def regress(self, img_original, body_bbox_list):
"""
args:
img_original: original raw image (BGR order by using cv2.imread)
body_bbox: bounding box around the target: (minX, minY, width, height)
outputs:
pred_vertices_img:
pred_joints_vis_img:
pred_rotmat
pred_betas
pred_camera
bbox: [bbr[0], bbr[1],bbr[0]+bbr[2], bbr[1]+bbr[3]])
bboxTopLeft: bbox top left (redundant)
boxScale_o2n: bbox scaling factor (redundant)
"""
pred_output_list = list()
for body_bbox in body_bbox_list:
img, norm_img, boxScale_o2n, bboxTopLeft, bbox = process_image_bbox(
img_original, body_bbox, input_res=constants.IMG_RES)
# bboxTopLeft = bbox['bboxXYWH'][:2]
'''
print("bboxTopLeft", bboxTopLeft)
print('img', img.shape)
cv2.imwrite("img.png", img)
print("here !!!!", )
sys.exit(0)
'''
if img is None:
pred_output_list.append(None)
continue
with torch.no_grad():
# model forward
pred_rotmat, pred_betas, pred_camera = self.model_regressor(
norm_img.to(self.device))
#Convert rot_mat to aa since hands are always in aa
pred_aa = rotmat3x3_to_angleaxis(pred_rotmat)
pred_aa = pred_aa.view(pred_aa.shape[0], -1)
smpl_output = self.smpl(betas=pred_betas,
body_pose=pred_aa[:, 3:],
global_orient=pred_aa[:, :3],
pose2rot=True)
pred_vertices = smpl_output.vertices
pred_joints_3d = smpl_output.joints
pred_vertices = pred_vertices[0].cpu().numpy()
pred_camera = pred_camera.cpu().numpy().ravel()
camScale = pred_camera[0] # *1.15
camTrans = pred_camera[1:]
pred_output = dict()
# Convert mesh to original image space (X,Y are aligned to image)
# 1. SMPL -> 2D bbox
# 2. 2D bbox -> original 2D image
pred_vertices_bbox = convert_smpl_to_bbox(
pred_vertices, camScale, camTrans)
pred_vertices_img = convert_bbox_to_oriIm(
pred_vertices_bbox, boxScale_o2n, bboxTopLeft,
img_original.shape[1], img_original.shape[0])
# Convert joint to original image space (X,Y are aligned to image)
pred_joints_3d = pred_joints_3d[0].cpu().numpy() # (1,49,3)
pred_joints_vis = pred_joints_3d[:, :3] # (49,3)
pred_joints_vis_bbox = convert_smpl_to_bbox(
pred_joints_vis, camScale, camTrans)
pred_joints_vis_img = convert_bbox_to_oriIm(
pred_joints_vis_bbox, boxScale_o2n, bboxTopLeft,
img_original.shape[1], img_original.shape[0])
# Output
pred_output['img_cropped'] = img[:, :, ::-1]
pred_output['pred_vertices_smpl'] = smpl_output.vertices[
0].cpu().numpy() # SMPL vertex in original smpl space
pred_output[
'pred_vertices_img'] = pred_vertices_img # SMPL vertex in image space
pred_output[
'pred_joints_img'] = pred_joints_vis_img # SMPL joints in image space
pred_rotmat_tensor = torch.zeros((1, 24, 3, 4),
dtype=torch.float32)
pred_rotmat_tensor[:, :, :, :3] = pred_rotmat.detach().cpu()
pred_aa_tensor = gu.rotation_matrix_to_angle_axis(
pred_rotmat_tensor.squeeze())
pred_output['pred_body_pose'] = pred_aa_tensor.cpu().numpy(
).reshape(1, 72)
pred_output['pred_rotmat'] = pred_rotmat.detach().cpu().numpy(
) # (1, 24, 3, 3)
pred_output['pred_betas'] = pred_betas.detach().cpu().numpy(
) # (1, 10)
pred_output['pred_camera'] = pred_camera
pred_output['bbox_top_left'] = bboxTopLeft
pred_output['bbox_scale_ratio'] = boxScale_o2n
pred_output['faces'] = self.smpl.faces
if self.use_smplx:
img_center = np.array(
(img_original.shape[1], img_original.shape[0])) * 0.5
# right hand
pred_joints = smpl_output.right_hand_joints[0].cpu().numpy(
)
pred_joints_bbox = convert_smpl_to_bbox(
pred_joints, camScale, camTrans)
pred_joints_img = convert_bbox_to_oriIm(
pred_joints_bbox, boxScale_o2n, bboxTopLeft,
img_original.shape[1], img_original.shape[0])
pred_output[
'right_hand_joints_img_coord'] = pred_joints_img
# left hand
pred_joints = smpl_output.left_hand_joints[0].cpu().numpy()
pred_joints_bbox = convert_smpl_to_bbox(
pred_joints, camScale, camTrans)
pred_joints_img = convert_bbox_to_oriIm(
pred_joints_bbox, boxScale_o2n, bboxTopLeft,
img_original.shape[1], img_original.shape[0])
pred_output['left_hand_joints_img_coord'] = pred_joints_img
pred_output_list.append(pred_output)
return pred_output_list
def intergration_copy_paste(pred_body_list, pred_hand_list, smplx_model,
image_shape):
integral_output_list = list()
for i in range(len(pred_body_list)):
body_info = pred_body_list[i]
hand_info = pred_hand_list[i]
if body_info is None or hand_info is None:
integral_output_list.append(None)
continue
# copy and paste
pred_betas = torch.from_numpy(body_info['pred_betas']).cuda()
pred_rotmat = torch.from_numpy(body_info['pred_rotmat']).cuda()
if hand_info['right_hand'] is not None:
right_hand_pose = torch.from_numpy(
hand_info['right_hand']['pred_hand_pose'][:, 3:]).cuda()
right_hand_global_orient = torch.from_numpy(
hand_info['right_hand']['pred_hand_pose'][:, :3]).cuda()
right_hand_local_orient = transfer_hand_wrist(
smplx_model, pred_rotmat[0], right_hand_global_orient,
'right_hand', 'l2g')
pred_rotmat[0, 21] = right_hand_local_orient
else:
right_hand_pose = torch.from_numpy(
np.zeros((1, 45), dtype=np.float32)).cuda()
if hand_info['left_hand'] is not None:
left_hand_pose = torch.from_numpy(
hand_info['left_hand']['pred_hand_pose'][:, 3:]).cuda()
left_hand_global_orient = torch.from_numpy(
hand_info['left_hand']['pred_hand_pose'][:, :3]).cuda()
left_hand_local_orient = transfer_hand_wrist(
smplx_model, pred_rotmat[0], left_hand_global_orient,
'left_hand', 'l2g')
pred_rotmat[0, 20] = left_hand_local_orient
else:
left_hand_pose = torch.from_numpy(
np.zeros((1, 45), dtype=np.float32)).cuda()
# smplx_output = smplx_model(
# betas = pred_betas,
# body_pose = pred_rotmat[:,1:],
# global_orient = pred_rotmat[:,0].unsqueeze(1),
# right_hand_pose = right_hand_pose,
# left_hand_pose= left_hand_pose,
# pose2rot = False)
#Convert rot_mat to aa since hands are always in aa
pred_aa = rotmat3x3_to_angleaxis(pred_rotmat)
pred_aa = pred_aa.view(pred_aa.shape[0], -1)
smplx_output = smplx_model(betas=pred_betas,
body_pose=pred_aa[:, 3:],
global_orient=pred_aa[:, :3],
right_hand_pose=right_hand_pose,
left_hand_pose=left_hand_pose,
pose2rot=True)
pred_vertices = smplx_output.vertices
pred_vertices = pred_vertices[0].detach().cpu().numpy()
pred_joints_3d = smplx_output.joints
pred_joints_3d = pred_joints_3d[0].detach().cpu().numpy()
camScale = body_info['pred_camera'][0]
camTrans = body_info['pred_camera'][1:]
bbox_top_left = body_info['bbox_top_left']
bbox_scale_ratio = body_info['bbox_scale_ratio']
integral_output = dict()
integral_output['pred_vertices_smpl'] = pred_vertices
integral_output['faces'] = smplx_model.faces
integral_output['bbox_scale_ratio'] = bbox_scale_ratio
integral_output['bbox_top_left'] = bbox_top_left
integral_output['pred_camera'] = body_info['pred_camera']
pred_rotmat_tensor = torch.zeros((1, 24, 3, 4), dtype=torch.float32)
pred_rotmat_tensor[:, :, :, :3] = pred_rotmat.detach().cpu()
pred_aa_tensor = gu.rotation_matrix_to_angle_axis(
pred_rotmat_tensor.squeeze())
integral_output['pred_body_pose'] = pred_aa_tensor.cpu().numpy(
).reshape(1, 72)
integral_output['pred_betas'] = pred_betas.detach().cpu().numpy()
integral_output['pred_left_hand_pose'] = left_hand_pose.detach().cpu(
).numpy()
integral_output['pred_right_hand_pose'] = right_hand_pose.detach().cpu(
).numpy()
# convert mesh to original image space (X,Y are aligned to image)
pred_vertices_bbox = convert_smpl_to_bbox(pred_vertices, camScale,
camTrans)
pred_vertices_img = convert_bbox_to_oriIm(pred_vertices_bbox,
bbox_scale_ratio,
bbox_top_left,
image_shape[1],
image_shape[0])
integral_output['pred_vertices_img'] = pred_vertices_img
integral_output_list.append(integral_output)
return integral_output_list