interactee fitting {} do not exist!'.format(frame_id))
continue
if not osp.exists(osp.join(fitting_root_camera_wearer, 'body_idx_{}'.format(camera_wearer_idx), 'results', frame_id, '000.pkl')):
print('camera wearer fitting {} do not exist!'.format(frame_id))
continue
if not osp.exists(osp.join(color_dir, frame_id + '.jpg')):
print('view {}, kinect color image {} do not exist!'.format(args.view, frame_id))
continue
##### read interactee smplx params
with open(osp.join(fitting_root_interactee, 'body_idx_{}'.format(interactee_idx), 'results', frame_id, '000.pkl'), 'rb') as f:
param = pickle.load(f)
torch_param = {}
if args.model_type == 'smpl':
torch_param['transl'] = torch.tensor(param['transl']).to(device)
torch_param['global_orient'] = torch.tensor(param['global_orient']).to(device)
torch_param['betas'] = torch.tensor(param['betas']).to(device)
torch_param['body_pose'] = torch.tensor(param['body_pose']).to(device)
elif args.model_type == 'smplx':
for key in param.keys():
if key in ['pose_embedding', 'camera_rotation', 'camera_translation', 'gender']:
continue
else:
torch_param[key] = torch.tensor(param[key]).to(device)
output = model_interactee(return_verts=True, **torch_param)
vertices = output.vertices.detach().cpu().numpy().squeeze()
body = trimesh.Trimesh(vertices, model_interactee.faces, process=False)
if args.view != 'master':
body.apply_transform(trans_maintosub)
body_mesh_interactee = pyrender.Mesh.from_trimesh(body, material=material_interactee)
##### read camera wearer smplx params
with open(osp.join(fitting_root_camera_wearer, 'body_idx_{}'.format(camera_wearer_idx), 'results', frame_id, '000.pkl'), 'rb') as f:
param = pickle.load(f)
torch_param = {}
if args.model_type == 'smpl':
torch_param['transl'] = torch.tensor(param['transl']).to(device)
torch_param['global_orient'] = torch.tensor(param['global_orient']).to(device)
torch_param['betas'] = torch.tensor(param['betas']).to(device)
torch_param['body_pose'] = torch.tensor(param['body_pose']).to(device)
elif args.model_type == 'smplx':
for key in param.keys():
if key in ['pose_embedding', 'camera_rotation', 'camera_translation', 'gender']:
continue
else:
torch_param[key] = torch.tensor(param[key]).to(device)
output = model_camera_wearer(return_verts=True, **torch_param)
vertices = output.vertices.detach().cpu().numpy().squeeze()
body = trimesh.Trimesh(vertices, model_camera_wearer.faces, process=False)
if args.view != 'master':
body.apply_transform(trans_maintosub)
body_mesh_camera_wearer = pyrender.Mesh.from_trimesh(body, material=material_camera_wearer)
###### render on undistorted color image
if args.rendering_mode == 'body' or args.rendering_mode == 'both':
img = cv2.imread(os.path.join(color_dir, frame_id + '.jpg'))[:, :, ::-1]
H, W, _ = img.shape
img_undistort = cv2.undistort(img.copy(),
np.asarray(color_cam['camera_mtx']),
np.asarray(color_cam['k']))
scene = pyrender.Scene(bg_color=[0.0, 0.0, 0.0, 0.0],
ambient_light=(0.3, 0.3, 0.3))
scene.add(camera, pose=camera_pose)
scene.add(light, pose=camera_pose)
scene.add(body_mesh_interactee, 'body_mesh_interactee')
scene.add(body_mesh_camera_wearer, 'body_mesh_camera_wearer')
r = pyrender.OffscreenRenderer(viewport_width=W,
viewport_height=H,
point_size=1.0)
color, _ = r.render(scene, flags=pyrender.RenderFlags.RGBA)
color = color.astype(np.float32)
valid_mask = (color[:, :, -1] > 0)[:, :, np.newaxis]
input_img = img_undistort
# input_img[:, :, :] = 255
output_img = (color[:, :, :-1] * valid_mask +
(1 - valid_mask) * input_img)
output_img = pil_img.fromarray((output_img).astype(np.uint8))
output_img.convert('RGB')
output_img = output_img.resize((int(W / 2), int(H / 2)))
output_img.save(os.path.join(rendering_dir, frame_id + '.jpg'))
if args.save_undistorted_img:
img_undistort = pil_img.fromarray(img_undistort.astype(np.uint8))
img_undistort = img_undistort.resize((int(W / args.scale), int(H / args.scale)))
img_undistort.save(os.path.join(rendering_dir, 'input_' + frame_id + '.jpg'))
###### render in 3d scene
if args.rendering_mode == '3d' or args.rendering_mode == 'both':
static_scene_mesh = pyrender.Mesh.from_trimesh(static_scene)
scene = pyrender.Scene()
scene.add(camera, pose=camera_pose)
scene.add(light, pose=camera_pose)
scene.add(static_scene_mesh, 'mesh')
scene.add(body_mesh_interactee, 'body_mesh_interactee')
scene.add(body_mesh_camera_wearer, 'body_mesh_camera_wearer')
r = pyrender.OffscreenRenderer(viewport_width=1920,
viewport_height=1080)
color, _ = r.render(scene)
color = color.astype(np.float32) / 255.0
img = pil_img.fromarray((color * 255).astype(np.uint8))
img = img.resize((int(1920 / args.scale), int(1080 / args.scale)))
img.save(os.path.join(body_scene_rendering_dir, frame_id + '.jpg'))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--release_data_root', type=str, default='/mnt/ssd/egobody_release', help='path to egobody dataset')
parser.add_argument('--save_root', type=str, default='/mnt/ssd', help='path to save renderings')
parser.add_argument('--recording_name', type=str, default='recording_20220225_S27_S26_01')
parser.add_argument('--view', type=str, default='master', choices=['master', 'sub_1', 'sub_2', 'sub_3', 'sub_4'])
parser.add_argument('--scene_name', type=str, default='cnb_dlab_0225')
parser.add_argument('--model_type', type=str, default='smplx', choices=['smplx', 'smpl'])
parser.add_argument('--save_undistorted_img', default='False', type=lambda x: x.lower() in ['true', '1'], help='save undistorted input image or not')
parser.add_argument('--scale', type=int, default=2, help='the scale to downsample output rendering images')
parser.add_argument('--start', type=int, default=0, help='id of the starting frame')
parser.add_argument('--step', type=int, default=1, help='id of the starting frame')
parser.add_argument('--model_folder', default='/mnt/hdd/PROX/body_models', type=str, help='path to smpl/smplx models')
parser.add_argument('--num_pca_comps', type=int, default=12)
parser.add_argument('--rendering_mode', default='3d', type=str, choices=['body', '3d', 'both'],
help='body: render gt body on egocentric images; 3d: render gt body in 3d scenes')
args = parser.parse_args()
main(args)