sadtalker_audio2pose/src/utils/preprocess_fromvideo.py

import numpy as np
import cv2, os, sys, torch
from tqdm import tqdm
from PIL import Image

# 3dmm extraction
import safetensors
import safetensors.torch
from src.face3d.util.preprocess import align_img
from src.face3d.util.load_mats import load_lm3d
from src.face3d.models import networks

from scipy.io import loadmat, savemat
from src.utils.croper import Preprocesser


import warnings

from src.utils.safetensor_helper import load_x_from_safetensor
warnings.filterwarnings("ignore")


def smooth_3dmm_params(params, window_size=5):
    # 创建一个新的数组来存储平滑后的参数
    smoothed_params = np.zeros_like(params)

    # 对每个参数进行平滑处理
    for i in range(params.shape[1]):
        
        # 在参数周围创建一个滑动窗口
        window = np.ones(int(window_size))/float(window_size)
        smoothed_param = np.convolve(params[:, i], window, 'same')

        # 将平滑后的参数存储在新数组中
        smoothed_params[:, i] = smoothed_param
    
    return smoothed_params



def split_coeff(coeffs):
        """
        Return:
            coeffs_dict     -- a dict of torch.tensors

        Parameters:
            coeffs          -- torch.tensor, size (B, 256)
        """
        id_coeffs = coeffs[:, :80]
        exp_coeffs = coeffs[:, 80: 144]
        tex_coeffs = coeffs[:, 144: 224]
        angles = coeffs[:, 224: 227]
        gammas = coeffs[:, 227: 254]
        translations = coeffs[:, 254:]
        return {
            'id': id_coeffs,
            'exp': exp_coeffs,
            'tex': tex_coeffs,
            'angle': angles,
            'gamma': gammas,
            'trans': translations
        }


class CropAndExtract():
    def __init__(self, sadtalker_path, device):

        self.propress = Preprocesser(device)
        self.net_recon = networks.define_net_recon(net_recon='resnet50', use_last_fc=False, init_path='').to(device)

        if sadtalker_path['use_safetensor']:
            checkpoint = safetensors.torch.load_file(sadtalker_path['checkpoint'])
            self.net_recon.load_state_dict(load_x_from_safetensor(checkpoint, 'face_3drecon'))
        else:
            checkpoint = torch.load(sadtalker_path['path_of_net_recon_model'], map_location=torch.device(device))
            self.net_recon.load_state_dict(checkpoint['net_recon'])

        self.net_recon.eval()
        self.lm3d_std = load_lm3d(sadtalker_path['dir_of_BFM_fitting'])
        self.device = device

    def generate(self, input_path, save_dir, crop_or_resize='crop', source_image_flag=False, pic_size=256, if_smooth=False):

        pic_name = os.path.splitext(os.path.split(input_path)[-1])[0]

        landmarks_path =  os.path.join(save_dir, pic_name+'_landmarks.txt')
        coeff_path =  os.path.join(save_dir, pic_name+'.mat')
        png_path =  os.path.join(save_dir, pic_name+'.png')

        #load input
        if not os.path.isfile(input_path):
            raise ValueError('input_path must be a valid path to video/image file')
        elif input_path.split('.')[-1] in ['jpg', 'png', 'jpeg']:
            # loader for first frame
            full_frames = [cv2.imread(input_path)]
            fps = 25
        else:
            # loader for videos
            video_stream = cv2.VideoCapture(input_path)
            fps = video_stream.get(cv2.CAP_PROP_FPS)
            full_frames = []
            while 1:
                still_reading, frame = video_stream.read()
                if not still_reading:
                    video_stream.release()
                    break
                full_frames.append(frame)
                if source_image_flag:
                    break

        x_full_frames= [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  for frame in full_frames]

        # print(x_full_frames)

        #### crop images as the
        if 'crop' in crop_or_resize.lower(): # default crop
            x_full_frames, crop, quad = self.propress.crop(x_full_frames, still=True if 'ext' in crop_or_resize.lower() else False, xsize=512)
            clx, cly, crx, cry = crop
            lx, ly, rx, ry = quad
            lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
            crop_info = ((ox2 - ox1, oy2 - oy1), crop, quad)
        elif 'full' in crop_or_resize.lower():
            x_full_frames, crop, quad = self.propress.crop(x_full_frames, still=True if 'ext' in crop_or_resize.lower() else False, xsize=512)
            clx, cly, crx, cry = crop
            lx, ly, rx, ry = quad
            lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
            crop_info = ((ox2 - ox1, oy2 - oy1), crop, quad)
        else: # resize mode
            oy1, oy2, ox1, ox2 = 0, x_full_frames[0].shape[0], 0, x_full_frames[0].shape[1]
            crop_info = ((ox2 - ox1, oy2 - oy1), None, None)

        frames_pil = [Image.fromarray(cv2.resize(frame,(pic_size, pic_size))) for frame in x_full_frames]
        if len(frames_pil) == 0:
            print('No face is detected in the input file')
            return None, None

        # save crop info
        for frame in frames_pil:
            cv2.imwrite(png_path, cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))

        # 2. get the landmark according to the detected face.
        if not os.path.isfile(landmarks_path):
            lm = self.propress.predictor.extract_keypoint(frames_pil, landmarks_path)
        else:
            print(' Using saved landmarks.')
            lm = np.loadtxt(landmarks_path).astype(np.float32)
            lm = lm.reshape([len(x_full_frames), -1, 2])

        if not os.path.isfile(coeff_path):
            # load 3dmm paramter generator from Deep3DFaceRecon_pytorch
            video_coeffs, full_coeffs = [],  []
            for idx in tqdm(range(len(frames_pil)), desc='3DMM Extraction In Video:'):
                frame = frames_pil[idx]
                W,H = frame.size
                lm1 = lm[idx].reshape([-1, 2])

                if np.mean(lm1) == -1:
                    lm1 = (self.lm3d_std[:, :2]+1)/2.
                    lm1 = np.concatenate(
                        [lm1[:, :1]*W, lm1[:, 1:2]*H], 1
                    )
                else:
                    lm1[:, -1] = H - 1 - lm1[:, -1]

                trans_params, im1, lm1, _ = align_img(frame, lm1, self.lm3d_std)

                trans_params_m = np.array([float(item) for item in np.hsplit(trans_params, len(trans_params))]).astype(np.float32)
                im_t = torch.tensor(np.array(im1)/255., dtype=torch.float32).permute(2, 0, 1).to(self.device).unsqueeze(0)

                with torch.no_grad():
                    full_coeff = self.net_recon(im_t)
                    coeffs = split_coeff(full_coeff)

                pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}

                pred_coeff = np.concatenate([
                    pred_coeff['exp'],
                    pred_coeff['angle'],
                    pred_coeff['trans'],
                    # trans_params_m[2:][None],
                    ], 1)
                video_coeffs.append(pred_coeff)
                full_coeffs.append(full_coeff.cpu().numpy())

            semantic_npy = np.array(video_coeffs)[:,0]

            if if_smooth:
                # pass
                semantic_npy[:, -6:] = smooth_3dmm_params(semantic_npy[:, -6:], window_size=10)

            savemat(coeff_path, {'coeff_3dmm': semantic_npy, 'full_3dmm': np.array(full_coeffs)[0], 'trans_params': trans_params})

        return coeff_path, png_path, crop_info