def bvh_reproj(args):
anim, names, frametime = BVH.load(args.bvh_path)
positions = Animation.positions_global(anim)
print(positions.shape)
camera_data = load_camera(args.json_path)
for cam_name in camera_data.keys():
if args.multi_process:
Process(target=bvh_reproj_for_cam,
args=(args, cam_name, camera_data, positions,
anim)).start()
else:
bvh_reproj_for_cam(args, cam_name, camera_data, positions, anim)
def __init__(self, config, is_train=True):
poses_3d_root, rotations, bones, alphas, contacts, projections = [], [], [], [], [], []
self.frames = []
self.config = config
self.rotation_number = ROTATION_NUMBERS.get(config.arch.rotation_type)
datasets = ['bvh'] #, 'bvh']
if 'h36m' in datasets:
dim_to_use_3d = h36m_utils.dimension_reducer(
3, config.arch.predict_joints)
subjects = h36m_utils.TRAIN_SUBJECTS if is_train else h36m_utils.TEST_SUBJECTS
actions = h36m_utils.define_actions('All')
self.cameras = h36m_utils.load_cameras(config.trainer.data_path)
for subject in subjects:
for action in actions:
for subaction in range(1, 3):
data_file = h5py.File(
'%s/S%s/%s-%s/annot.h5' %
(config.trainer.data_path, subject, action,
subaction), 'r')
data_size = data_file['frame'].size / 4
data_set = np.array(data_file['pose/3d']).reshape(
(-1, 96))[:, dim_to_use_3d]
for i in range(4):
camera_name = data_file['camera'][int(data_size *
i)]
R, T, f, c, k, p, res_w, res_h = self.cameras[(
subject, str(camera_name))]
set_3d = data_set[int(data_size *
i):int(data_size *
(i + 1))].copy()
set_3d_world = h36m_utils.camera_to_world_frame(
set_3d.reshape((-1, 3)), R, T)
# set_3d_world[:, [1, 2]] = set_3d_world[:, [2, 1]]
# set_3d_world[:, [2]] *= -1
# set_3d_world = set_3d_world.reshape((-1, config.arch.predict_joints * 3))
set_3d_root = set_3d_world - np.tile(
set_3d_world[:, :3],
[1, int(set_3d_world.shape[-1] / 3)])
set_bones = self.get_bones(
set_3d_root, config.arch.predict_joints)
set_alphas = np.mean(set_bones, axis=1)
self.frames.append(set_3d_root.shape[0])
poses_3d_root.append(
set_3d_root /
np.expand_dims(set_alphas, axis=-1))
rotations.append(
np.zeros((set_3d_root.shape[0],
int(set_3d_root.shape[1] / 3 *
self.rotation_number))))
bones.append(set_bones /
np.expand_dims(set_alphas, axis=-1))
alphas.append(set_alphas)
contacts.append(
self.get_contact(set_3d_world,
config.arch.predict_joints))
projections.append(
(set_3d_world.copy() /
np.expand_dims(set_alphas, axis=-1)).reshape(
(set_3d_world.shape[0], -1, 3))[:, 0, 2])
if 'bvh' in datasets:
to_keep = [
0, 7, 8, 9, 2, 3, 4, 12, 15, 18, 19, 20, 25, 26, 27
] if config.arch.predict_joints == 15 else [
0, 7, 8, 9, 2, 3, 4, 12, 13, 15, 16, 18, 19, 20, 25, 26, 27
]
parents = [
-1, 0, 1, 2, 0, 4, 5, 0, 7, 7, 9, 10, 7, 12, 13
] if config.arch.predict_joints == 15 else [
-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15
]
bvh_files = util.make_dataset(['/mnt/dataset/test_bvh'],
phase='bvh',
data_split=1)
bvh_files = bvh_files[:int(len(bvh_files) *
0.8)] if is_train else bvh_files[
int(len(bvh_files) * 0.8):]
for bvh_file in bvh_files:
original_anim, joint_names, frame_rate = BVH.load(bvh_file)
set_skel_in = original_anim.positions[:, to_keep, :]
set_rotations = original_anim.rotations.qs[:, to_keep, :]
anim = Animation.Animation(
Quaternions(set_rotations), set_skel_in,
original_anim.orients.qs[to_keep, :], set_skel_in,
np.array(parents))
set_3d_world = Animation.positions_global(anim).reshape(
set_rotations.shape[0], -1)
set_3d_world[:, 0:3] = (set_3d_world[:, 3:6] +
set_3d_world[:, 12:15]) / 2
set_3d_root = set_3d_world - np.tile(
set_3d_world[:, :3],
[1, int(set_3d_world.shape[-1] / 3)])
set_bones = self.get_bones(set_3d_root,
config.arch.predict_joints)
set_alphas = np.mean(set_bones, axis=1)
self.frames.append(set_3d_root.shape[0])
poses_3d_root.append(set_3d_root /
np.expand_dims(set_alphas, axis=-1))
rotations.append(
np.zeros((set_3d_root.shape[0],
int(set_3d_root.shape[1] / 3 *
self.rotation_number))))
bones.append(set_bones / np.expand_dims(set_alphas, axis=-1))
alphas.append(set_alphas)
contacts.append(
self.get_contact(set_3d_world, config.arch.predict_joints))
projections.append(
(set_3d_world.copy() /
np.expand_dims(set_alphas, axis=-1)).reshape(
(set_3d_world.shape[0], -1, 3))[:, 0, 2])
self.poses_3d = np.concatenate(poses_3d_root, axis=0)
self.rotations = np.concatenate(rotations, axis=0)
self.bones = np.concatenate(bones, axis=0)
self.alphas = np.concatenate(alphas, axis=0)
self.contacts = np.concatenate(contacts, axis=0)
self.projections = np.concatenate(projections, axis=0)
posed_3d_flip = self.get_flipping(self.poses_3d, 3,
config.arch.predict_joints)
if config.trainer.data_aug_flip and is_train:
self.poses_3d = np.concatenate([self.poses_3d, posed_3d_flip],
axis=0)
self.poses_2d = self.get_projection(self.poses_3d)
self.poses_2d_root = (self.poses_2d -
self.poses_2d[:, 0, None]).reshape(
(self.poses_3d.shape[0], -1))
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from utils import visualization
fig = plt.figure()
gs = gridspec.GridSpec(1, 2)
for i in range(1):
ax1 = plt.subplot(gs[0], projection='3d')
visualization.show3Dpose(self.poses_3d[i], ax1, radius=5)
ax2 = plt.subplot(gs[1])
visualization.show2Dpose(self.poses_2d_root[i] * 1000 + 500,
ax2,
radius=1000)
fig.savefig('./images/2d_3d/_%d.png' % i)
fig.clear()
self.update_sequence_index()
import sys
sys.path.append('./')
import numpy as np
import utils.BVH as BVH
from utils.Quaternions import Quaternions
from utils import util
rotations_bvh = []
bvh_files = util.make_dataset(['/mnt/dataset/cmubvh'], phase='bvh', data_split=1, sort_index=0)
for file in bvh_files:
original_anim, _, frametime = BVH.load(file, rotate=True)
sampling = 3
to_keep = [0, 7, 8, 2, 3, 12, 13, 15, 18, 19, 25, 26]
real_rotations = original_anim.rotations.qs[1:, to_keep, :]
rotations_bvh.append(real_rotations[np.arange(0, real_rotations.shape[0] // sampling) * sampling].astype('float32'))
np.savez_compressed('./data/data_cmu.npz', rotations=rotations_bvh)