tddfa/TDDFA.py

# coding: utf-8

__author__ = 'cleardusk'

import os.path as osp
import time
import numpy as np
import cv2
import torch
from torchvision.transforms import Compose
import torch.backends.cudnn as cudnn

import tddfa.models as models
from tddfa.bfm import BFMModel
from tddfa.utils.io import _load
from tddfa.utils.functions import (
    crop_img, parse_roi_box_from_bbox, parse_roi_box_from_landmark,
)
from tddfa.utils.tddfa_util import (
    load_model, _parse_param, similar_transform,
    ToTensorGjz, NormalizeGjz
)

make_abs_path = lambda fn: osp.join(osp.dirname(osp.realpath(__file__)), fn)


class TDDFA(object):
    """TDDFA: named Three-D Dense Face Alignment (TDDFA)"""

    def __init__(self, **kvs):
        torch.set_grad_enabled(False)

        # load BFM
        self.bfm = BFMModel(
            bfm_fp=kvs.get('bfm_fp', make_abs_path('configs/bfm_noneck_v3.pkl')),
            shape_dim=kvs.get('shape_dim', 40),
            exp_dim=kvs.get('exp_dim', 10)
        )
        self.tri = self.bfm.tri

        # config
        self.gpu_mode = kvs.get('gpu_mode', False)
        self.gpu_id = kvs.get('gpu_id', 0)
        self.size = kvs.get('size', 120)

        param_mean_std_fp = kvs.get(
            'param_mean_std_fp', make_abs_path(f'configs/param_mean_std_62d_{self.size}x{self.size}.pkl')
        )

        # load model, default output is dimension with length 62 = 12(pose) + 40(shape) +10(expression)
        model = getattr(models, kvs.get('arch'))(
            num_classes=kvs.get('num_params', 62),
            widen_factor=kvs.get('widen_factor', 1),
            size=self.size,
            mode=kvs.get('mode', 'small')
        )
        model = load_model(model, kvs.get('checkpoint_fp'))

        if self.gpu_mode:
            cudnn.benchmark = True
            model = model.cuda(device=self.gpu_id)

        self.model = model
        self.model.eval()  # eval mode, fix BN

        # data normalization
        transform_normalize = NormalizeGjz(mean=127.5, std=128)
        transform_to_tensor = ToTensorGjz()
        transform = Compose([transform_to_tensor, transform_normalize])
        self.transform = transform

        # params normalization config
        r = _load(param_mean_std_fp)
        self.param_mean = r.get('mean')
        self.param_std = r.get('std')

        # print('param_mean and param_srd', self.param_mean, self.param_std)

    def __call__(self, img_ori, objs, **kvs):
        """The main call of TDDFA, given image and box / landmark, return 3DMM params and roi_box
        :param img_ori: the input image
        :param objs: the list of box or landmarks
        :param kvs: options
        :return: param list and roi_box list
        """
        # Crop image, forward to get the param
        param_lst = []
        roi_box_lst = []

        crop_policy = kvs.get('crop_policy', 'box')
        for obj in objs:
            if crop_policy == 'box':
                # by face box
                roi_box = parse_roi_box_from_bbox(obj)
            elif crop_policy == 'landmark':
                # by landmarks
                roi_box = parse_roi_box_from_landmark(obj)
            else:
                raise ValueError(f'Unknown crop policy {crop_policy}')

            roi_box_lst.append(roi_box)
            img = crop_img(img_ori, roi_box)
            img = cv2.resize(img, dsize=(self.size, self.size), interpolation=cv2.INTER_LINEAR)
            inp = self.transform(img).unsqueeze(0)

            if self.gpu_mode:
                inp = inp.cuda(device=self.gpu_id)

            if kvs.get('timer_flag', False):
                end = time.time()
                param = self.model(inp)
                elapse = f'Inference: {(time.time() - end) * 1000:.1f}ms'
                print(elapse)
            else:
                param = self.model(inp)

            param = param.squeeze().cpu().detach().numpy().flatten().astype(np.float32)
            param = param * self.param_std + self.param_mean  # re-scale
            # print('output', param)
            param_lst.append(param)

        return param_lst, roi_box_lst

    def recon_vers(self, param_lst, roi_box_lst, **kvs):
        dense_flag = kvs.get('dense_flag', False)
        size = self.size

        ver_lst = []
        for param, roi_box in zip(param_lst, roi_box_lst):
            if dense_flag:
                R, offset, alpha_shp, alpha_exp = _parse_param(param)
                pts3d = R @ (self.bfm.u + self.bfm.w_shp @ alpha_shp + self.bfm.w_exp @ alpha_exp). \
                    reshape(3, -1, order='F') + offset
                pts3d = similar_transform(pts3d, roi_box, size)
            else:
                R, offset, alpha_shp, alpha_exp = _parse_param(param)
                pts3d = R @ (self.bfm.u_base + self.bfm.w_shp_base @ alpha_shp + self.bfm.w_exp_base @ alpha_exp). \
                    reshape(3, -1, order='F') + offset
                pts3d = similar_transform(pts3d, roi_box, size)

            ver_lst.append(pts3d)

        return ver_lst