import numpy as np
import cv2
import random
from config import cfg
import math
from utils.human_models import smpl_x, smpl
from utils.transforms import cam2pixel, transform_joint_to_other_db
from plyfile import PlyData, PlyElement
import torch
def load_img(path, order='RGB'):
img = cv2.imread(path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if not isinstance(img, np.ndarray):
raise IOError("Fail to read %s" % path)
if order == 'RGB':
img = img[:, :, ::-1].copy()
img = img.astype(np.float32)
return img
def get_bbox(joint_img, joint_valid, extend_ratio=1.2):
x_img, y_img = joint_img[:, 0], joint_img[:, 1]
x_img = x_img[joint_valid == 1];
y_img = y_img[joint_valid == 1];
xmin = min(x_img);
ymin = min(y_img);
xmax = max(x_img);
ymax = max(y_img);
x_center = (xmin + xmax) / 2.;
width = xmax - xmin;
xmin = x_center - 0.5 * width * extend_ratio
xmax = x_center + 0.5 * width * extend_ratio
y_center = (ymin + ymax) / 2.;
height = ymax - ymin;
ymin = y_center - 0.5 * height * extend_ratio
ymax = y_center + 0.5 * height * extend_ratio
bbox = np.array([xmin, ymin, xmax - xmin, ymax - ymin]).astype(np.float32)
return bbox
def sanitize_bbox(bbox, img_width, img_height):
x, y, w, h = bbox
x1 = np.max((0, x))
y1 = np.max((0, y))
x2 = np.min((img_width - 1, x1 + np.max((0, w - 1))))
y2 = np.min((img_height - 1, y1 + np.max((0, h - 1))))
if w * h > 0 and x2 > x1 and y2 > y1:
bbox = np.array([x1, y1, x2 - x1, y2 - y1])
else:
bbox = None
return bbox
def process_bbox(bbox, img_width, img_height, ratio=1.25):
bbox = sanitize_bbox(bbox, img_width, img_height)
if bbox is None:
return bbox
# aspect ratio preserving bbox
w = bbox[2]
h = bbox[3]
c_x = bbox[0] + w / 2.
c_y = bbox[1] + h / 2.
aspect_ratio = cfg.input_img_shape[1] / cfg.input_img_shape[0]
if w > aspect_ratio * h:
h = w / aspect_ratio
elif w < aspect_ratio * h:
w = h * aspect_ratio
bbox[2] = w * ratio
bbox[3] = h * ratio
bbox[0] = c_x - bbox[2] / 2.
bbox[1] = c_y - bbox[3] / 2.
bbox = bbox.astype(np.float32)
return bbox
def get_aug_config():
scale_factor = 0.25
rot_factor = 30
color_factor = 0.2
scale = np.clip(np.random.randn(), -1.0, 1.0) * scale_factor + 1.0
rot = np.clip(np.random.randn(), -2.0,
2.0) * rot_factor if random.random() <= 0.6 else 0
c_up = 1.0 + color_factor
c_low = 1.0 - color_factor
color_scale = np.array([random.uniform(c_low, c_up), random.uniform(c_low, c_up), random.uniform(c_low, c_up)])
do_flip = random.random() <= 0.5
return scale, rot, color_scale, do_flip
def augmentation(img, bbox, data_split):
if getattr(cfg, 'no_aug', False):
scale, rot, color_scale, do_flip = 1.0, 0.0, np.array([1, 1, 1]), False
elif data_split == 'train':
scale, rot, color_scale, do_flip = get_aug_config()
else:
scale, rot, color_scale, do_flip = 1.0, 0.0, np.array([1, 1, 1]), False
img, trans, inv_trans = generate_patch_image(img, bbox, scale, rot, do_flip, cfg.input_img_shape)
img = np.clip(img * color_scale[None, None, :], 0, 255)
return img, trans, inv_trans, rot, do_flip
def generate_patch_image(cvimg, bbox, scale, rot, do_flip, out_shape):
img = cvimg.copy()
img_height, img_width, img_channels = img.shape
bb_c_x = float(bbox[0] + 0.5 * bbox[2])
bb_c_y = float(bbox[1] + 0.5 * bbox[3])
bb_width = float(bbox[2])
bb_height = float(bbox[3])
if do_flip:
img = img[:, ::-1, :]
bb_c_x = img_width - bb_c_x - 1
trans = gen_trans_from_patch_cv(bb_c_x, bb_c_y, bb_width, bb_height, out_shape[1], out_shape[0], scale, rot)
img_patch = cv2.warpAffine(img, trans, (int(out_shape[1]), int(out_shape[0])), flags=cv2.INTER_LINEAR)
img_patch = img_patch.astype(np.float32)
inv_trans = gen_trans_from_patch_cv(bb_c_x, bb_c_y, bb_width, bb_height, out_shape[1], out_shape[0], scale, rot,
inv=True)
return img_patch, trans, inv_trans
def rotate_2d(pt_2d, rot_rad):
x = pt_2d[0]
y = pt_2d[1]
sn, cs = np.sin(rot_rad), np.cos(rot_rad)
xx = x * cs - y * sn
yy = x * sn + y * cs
return np.array([xx, yy], dtype=np.float32)
def gen_trans_from_patch_cv(c_x, c_y, src_width, src_height, dst_width, dst_height, scale, rot, inv=False):
# augment size with scale
src_w = src_width * scale
src_h = src_height * scale
src_center = np.array([c_x, c_y], dtype=np.float32)
# augment rotation
rot_rad = np.pi * rot / 180
src_downdir = rotate_2d(np.array([0, src_h * 0.5], dtype=np.float32), rot_rad)
src_rightdir = rotate_2d(np.array([src_w * 0.5, 0], dtype=np.float32), rot_rad)
dst_w = dst_width
dst_h = dst_height
dst_center = np.array([dst_w * 0.5, dst_h * 0.5], dtype=np.float32)
dst_downdir = np.array([0, dst_h * 0.5], dtype=np.float32)
dst_rightdir = np.array([dst_w * 0.5, 0], dtype=np.float32)
src = np.zeros((3, 2), dtype=np.float32)
src[0, :] = src_center
src[1, :] = src_center + src_downdir
src[2, :] = src_center + src_rightdir
dst = np.zeros((3, 2), dtype=np.float32)
dst[0, :] = dst_center
dst[1, :] = dst_center + dst_downdir
dst[2, :] = dst_center + dst_rightdir
if inv:
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
else:
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
trans = trans.astype(np.float32)
return trans
def process_db_coord(joint_img, joint_cam, joint_valid, do_flip, img_shape, flip_pairs, img2bb_trans, rot,
src_joints_name, target_joints_name):
joint_img_original = joint_img.copy()
joint_img, joint_cam, joint_valid = joint_img.copy(), joint_cam.copy(), joint_valid.copy()
# flip augmentation
if do_flip:
joint_cam[:, 0] = -joint_cam[:, 0]
joint_img[:, 0] = img_shape[1] - 1 - joint_img[:, 0]
for pair in flip_pairs:
joint_img[pair[0], :], joint_img[pair[1], :] = joint_img[pair[1], :].copy(), joint_img[pair[0], :].copy()
joint_cam[pair[0], :], joint_cam[pair[1], :] = joint_cam[pair[1], :].copy(), joint_cam[pair[0], :].copy()
joint_valid[pair[0], :], joint_valid[pair[1], :] = joint_valid[pair[1], :].copy(), joint_valid[pair[0],
:].copy()
# 3D data rotation augmentation
rot_aug_mat = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
[np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],
[0, 0, 1]], dtype=np.float32)
joint_cam = np.dot(rot_aug_mat, joint_cam.transpose(1, 0)).transpose(1, 0)
# affine transformation
joint_img_xy1 = np.concatenate((joint_img[:, :2], np.ones_like(joint_img[:, :1])), 1)
joint_img[:, :2] = np.dot(img2bb_trans, joint_img_xy1.transpose(1, 0)).transpose(1, 0)
joint_img[:, 0] = joint_img[:, 0] / cfg.input_img_shape[1] * cfg.output_hm_shape[2]
joint_img[:, 1] = joint_img[:, 1] / cfg.input_img_shape[0] * cfg.output_hm_shape[1]
# check truncation
joint_trunc = joint_valid * ((joint_img_original[:, 0] > 0) * (joint_img[:, 0] >= 0) * (joint_img[:, 0] < cfg.output_hm_shape[2]) * \
(joint_img_original[:, 1] > 0) *(joint_img[:, 1] >= 0) * (joint_img[:, 1] < cfg.output_hm_shape[1]) * \
(joint_img_original[:, 2] > 0) *(joint_img[:, 2] >= 0) * (joint_img[:, 2] < cfg.output_hm_shape[0])).reshape(-1,
1).astype(
np.float32)
# transform joints to target db joints
joint_img = transform_joint_to_other_db(joint_img, src_joints_name, target_joints_name)
joint_cam_wo_ra = transform_joint_to_other_db(joint_cam, src_joints_name, target_joints_name)
joint_valid = transform_joint_to_other_db(joint_valid, src_joints_name, target_joints_name)
joint_trunc = transform_joint_to_other_db(joint_trunc, src_joints_name, target_joints_name)
# root-alignment, for joint_cam input wo ra
joint_cam_ra = joint_cam_wo_ra.copy()
joint_cam_ra = joint_cam_ra - joint_cam_ra[smpl_x.root_joint_idx, None, :] # root-relative
joint_cam_ra[smpl_x.joint_part['lhand'], :] = joint_cam_ra[smpl_x.joint_part['lhand'], :] - joint_cam_ra[
smpl_x.lwrist_idx, None,
:] # left hand root-relative
joint_cam_ra[smpl_x.joint_part['rhand'], :] = joint_cam_ra[smpl_x.joint_part['rhand'], :] - joint_cam_ra[
smpl_x.rwrist_idx, None,
:] # right hand root-relative
joint_cam_ra[smpl_x.joint_part['face'], :] = joint_cam_ra[smpl_x.joint_part['face'], :] - joint_cam_ra[smpl_x.neck_idx,
None,
:] # face root-relative
return joint_img, joint_cam_wo_ra, joint_cam_ra, joint_valid, joint_trunc
def process_human_model_output(human_model_param, cam_param, do_flip, img_shape, img2bb_trans, rot, human_model_type, joint_img=None):
if human_model_type == 'smplx':
human_model = smpl_x
rotation_valid = np.ones((smpl_x.orig_joint_num), dtype=np.float32)
coord_valid = np.ones((smpl_x.joint_num), dtype=np.float32)
root_pose, body_pose, shape, trans = human_model_param['root_pose'], human_model_param['body_pose'], \
human_model_param['shape'], human_model_param['trans']
if 'lhand_pose' in human_model_param and human_model_param['lhand_valid']:
lhand_pose = human_model_param['lhand_pose']
else:
lhand_pose = np.zeros((3 * len(smpl_x.orig_joint_part['lhand'])), dtype=np.float32)
rotation_valid[smpl_x.orig_joint_part['lhand']] = 0
coord_valid[smpl_x.joint_part['lhand']] = 0
if 'rhand_pose' in human_model_param and human_model_param['rhand_valid']:
rhand_pose = human_model_param['rhand_pose']
else:
rhand_pose = np.zeros((3 * len(smpl_x.orig_joint_part['rhand'])), dtype=np.float32)
rotation_valid[smpl_x.orig_joint_part['rhand']] = 0
coord_valid[smpl_x.joint_part['rhand']] = 0
if 'jaw_pose' in human_model_param and 'expr' in human_model_param and human_model_param['face_valid']:
jaw_pose = human_model_param['jaw_pose']
expr = human_model_param['expr']
expr_valid = True
else:
jaw_pose = np.zeros((3), dtype=np.float32)
expr = np.zeros((smpl_x.expr_code_dim), dtype=np.float32)
rotation_valid[smpl_x.orig_joint_part['face']] = 0
coord_valid[smpl_x.joint_part['face']] = 0
expr_valid = False
if 'gender' in human_model_param:
gender = human_model_param['gender']
else:
gender = 'neutral'
root_pose = torch.FloatTensor(root_pose).view(1, 3) # (1,3)
body_pose = torch.FloatTensor(body_pose).view(-1, 3) # (21,3)
lhand_pose = torch.FloatTensor(lhand_pose).view(-1, 3) # (15,3)
rhand_pose = torch.FloatTensor(rhand_pose).view(-1, 3) # (15,3)
jaw_pose = torch.FloatTensor(jaw_pose).view(-1, 3) # (1,3)
shape = torch.FloatTensor(shape).view(1, -1) # SMPLX shape parameter
expr = torch.FloatTensor(expr).view(1, -1) # SMPLX expression parameter
trans = torch.FloatTensor(trans).view(1, -1) # translation vector
# apply camera extrinsic (rotation)
# merge root pose and camera rotation
if 'R' in cam_param:
R = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3)
root_pose = root_pose.numpy()
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(R, root_pose))
root_pose = torch.from_numpy(root_pose).view(1, 3)
# get mesh and joint coordinates
zero_pose = torch.zeros((1, 3)).float() # eye poses
with torch.no_grad():
output = smpl_x.layer[gender](betas=shape, body_pose=body_pose.view(1, -1), global_orient=root_pose,
transl=trans, left_hand_pose=lhand_pose.view(1, -1),
right_hand_pose=rhand_pose.view(1, -1), jaw_pose=jaw_pose.view(1, -1),
leye_pose=zero_pose, reye_pose=zero_pose, expression=expr)
mesh_cam = output.vertices[0].numpy()
joint_cam = output.joints[0].numpy()[smpl_x.joint_idx, :]
# apply camera exrinsic (translation)
# compenstate rotation (translation from origin to root joint was not cancled)
if 'R' in cam_param and 't' in cam_param:
R, t = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3), np.array(cam_param['t'],
dtype=np.float32).reshape(1, 3)
root_cam = joint_cam[smpl_x.root_joint_idx, None, :]
joint_cam = joint_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
mesh_cam = mesh_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
# concat root, body, two hands, and jaw pose
pose = torch.cat((root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose))
# joint coordinates
if 'focal' not in cam_param or 'princpt' not in cam_param:
assert joint_img is not None
else:
joint_img = cam2pixel(joint_cam, cam_param['focal'], cam_param['princpt'])
joint_img_original = joint_img.copy()
joint_cam = joint_cam - joint_cam[smpl_x.root_joint_idx, None, :] # root-relative
joint_cam[smpl_x.joint_part['lhand'], :] = joint_cam[smpl_x.joint_part['lhand'], :] - joint_cam[
smpl_x.lwrist_idx, None,
:] # left hand root-relative
joint_cam[smpl_x.joint_part['rhand'], :] = joint_cam[smpl_x.joint_part['rhand'], :] - joint_cam[
smpl_x.rwrist_idx, None,
:] # right hand root-relative
joint_cam[smpl_x.joint_part['face'], :] = joint_cam[smpl_x.joint_part['face'], :] - joint_cam[smpl_x.neck_idx,
None,
:] # face root-relative
joint_img[smpl_x.joint_part['body'], 2] = (joint_cam[smpl_x.joint_part['body'], 2].copy() / (
cfg.body_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0] # body depth discretize
joint_img[smpl_x.joint_part['lhand'], 2] = (joint_cam[smpl_x.joint_part['lhand'], 2].copy() / (
cfg.hand_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0] # left hand depth discretize
joint_img[smpl_x.joint_part['rhand'], 2] = (joint_cam[smpl_x.joint_part['rhand'], 2].copy() / (
cfg.hand_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0] # right hand depth discretize
joint_img[smpl_x.joint_part['face'], 2] = (joint_cam[smpl_x.joint_part['face'], 2].copy() / (
cfg.face_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0] # face depth discretize
elif human_model_type == 'smpl':
human_model = smpl
pose, shape, trans = human_model_param['pose'], human_model_param['shape'], human_model_param['trans']
if 'gender' in human_model_param:
gender = human_model_param['gender']
else:
gender = 'neutral'
pose = torch.FloatTensor(pose).view(-1, 3)
shape = torch.FloatTensor(shape).view(1, -1);
trans = torch.FloatTensor(trans).view(1, -1) # translation vector
# apply camera extrinsic (rotation)
# merge root pose and camera rotation
if 'R' in cam_param:
R = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3)
root_pose = pose[smpl.orig_root_joint_idx, :].numpy()
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(R, root_pose))
pose[smpl.orig_root_joint_idx] = torch.from_numpy(root_pose).view(3)
# get mesh and joint coordinates
root_pose = pose[smpl.orig_root_joint_idx].view(1, 3)
body_pose = torch.cat((pose[:smpl.orig_root_joint_idx, :], pose[smpl.orig_root_joint_idx + 1:, :])).view(1, -1)
with torch.no_grad():
output = smpl.layer[gender](betas=shape, body_pose=body_pose, global_orient=root_pose, transl=trans)
mesh_cam = output.vertices[0].numpy()
joint_cam = np.dot(smpl.joint_regressor, mesh_cam)
# apply camera exrinsic (translation)
# compenstate rotation (translation from origin to root joint was not cancled)
if 'R' in cam_param and 't' in cam_param:
R, t = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3), np.array(cam_param['t'],
dtype=np.float32).reshape(1, 3)
root_cam = joint_cam[smpl.root_joint_idx, None, :]
joint_cam = joint_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
mesh_cam = mesh_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
# joint coordinates
if 'focal' not in cam_param or 'princpt' not in cam_param:
assert joint_img is not None
else:
joint_img = cam2pixel(joint_cam, cam_param['focal'], cam_param['princpt'])
joint_img_original = joint_img.copy()
joint_cam = joint_cam - joint_cam[smpl.root_joint_idx, None, :] # body root-relative
joint_img[:, 2] = (joint_cam[:, 2].copy() / (cfg.body_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[
0] # body depth discretize
elif human_model_type == 'mano':
human_model = mano
pose, shape, trans = human_model_param['pose'], human_model_param['shape'], human_model_param['trans']
hand_type = human_model_param['hand_type']
pose = torch.FloatTensor(pose).view(-1, 3)
shape = torch.FloatTensor(shape).view(1, -1);
trans = torch.FloatTensor(trans).view(1, -1) # translation vector
# apply camera extrinsic (rotation)
# merge root pose and camera rotation
if 'R' in cam_param:
R = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3)
root_pose = pose[mano.orig_root_joint_idx, :].numpy()
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(R, root_pose))
pose[mano.orig_root_joint_idx] = torch.from_numpy(root_pose).view(3)
# get mesh and joint coordinates
root_pose = pose[mano.orig_root_joint_idx].view(1, 3)
hand_pose = torch.cat((pose[:mano.orig_root_joint_idx, :], pose[mano.orig_root_joint_idx + 1:, :])).view(1, -1)
with torch.no_grad():
output = mano.layer[hand_type](betas=shape, hand_pose=hand_pose, global_orient=root_pose, transl=trans)
mesh_cam = output.vertices[0].numpy()
joint_cam = np.dot(mano.joint_regressor, mesh_cam)
# apply camera exrinsic (translation)
# compenstate rotation (translation from origin to root joint was not cancled)
if 'R' in cam_param and 't' in cam_param:
R, t = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3), np.array(cam_param['t'],
dtype=np.float32).reshape(1, 3)
root_cam = joint_cam[mano.root_joint_idx, None, :]
joint_cam = joint_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
mesh_cam = mesh_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
# joint coordinates
if 'focal' not in cam_param or 'princpt' not in cam_param:
assert joint_img is not None
else:
joint_img = cam2pixel(joint_cam, cam_param['focal'], cam_param['princpt'])
joint_cam = joint_cam - joint_cam[mano.root_joint_idx, None, :] # hand root-relative
joint_img[:, 2] = (joint_cam[:, 2].copy() / (cfg.hand_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[
0] # hand depth discretize
mesh_cam_orig = mesh_cam.copy() # back-up the original one
## so far, data augmentations are not applied yet
## now, apply data augmentations
# image projection
if do_flip:
joint_cam[:, 0] = -joint_cam[:, 0]
joint_img[:, 0] = img_shape[1] - 1 - joint_img[:, 0]
for pair in human_model.flip_pairs:
joint_cam[pair[0], :], joint_cam[pair[1], :] = joint_cam[pair[1], :].copy(), joint_cam[pair[0], :].copy()
joint_img[pair[0], :], joint_img[pair[1], :] = joint_img[pair[1], :].copy(), joint_img[pair[0], :].copy()
if human_model_type == 'smplx':
coord_valid[pair[0]], coord_valid[pair[1]] = coord_valid[pair[1]].copy(), coord_valid[pair[0]].copy()
# x,y affine transform, root-relative depth
joint_img_xy1 = np.concatenate((joint_img[:, :2], np.ones_like(joint_img[:, 0:1])), 1)
joint_img[:, :2] = np.dot(img2bb_trans, joint_img_xy1.transpose(1, 0)).transpose(1, 0)[:, :2]
joint_img[:, 0] = joint_img[:, 0] / cfg.input_img_shape[1] * cfg.output_hm_shape[2]
joint_img[:, 1] = joint_img[:, 1] / cfg.input_img_shape[0] * cfg.output_hm_shape[1]
# check truncation
# TODO
joint_trunc = ((joint_img_original[:, 0] > 0) * (joint_img[:, 0] >= 0) * (joint_img[:, 0] < cfg.output_hm_shape[2]) * \
(joint_img_original[:, 1] > 0) * (joint_img[:, 1] >= 0) * (joint_img[:, 1] < cfg.output_hm_shape[1]) * \
(joint_img_original[:, 2] > 0) * (joint_img[:, 2] >= 0) * (joint_img[:, 2] < cfg.output_hm_shape[0])).reshape(-1, 1).astype(
np.float32)
# 3D data rotation augmentation
rot_aug_mat = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
[np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],
[0, 0, 1]], dtype=np.float32)
# coordinate
joint_cam = np.dot(rot_aug_mat, joint_cam.transpose(1, 0)).transpose(1, 0)
# parameters
# flip pose parameter (axis-angle)
if do_flip:
for pair in human_model.orig_flip_pairs:
pose[pair[0], :], pose[pair[1], :] = pose[pair[1], :].clone(), pose[pair[0], :].clone()
if human_model_type == 'smplx':
rotation_valid[pair[0]], rotation_valid[pair[1]] = rotation_valid[pair[1]].copy(), rotation_valid[
pair[0]].copy()
pose[:, 1:3] *= -1 # multiply -1 to y and z axis of axis-angle
# rotate root pose
pose = pose.numpy()
root_pose = pose[human_model.orig_root_joint_idx, :]
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(rot_aug_mat, root_pose))
pose[human_model.orig_root_joint_idx] = root_pose.reshape(3)
# change to mean shape if beta is too far from it
shape[(shape.abs() > 3).any(dim=1)] = 0.
shape = shape.numpy().reshape(-1)
# return results
if human_model_type == 'smplx':
pose = pose.reshape(-1)
expr = expr.numpy().reshape(-1)
return joint_img, joint_cam, joint_trunc, pose, shape, expr, rotation_valid, coord_valid, expr_valid, mesh_cam_orig
elif human_model_type == 'smpl':
pose = pose.reshape(-1)
return joint_img, joint_cam, joint_trunc, pose, shape, mesh_cam_orig
elif human_model_type == 'mano':
pose = pose.reshape(-1)
return joint_img, joint_cam, joint_trunc, pose, shape, mesh_cam_orig
def get_fitting_error_3D(db_joint, db_joint_from_fit, joint_valid):
# mask coordinate
db_joint = db_joint[np.tile(joint_valid, (1, 3)) == 1].reshape(-1, 3)
db_joint_from_fit = db_joint_from_fit[np.tile(joint_valid, (1, 3)) == 1].reshape(-1, 3)
db_joint_from_fit = db_joint_from_fit - np.mean(db_joint_from_fit, 0)[None, :] + np.mean(db_joint, 0)[None,
:] # translation alignment
error = np.sqrt(np.sum((db_joint - db_joint_from_fit) ** 2, 1)).mean()
return error
def load_obj(file_name):
v = []
obj_file = open(file_name)
for line in obj_file:
words = line.split(' ')
if words[0] == 'v':
x, y, z = float(words[1]), float(words[2]), float(words[3])
v.append(np.array([x, y, z]))
return np.stack(v)
def load_ply(file_name):
plydata = PlyData.read(file_name)
x = plydata['vertex']['x']
y = plydata['vertex']['y']
z = plydata['vertex']['z']
v = np.stack((x, y, z), 1)
return v
def resize_bbox(bbox, scale=1.2):
if isinstance(bbox, list):
x1, y1, x2, y2 = bbox[0], bbox[1], bbox[2], bbox[3]
else:
x1, y1, x2, y2 = bbox
x_center = (x1+x2)/2.0
y_center = (y1+y2)/2.0
x_size, y_size = x2-x1, y2-y1
x1_resize = x_center-x_size/2.0*scale
x2_resize = x_center+x_size/2.0*scale
y1_resize = y_center - y_size / 2.0 * scale
y2_resize = y_center + y_size / 2.0 * scale
bbox[0], bbox[1], bbox[2], bbox[3] = x1_resize, y1_resize, x2_resize, y2_resize
return bbox