def get_coco_from_mesh(self, mesh_coord_cam, cam_param):
# regress coco joints
joint_coord_cam = np.dot(self.joint_regressor_coco, mesh_coord_cam)
joint_coord_cam = self.add_pelvis_and_neck(joint_coord_cam)
# projection
f, c = cam_param['focal'], cam_param['princpt']
joint_coord_img = cam2pixel(joint_coord_cam, f, c)
joint_coord_img[:, 2] = 1
return joint_coord_cam, joint_coord_img
def get_coco_from_mesh(self, mesh_coord_cam, cam_param):
# regress coco joints
mesh = torch.Tensor(mesh_coord_cam)
joint_coord_cam = torch.matmul(self.joint_regressor_coco, mesh)
joint_coord_cam = joint_coord_cam.numpy()
joint_coord_cam = self.add_pelvis_and_neck(joint_coord_cam)
# projection
f, c = cam_param['focal'], cam_param['princpt']
joint_coord_img = cam2pixel(joint_coord_cam, f, c)
joint_coord_img[:, 2] = 1
return joint_coord_cam, joint_coord_img
def get_joints_from_mesh(self, mesh, joint_set_name, cam_param):
joint_coord_cam = None
if joint_set_name == 'human36':
joint_coord_cam = np.dot(self.joint_regressor_h36m, mesh)
elif joint_set_name == 'coco':
joint_coord_cam = np.dot(self.joint_regressor_coco, mesh)
joint_coord_cam = self.add_pelvis_and_neck(joint_coord_cam)
# projection
f, c = np.array(cam_param['focal'],
dtype=np.float32), np.array(cam_param['princpt'],
dtype=np.float32)
joint_coord_img = cam2pixel(joint_coord_cam / 1000, f, c)
joint_coord_img[:, 2] = 1
return joint_coord_cam, joint_coord_img
def __getitem__(self, idx):
data = copy.deepcopy(self.datalist[idx])
img_id, img_name, cam_param, bbox, smpl_param, img_shape = data[
'img_id'], data['img_name'], data['cam_param'], data['bbox'], data[
'smpl_param'], data['img_shape']
flip, rot = augm_params(is_train=(self.data_split == 'train'))
# smpl coordinates
smpl_mesh_coord_cam, smpl_joint_coord_cam = self.get_smpl_coord(
smpl_param)
smpl_coord_cam = np.concatenate(
(smpl_mesh_coord_cam, smpl_joint_coord_cam))
smpl_coord_img = cam2pixel(smpl_coord_cam, cam_param['focal'],
cam_param['princpt'])
joint_coord_img = smpl_coord_img[self.smpl_vertex_num:][:, :2]
# vis_2d_pose(joint_coord_img, img_path, self.smpl_skeleton, prefix='gt')
# root relative cam coord
smpl_coord_cam = smpl_coord_cam - smpl_coord_cam[
self.smpl_vertex_num + self.smpl_root_joint_idx]
mesh_coord_cam = smpl_coord_cam[:self.smpl_vertex_num]
joint_coord_cam = smpl_coord_cam[self.smpl_vertex_num:]
if not cfg.DATASET.use_gt_input:
# train / test with 2d dection input
det_data = self.datalist_pose2d_det[idx]
assert img_id == det_data['img_id']
joint_coord_img = det_data['img_joint']
# vis_2d_pose(joint_coord_img, img_path, self.smpl_skeleton, prefix='det')
# vis_3d_pose(joint_coord_cam, self.smpl_skeleton, joint_set_name='smpl')
# make new bbox
bbox = get_bbox(joint_coord_img)
bbox = process_bbox(bbox.copy())
# aug
joint_coord_img, trans = j2d_processing(
joint_coord_img.copy(),
(cfg.MODEL.input_shape[1], cfg.MODEL.input_shape[0]), bbox, rot,
flip, self.flip_pairs)
joint_coord_cam = j3d_processing(joint_coord_cam, rot, flip,
self.flip_pairs)
# -> 0~1
joint_coord_img = joint_coord_img[:, :2]
joint_coord_img /= np.array(
[[cfg.MODEL.input_shape[1], cfg.MODEL.input_shape[0]]])
# normalize loc&scale
mean, std = np.mean(joint_coord_img, axis=0), np.std(joint_coord_img,
axis=0)
joint_coord_img = (joint_coord_img.copy() - mean) / std
if cfg.MODEL.name == 'pose2mesh_net':
# default valid
mesh_valid = np.ones((len(mesh_coord_cam), 1), dtype=np.float32)
reg_joint_valid = np.ones((len(joint_coord_cam), 1),
dtype=np.float32)
lift_joint_valid = np.ones((len(joint_coord_cam), 1),
dtype=np.float32)
inputs = {'pose2d': joint_coord_img}
targets = {
'mesh': mesh_coord_cam / 1000,
'lift_pose3d': joint_coord_cam,
'reg_pose3d': joint_coord_cam
}
meta = {
'mesh_valid': mesh_valid,
'lift_pose3d_valid': lift_joint_valid,
'reg_pose3d_valid': reg_joint_valid
}
elif cfg.MODEL.name == 'posenet':
# default valid
joint_valid = np.ones((len(joint_coord_cam), 1), dtype=np.float32)
return joint_coord_img, joint_coord_cam, joint_valid
return inputs, targets, meta
def load_data(self):
print('Load annotations of Human36M Protocol ' + str(self.protocol))
subject_list = self.get_subject()
sampling_ratio = self.get_subsampling_ratio()
# aggregate annotations from each subject
db = COCO()
cameras = {}
joints = {}
smpl_params = {}
for subject in subject_list:
# data load
with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_data.json'), 'r') as f:
annot = json.load(f)
if len(db.dataset) == 0:
for k, v in annot.items():
db.dataset[k] = v
else:
for k, v in annot.items():
db.dataset[k] += v
# camera load
with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_camera.json'), 'r') as f:
cameras[str(subject)] = json.load(f)
# joint coordinate load
with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_joint_3d.json'), 'r') as f:
joints[str(subject)] = json.load(f)
# smpl parameter load
with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_smpl_param.json'), 'r') as f:
smpl_params[str(subject)] = json.load(f)
db.createIndex()
skip_idx = []
datalist = []
skip_img_idx = []
for aid in db.anns.keys():
ann = db.anns[aid]
image_id = ann['image_id']
img = db.loadImgs(image_id)[0]
img_path = osp.join(self.img_dir, img['file_name'])
img_name = img_path.split('/')[-1]
# check subject and frame_idx
frame_idx = img['frame_idx'];
if frame_idx % sampling_ratio != 0:
continue
# check smpl parameter exist
subject = img['subject'];
action_idx = img['action_idx'];
subaction_idx = img['subaction_idx'];
frame_idx = img['frame_idx'];
try:
smpl_param = smpl_params[str(subject)][str(action_idx)][str(subaction_idx)][str(frame_idx)]
except KeyError:
skip_idx.append(image_id)
skip_img_idx.append(img_path.split('/')[-1])
continue
smpl_param['gender'] = 'neutral' # self.subject_genders[subject] # set corresponding gender
# camera parameter
cam_idx = img['cam_idx']
cam_param = cameras[str(subject)][str(cam_idx)]
R, t, f, c = np.array(cam_param['R'], dtype=np.float32), np.array(cam_param['t'],
dtype=np.float32), np.array(
cam_param['f'], dtype=np.float32), np.array(cam_param['c'], dtype=np.float32)
cam_param = {'R': R, 't': t, 'focal': f, 'princpt': c}
# project world coordinate to cam, image coordinate space
joint_world = np.array(joints[str(subject)][str(action_idx)][str(subaction_idx)][str(frame_idx)],
dtype=np.float32)
joint_cam = world2cam(joint_world, R, t)
joint_img = cam2pixel(joint_cam, f, c)
joint_vis = np.ones((self.human36_joint_num, 1))
bbox = process_bbox(np.array(ann['bbox']))
if bbox is None: continue
datalist.append({
'img_path': img_path,
'img_name': img_name,
'img_id': image_id,
'bbox': bbox,
'img_hw': (img['height'], img['width']),
'joint_img': joint_img, # [x_img, y_img, z_cam]
'joint_cam': joint_cam, # [X, Y, Z] in camera coordinate
'joint_vis': joint_vis,
'smpl_param': smpl_param,
'cam_param': cam_param})
datalist = sorted(datalist, key=lambda x: x['img_name'])
return datalist, skip_idx, skip_img_idx
