def train_dataloader(self):
if self.hparams.no_data_aug:
tfms = None
else:
tfms = augmentation(0.9)
return self.__dataloader(tfms, self.train_fnames)
def __getitem__(self, idx):
data = copy.deepcopy(self.datalist[idx])
img_path, skeleton_path, original_shape, action_label, frame_num, start_frame_idx = data[
'img_path'], data['skeleton_path'], data['original_shape'], data[
'action_label'], data['frame_num'], data['start_frame_idx']
# video load
video, skeleton_frame_idxs = load_video(img_path, frame_num,
start_frame_idx)
resized_shape = video.shape[1:3]
# augmentation
video, img2aug_trans, aug2img_trans, do_flip = augmentation(
video, self.data_split)
video = video.transpose(0, 3, 1, 2).astype(
np.float32) / 255. # frame_num, channel_dim, height, width
# skeleton information load
pose_coords, pose_scores = self.load_skeleton(skeleton_path,
skeleton_frame_idxs,
original_shape,
resized_shape)
# process skeleton information
pose_coords, pose_scores = process_skeleton(pose_coords, pose_scores,
img2aug_trans, do_flip,
self.flip_pairs,
self.joint_num,
resized_shape)
"""
# for debug
# keypoint visualization
for i in range(cfg.frame_per_seg):
img = video[i,::-1,:,:].transpose(1,2,0) * 255
person_num = len(pose_coords[i])
for p in range(person_num):
#for j in range(self.joint_num):
#coord = (int(pose_coords[i][p][j][0]), int(pose_coords[i][p][j][1]))
#cv2.circle(img, coord, radius=3, color=(255,0,0), thickness=-1, lineType=cv2.LINE_AA)
#cv2.imwrite(str(idx) + '_' + str(action_label) + '_' + str(i) + '_' + str(j) + '.jpg', img)
coord = pose_coords[i][p].copy()
coord[:,0] = coord[:,0] / cfg.input_hm_shape[1] * cfg.input_img_shape[1]
coord[:,1] = coord[:,1] / cfg.input_hm_shape[0] * cfg.input_img_shape[0]
img = vis_keypoints(img, pose_coords[i][p] * 4, self.skeleton)
cv2.imwrite(str(idx) + '_' + str(action_label) + '_' + str(i) + '.jpg', img)
"""
inputs = {
'video': video,
'pose_coords': pose_coords,
'pose_scores': pose_scores
}
targets = {'action_label': action_label}
meta_info = {'img_id': data['img_id']}
return inputs, targets, meta_info
def __getitem__(self, idx):
data = self.datalist[idx]
img_path, bbox, joint, hand_type, hand_type_valid = data[
'img_path'], data['bbox'], data['joint'], data['hand_type'], data[
'hand_type_valid']
joint_cam = joint['cam_coord'].copy()
joint_img = joint['img_coord'].copy()
joint_valid = joint['valid'].copy()
hand_type = self.handtype_str2array(hand_type)
joint_coord = np.concatenate((joint_img, joint_cam[:, 2, None]), 1)
# image load
img = load_img(img_path)
# augmentation
img, joint_coord, joint_valid, hand_type, inv_trans = augmentation(
img, bbox, joint_coord, joint_valid, hand_type, self.mode,
self.joint_type)
rel_root_depth = np.array([
joint_coord[self.root_joint_idx['left'], 2] -
joint_coord[self.root_joint_idx['right'], 2]
],
dtype=np.float32).reshape(1)
root_valid = np.array(
[
joint_valid[self.root_joint_idx['right']] *
joint_valid[self.root_joint_idx['left']]
],
dtype=np.float32).reshape(
1) if hand_type[0] * hand_type[1] == 1 else np.zeros(
(1), dtype=np.float32)
# transform to output heatmap space
joint_coord, joint_valid, rel_root_depth, root_valid = transform_input_to_output_space(
joint_coord, joint_valid, rel_root_depth, root_valid,
self.root_joint_idx, self.joint_type)
img = self.transform(img.astype(np.float32)) / 255.
inputs = {'img': img}
targets = {
'joint_coord': joint_coord,
'rel_root_depth': rel_root_depth,
'hand_type': hand_type
}
meta_info = {
'joint_valid': joint_valid,
'root_valid': root_valid,
'hand_type_valid': hand_type_valid,
'inv_trans': inv_trans,
'capture': int(data['capture']),
'cam': int(data['cam']),
'frame': int(data['frame'])
}
return inputs, targets, meta_info
def __getitem__(self, idx):
data = copy.deepcopy(self.datalist[idx])
img_path, bbox, smpl_param = data['img_path'], data['bbox'], data['smpl_param']
# img
img = load_img(img_path)
img, img2bb_trans, bb2img_trans, _, _ = augmentation(img, bbox, self.data_split)
img = self.transform(img.astype(np.float32))/255.
# smpl coordinates
smpl_mesh_cam, smpl_joint_cam = self.get_smpl_coord(smpl_param)
inputs = {'img': img}
targets = {'fit_mesh_coord_cam': smpl_mesh_cam}
meta_info = {'bb2img_trans': bb2img_trans}
return inputs, targets, meta_info
def get_batch_from_txt_files_(self):
self.index += 1
#for i, f in enumerate(fs):
img = load_image(osp.join('/home/ubuntu/3d-testing/InterHand2.6M/main/', self.fs[self.index]).strip())
box = self.bs[self.index].split(",")
bbox = []
for b in box:
bbox.append(float(b.replace("\n", "")))
img, inv_trans = augmentation(img, np.array(bbox), None, None, None, 'test', None)
img = self.transform(img.astype(np.float32))/255.
# image = cv2.imread(osp.join('/home/ubuntu/3d-testing/InterHand2.6M/main/', self.fs[self.index]).strip())
# image = cv2.rectangle(image, (int(bbox[0]), int(bbox[1])), (int(bbox[0])+int(bbox[2]), int(bbox[0]) + int(bbox[3])), (255,0,0), 3)
# print(cv2.imwrite("bbox_testing/" + str(self.index)+".jpg", image))
return img, osp.join('/home/ubuntu/3d-testing/InterHand2.6M/main/', self.fs[self.index]).strip(), inv_trans
def __getitem__(self, idx):
data = copy.deepcopy(self.datalist[idx])
img_path, img_shape, bbox, joint_cam, cam_param, mano_param = data[
'img_path'], data['img_shape'], data['bbox'], data[
'joint_cam'], data['cam_param'], data['mano_param']
# img
img = load_img(img_path)
img, img2bb_trans, bb2img_trans, rot, _ = augmentation(
img, bbox, self.data_split, exclude_flip=True
) # FreiHAND dataset only contains right hands. do not perform flip aug.
img = self.transform(img.astype(np.float32)) / 255.
if self.data_split == 'train':
# mano coordinates
mano_mesh_cam, mano_joint_cam, mano_pose, mano_shape = self.get_mano_coord(
mano_param, cam_param)
mano_coord_cam = np.concatenate((mano_mesh_cam, mano_joint_cam))
focal, princpt = cam_param['focal'], cam_param['princpt']
mano_coord_img = cam2pixel(mano_coord_cam, focal, princpt)
# affine transform x,y coordinates. root-relative depth
mano_coord_img_xy1 = np.concatenate(
(mano_coord_img[:, :2], np.ones_like(mano_coord_img[:, :1])),
1)
mano_coord_img[:, :2] = np.dot(img2bb_trans,
mano_coord_img_xy1.transpose(
1, 0)).transpose(1, 0)[:, :2]
root_joint_depth = mano_coord_cam[self.vertex_num +
self.root_joint_idx][2]
mano_coord_img[:, 2] = mano_coord_img[:, 2] - root_joint_depth
mano_coord_img[:, 0] = mano_coord_img[:, 0] / cfg.input_img_shape[
1] * cfg.output_hm_shape[2]
mano_coord_img[:, 1] = mano_coord_img[:, 1] / cfg.input_img_shape[
0] * cfg.output_hm_shape[1]
mano_coord_img[:, 2] = (mano_coord_img[:, 2] /
(cfg.bbox_3d_size / 2) +
1) / 2. * cfg.output_hm_shape[0]
# check truncation
mano_trunc = ((mano_coord_img[:,0] >= 0) * (mano_coord_img[:,0] < cfg.output_hm_shape[2]) * \
(mano_coord_img[:,1] >= 0) * (mano_coord_img[:,1] < cfg.output_hm_shape[1]) * \
(mano_coord_img[:,2] >= 0) * (mano_coord_img[:,2] < cfg.output_hm_shape[0])).reshape(-1,1).astype(np.float32)
# split mesh and joint coordinates
mano_mesh_img = mano_coord_img[:self.vertex_num]
mano_joint_img = mano_coord_img[self.vertex_num:]
mano_mesh_trunc = mano_trunc[:self.vertex_num]
mano_joint_trunc = mano_trunc[self.vertex_num:]
# 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)
# parameter
mano_pose = mano_pose.reshape(-1, 3)
root_pose = mano_pose[self.root_joint_idx, :]
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(rot_aug_mat, root_pose))
mano_pose[self.root_joint_idx] = root_pose.reshape(3)
mano_pose = mano_pose.reshape(-1)
# mano coordinate
mano_joint_cam = mano_joint_cam - mano_joint_cam[
self.root_joint_idx, None] # root-relative
mano_joint_cam = np.dot(rot_aug_mat, mano_joint_cam.transpose(
1, 0)).transpose(1, 0)
orig_joint_img = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
orig_joint_cam = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
orig_joint_valid = np.zeros((self.joint_num, 1),
dtype=np.float32) # dummy
orig_joint_trunc = np.zeros((self.joint_num, 1),
dtype=np.float32) # dummy
inputs = {'img': img}
targets = {
'orig_joint_img': orig_joint_img,
'fit_joint_img': mano_joint_img,
'fit_mesh_img': mano_mesh_img,
'orig_joint_cam': orig_joint_cam,
'fit_joint_cam': mano_joint_cam,
'pose_param': mano_pose,
'shape_param': mano_shape
}
meta_info = {
'orig_joint_valid': orig_joint_valid,
'orig_joint_trunc': orig_joint_trunc,
'fit_joint_trunc': mano_joint_trunc,
'fit_mesh_trunc': mano_mesh_trunc,
'is_valid_fit': float(True),
'is_3D': float(True)
}
else:
inputs = {'img': img}
targets = {}
meta_info = {'bb2img_trans': bb2img_trans}
return inputs, targets, meta_info
def __getitem__(self, idx):
data = copy.deepcopy(self.datalist[idx])
img_path, img_shape, bbox, smpl_param, cam_param = data[
'img_path'], data['img_shape'], data['bbox'], data[
'smpl_param'], data['cam_param']
# img
img = load_img(img_path)
img, img2bb_trans, bb2img_trans, rot, do_flip = augmentation(
img, bbox, self.data_split)
img = self.transform(img.astype(np.float32)) / 255.
# muco gt
muco_joint_img = data['joint_img']
muco_joint_cam = data['joint_cam']
muco_joint_cam = muco_joint_cam - muco_joint_cam[
self.muco_root_joint_idx, None, :] # root-relative
muco_joint_valid = data['joint_valid']
if do_flip:
muco_joint_img[:, 0] = img_shape[1] - 1 - muco_joint_img[:, 0]
muco_joint_cam[:, 0] = -muco_joint_cam[:, 0]
for pair in self.muco_flip_pairs:
muco_joint_img[pair[0], :], muco_joint_img[
pair[1], :] = muco_joint_img[
pair[1], :].copy(), muco_joint_img[pair[0], :].copy()
muco_joint_cam[pair[0], :], muco_joint_cam[
pair[1], :] = muco_joint_cam[
pair[1], :].copy(), muco_joint_cam[pair[0], :].copy()
muco_joint_valid[pair[0], :], muco_joint_valid[
pair[1], :] = muco_joint_valid[pair[1], :].copy(
), muco_joint_valid[pair[0], :].copy()
muco_joint_img_xy1 = np.concatenate(
(muco_joint_img[:, :2], np.ones_like(muco_joint_img[:, :1])), 1)
muco_joint_img[:, :2] = np.dot(img2bb_trans,
muco_joint_img_xy1.transpose(
1, 0)).transpose(1, 0)
muco_joint_img[:, 0] = muco_joint_img[:, 0] / cfg.input_img_shape[
1] * cfg.output_hm_shape[2]
muco_joint_img[:, 1] = muco_joint_img[:, 1] / cfg.input_img_shape[
0] * cfg.output_hm_shape[1]
muco_joint_img[:, 2] = muco_joint_img[:, 2] - muco_joint_img[
self.muco_root_joint_idx][2] # root-relative
muco_joint_img[:, 2] = (
muco_joint_img[:, 2] /
(cfg.bbox_3d_size * 1000 / 2) + 1) / 2. * cfg.output_hm_shape[
0] # change cfg.bbox_3d_size from meter to milimeter
# check truncation
muco_joint_trunc = muco_joint_valid * ((muco_joint_img[:,0] >= 0) * (muco_joint_img[:,0] < cfg.output_hm_shape[2]) * \
(muco_joint_img[:,1] >= 0) * (muco_joint_img[:,1] < cfg.output_hm_shape[1]) * \
(muco_joint_img[:,2] >= 0) * (muco_joint_img[:,2] < cfg.output_hm_shape[0])).reshape(-1,1).astype(np.float32)
# transform muco joints to target db joints
muco_joint_img = transform_joint_to_other_db(muco_joint_img,
self.muco_joints_name,
self.joints_name)
muco_joint_cam = transform_joint_to_other_db(muco_joint_cam,
self.muco_joints_name,
self.joints_name)
muco_joint_valid = transform_joint_to_other_db(muco_joint_valid,
self.muco_joints_name,
self.joints_name)
muco_joint_trunc = transform_joint_to_other_db(muco_joint_trunc,
self.muco_joints_name,
self.joints_name)
if smpl_param is not None:
# smpl coordinates
smpl_mesh_cam, smpl_joint_cam, smpl_pose, smpl_shape = self.get_smpl_coord(
smpl_param, cam_param, do_flip, img_shape)
smpl_coord_cam = np.concatenate((smpl_mesh_cam, smpl_joint_cam))
focal, princpt = cam_param['focal'], cam_param['princpt']
smpl_coord_img = cam2pixel(smpl_coord_cam, focal, princpt)
# affine transform x,y coordinates. root-relative depth
smpl_coord_img_xy1 = np.concatenate(
(smpl_coord_img[:, :2], np.ones_like(smpl_coord_img[:, :1])),
1)
smpl_coord_img[:, :2] = np.dot(img2bb_trans,
smpl_coord_img_xy1.transpose(
1, 0)).transpose(1, 0)[:, :2]
smpl_coord_img[:, 2] = smpl_coord_img[:, 2] - smpl_coord_cam[
self.vertex_num + self.root_joint_idx][2]
smpl_coord_img[:, 0] = smpl_coord_img[:, 0] / cfg.input_img_shape[
1] * cfg.output_hm_shape[2]
smpl_coord_img[:, 1] = smpl_coord_img[:, 1] / cfg.input_img_shape[
0] * cfg.output_hm_shape[1]
smpl_coord_img[:, 2] = (
smpl_coord_img[:, 2] /
(cfg.bbox_3d_size * 1000 / 2) + 1) / 2. * cfg.output_hm_shape[
0] # change cfg.bbox_3d_size from meter to milimeter
# check truncation
smpl_trunc = ((smpl_coord_img[:,0] >= 0) * (smpl_coord_img[:,0] < cfg.output_hm_shape[2]) * \
(smpl_coord_img[:,1] >= 0) * (smpl_coord_img[:,1] < cfg.output_hm_shape[1]) * \
(smpl_coord_img[:,2] >= 0) * (smpl_coord_img[:,2] < cfg.output_hm_shape[0])).reshape(-1,1).astype(np.float32)
# split mesh and joint coordinates
smpl_mesh_img = smpl_coord_img[:self.vertex_num]
smpl_joint_img = smpl_coord_img[self.vertex_num:]
smpl_mesh_trunc = smpl_trunc[:self.vertex_num]
smpl_joint_trunc = smpl_trunc[self.vertex_num:]
# if fitted mesh is too far from muco gt, discard it
is_valid_fit = True
error = self.get_fitting_error(data['joint_cam'], smpl_mesh_cam,
do_flip)
if error > self.fitting_thr:
is_valid_fit = False
else:
smpl_joint_img = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
smpl_joint_cam = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
smpl_mesh_img = np.zeros((self.vertex_num, 3),
dtype=np.float32) # dummy
smpl_pose = np.zeros((72), dtype=np.float32) # dummy
smpl_shape = np.zeros((10), dtype=np.float32) # dummy
smpl_joint_trunc = np.zeros((self.joint_num, 1),
dtype=np.float32) # dummy
smpl_mesh_trunc = np.zeros((self.vertex_num, 1),
dtype=np.float32) # dummy
is_valid_fit = False
# 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)
# muco coordinate
muco_joint_cam = np.dot(rot_aug_mat, muco_joint_cam.transpose(
1, 0)).transpose(1, 0) / 1000 # milimeter to meter
# parameter
smpl_pose = smpl_pose.reshape(-1, 3)
root_pose = smpl_pose[self.root_joint_idx, :]
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(rot_aug_mat, root_pose))
smpl_pose[self.root_joint_idx] = root_pose.reshape(3)
smpl_pose = smpl_pose.reshape(-1)
# smpl coordinate
smpl_joint_cam = smpl_joint_cam - smpl_joint_cam[self.root_joint_idx,
None] # root-relative
smpl_joint_cam = np.dot(rot_aug_mat, smpl_joint_cam.transpose(
1, 0)).transpose(1, 0) / 1000 # milimeter to meter
inputs = {'img': img}
targets = {
'orig_joint_img': muco_joint_img,
'fit_joint_img': smpl_joint_img,
'fit_mesh_img': smpl_mesh_img,
'orig_joint_cam': muco_joint_cam,
'fit_joint_cam': smpl_joint_cam,
'pose_param': smpl_pose,
'shape_param': smpl_shape
}
meta_info = {
'orig_joint_valid': muco_joint_valid,
'orig_joint_trunc': muco_joint_trunc,
'fit_joint_trunc': smpl_joint_trunc,
'fit_mesh_trunc': smpl_mesh_trunc,
'is_valid_fit': float(is_valid_fit),
'is_3D': float(True)
}
return inputs, targets, meta_info
def __getitem__(self, idx):
data = copy.deepcopy(self.datalist[idx])
img_path, img_shape, bbox = data['img_path'], data['img_shape'], data[
'bbox']
# image load and affine transform
img = load_img(img_path)
img, img2bb_trans, bb2img_trans, rot, do_flip = augmentation(
img, bbox, self.data_split)
img = self.transform(img.astype(np.float32)) / 255.
if self.data_split == 'train':
# coco gt
coco_joint_img = data['joint_img']
coco_joint_valid = data['joint_valid']
if do_flip:
coco_joint_img[:, 0] = img_shape[1] - 1 - coco_joint_img[:, 0]
for pair in self.coco_flip_pairs:
coco_joint_img[pair[0], :], coco_joint_img[
pair[1], :] = coco_joint_img[pair[1], :].copy(
), coco_joint_img[pair[0], :].copy()
coco_joint_valid[pair[0], :], coco_joint_valid[
pair[1], :] = coco_joint_valid[pair[1], :].copy(
), coco_joint_valid[pair[0], :].copy()
coco_joint_img_xy1 = np.concatenate(
(coco_joint_img[:, :2], np.ones_like(coco_joint_img[:, :1])),
1)
coco_joint_img[:, :2] = np.dot(img2bb_trans,
coco_joint_img_xy1.transpose(
1, 0)).transpose(1, 0)
coco_joint_img[:, 0] = coco_joint_img[:, 0] / cfg.input_img_shape[
1] * cfg.output_hm_shape[2]
coco_joint_img[:, 1] = coco_joint_img[:, 1] / cfg.input_img_shape[
0] * cfg.output_hm_shape[1]
# backup for calculating fitting error
_coco_joint_img = coco_joint_img.copy()
_coco_joint_valid = coco_joint_valid.copy()
# check truncation
coco_joint_trunc = coco_joint_valid * ((coco_joint_img[:,0] >= 0) * (coco_joint_img[:,0] < cfg.output_hm_shape[2]) * \
(coco_joint_img[:,1] >= 0) * (coco_joint_img[:,1] < cfg.output_hm_shape[1])).reshape(-1,1).astype(np.float32)
# transform coco joints to target db joints
coco_joint_img = transform_joint_to_other_db(
coco_joint_img, self.coco_joints_name, self.joints_name)
coco_joint_cam = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
coco_joint_valid = transform_joint_to_other_db(
coco_joint_valid, self.coco_joints_name, self.joints_name)
coco_joint_trunc = transform_joint_to_other_db(
coco_joint_trunc, self.coco_joints_name, self.joints_name)
smplify_result = data['smplify_result']
if smplify_result is not None:
# use fitted mesh
smpl_param, cam_param = smplify_result[
'smpl_param'], smplify_result['cam_param']
smpl_mesh_cam, smpl_joint_cam, smpl_pose, smpl_shape = self.get_smpl_coord(
smpl_param, cam_param, do_flip, img_shape)
smpl_coord_cam = np.concatenate(
(smpl_mesh_cam, smpl_joint_cam))
smpl_coord_img = cam2pixel(smpl_coord_cam, cam_param['focal'],
cam_param['princpt'])
# x,y affine transform, root-relative depth
smpl_coord_img_xy1 = np.concatenate(
(smpl_coord_img[:, :2], np.ones_like(
smpl_coord_img[:, 0:1])), 1)
smpl_coord_img[:, :2] = np.dot(
img2bb_trans,
smpl_coord_img_xy1.transpose(1, 0)).transpose(1, 0)[:, :2]
smpl_coord_img[:, 2] = smpl_coord_img[:, 2] - smpl_coord_cam[
self.vertex_num + self.root_joint_idx][2]
smpl_coord_img[:,
0] = smpl_coord_img[:, 0] / cfg.input_img_shape[
1] * cfg.output_hm_shape[2]
smpl_coord_img[:,
1] = smpl_coord_img[:, 1] / cfg.input_img_shape[
0] * cfg.output_hm_shape[1]
smpl_coord_img[:, 2] = (smpl_coord_img[:, 2] /
(cfg.bbox_3d_size / 2) +
1) / 2. * cfg.output_hm_shape[0]
# check truncation
smpl_trunc = ((smpl_coord_img[:,0] >= 0) * (smpl_coord_img[:,0] < cfg.output_hm_shape[2]) * \
(smpl_coord_img[:,1] >= 0) * (smpl_coord_img[:,1] < cfg.output_hm_shape[1]) * \
(smpl_coord_img[:,2] >= 0) * (smpl_coord_img[:,2] < cfg.output_hm_shape[0])).reshape(-1,1).astype(np.float32)
# split mesh and joint coordinates
smpl_mesh_img = smpl_coord_img[:self.vertex_num]
smpl_joint_img = smpl_coord_img[self.vertex_num:]
smpl_mesh_trunc = smpl_trunc[:self.vertex_num]
smpl_joint_trunc = smpl_trunc[self.vertex_num:]
# if fitted mesh is too far from h36m gt, discard it
is_valid_fit = True
error = self.get_fitting_error(_coco_joint_img, smpl_mesh_cam,
cam_param, img2bb_trans,
_coco_joint_valid)
if error > self.fitting_thr:
is_valid_fit = False
else:
smpl_joint_img = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
smpl_joint_cam = np.zeros((self.joint_num, 3),
dtype=np.float32) # dummy
smpl_mesh_img = np.zeros((self.vertex_num, 3),
dtype=np.float32) # dummy
smpl_pose = np.zeros((72), dtype=np.float32) # dummy
smpl_shape = np.zeros((10), dtype=np.float32) # dummy
smpl_joint_trunc = np.zeros((self.joint_num, 1),
dtype=np.float32)
smpl_mesh_trunc = np.zeros((self.vertex_num, 1),
dtype=np.float32)
is_valid_fit = False
# 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)
# parameter
smpl_pose = smpl_pose.reshape(-1, 3)
root_pose = smpl_pose[self.root_joint_idx, :]
root_pose, _ = cv2.Rodrigues(root_pose)
root_pose, _ = cv2.Rodrigues(np.dot(rot_aug_mat, root_pose))
smpl_pose[self.root_joint_idx] = root_pose.reshape(3)
smpl_pose = smpl_pose.reshape(-1)
# smpl coordinate
smpl_joint_cam = smpl_joint_cam - smpl_joint_cam[
self.root_joint_idx, None] # root-relative
smpl_joint_cam = np.dot(rot_aug_mat, smpl_joint_cam.transpose(
1, 0)).transpose(1, 0)
inputs = {'img': img}
targets = {
'orig_joint_img': coco_joint_img,
'fit_joint_img': smpl_joint_img,
'fit_mesh_img': smpl_mesh_img,
'orig_joint_cam': coco_joint_cam,
'fit_joint_cam': smpl_joint_cam,
'pose_param': smpl_pose,
'shape_param': smpl_shape
}
meta_info = {
'orig_joint_valid': coco_joint_valid,
'orig_joint_trunc': coco_joint_trunc,
'fit_joint_trunc': smpl_joint_trunc,
'fit_mesh_trunc': smpl_mesh_trunc,
'is_valid_fit': float(is_valid_fit),
'is_3D': float(False)
}
return inputs, targets, meta_info
else:
inputs = {'img': img}
targets = {}
meta_info = {'bb2img_trans': bb2img_trans}
return inputs, targets, meta_info
def run_webcam():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
if cfg.dataset == 'InterHand2.6M':
assert args.test_set, 'Test set is required. Select one of test/val'
assert args.annot_subset, "Please set proper annotation subset. Select one of all, human_annot, machine_annot"
else:
args.test_set = 'test'
args.annot_subset = 'all'
tester = Tester(args.test_epoch)
tester._make_batch_generator(args.test_set, args.annot_subset)
tester._make_model()
detector = YOLO_AKASH("best.pt", save_img=True)
test_loader = CustomLoader(transforms.ToTensor(), 'my_hand', 1)
cap = cv2.VideoCapture(0)
with torch.no_grad():
while (True):
ret, frame = cap.read()
inputs = {}
preds = {
'joint_coord': [],
'rel_root_depth': [],
'hand_type': []
}
img = test_loader.process_frame(frame)
inputs['img'] = torch.reshape(img, (1, 3, 256, 256))
boxes, confs, labels = detector.detect_image(img)
img, inv_trans = augmentation(img, np.array(bbox), None, None,
None, 'test', None)
# forward
out = tester.model(inputs)
joint_coord_out = out['joint_coord'].cpu().numpy()
rel_root_depth_out = out['rel_root_depth'].cpu().numpy()
hand_type_out = out['hand_type'].cpu().numpy()
preds['joint_coord'].append(joint_coord_out)
preds['rel_root_depth'].append(rel_root_depth_out)
preds['hand_type'].append(hand_type_out)
# evaluate
preds = {k: np.concatenate(v) for k, v in preds.items()}
##Changes need to be done from here onwards !!!!!!!!!!!!!!!!!!!!!!!!
keypoint_frame = test_loader.visualize(preds, filename,
img_path, inv_trans)
cv2.imshow('webcam', keypoint_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()