expre-gen/demo.py

#-*- coding : utf-8-*-

import numpy as np
import argparse
from scipy.signal import savgol_filter
import os
import torch
from a2fmodel import A2BModel
import random
import os, subprocess
import shlex
import librosa

@torch.no_grad()
def test(args):
    result_path = args.result_path
    os.makedirs(result_path, exist_ok=True)
    eye1 = np.array( [ 0.36537236,0.950235724,  0.95593375,0.916715622, 0.367256105,  0.119113259,0.025357503])
    eye2 = np.array( [0.234776169, 0.909951985,  0.944758058, 0.777862132,0.191071674, 0.235437036,  0.089163929])
    eye3 = np.array([0.870040774,  0.949833691, 0.949418545,0.695911646, 0.191071674,  0.072576277, 0.007108896])
    eye4 = np.array( [0.000307991, 0.556701422,  0.952656746,  0.942345619, 0.425857186,  0.148335218,  0.017659493])
    model = A2BModel(args)
    
    # model.load_state_dict(torch.load(args.model_path, map_location=torch.device(args.device)), strict=False)
    state = torch.load(args.model_path, map_location=torch.device(args.device))
    # 建议统一跳过情感分类头（dense 和 out_proj 都跳）
    drop_prefixes = [
        "audio_encoder_emo.classifier.out_proj.",
        "audio_encoder_emo.classifier.dense.",
    ]
    filtered_state = {k: v for k, v in state.items() if not any(k.startswith(p) for p in drop_prefixes)}
    missing, unexpected = model.load_state_dict(filtered_state, strict=False)
    print("[load_state_dict] missing:", missing)
    print("[load_state_dict] unexpected:", unexpected)

    model = model.to(args.device)
    model.eval()
    wav_path = args.wav_path
    file_name = wav_path.split('/')[-1].split('.')[0]
    speech_array, sampling_rate = librosa.load(os.path.join(wav_path), sr=16000)
    audio = torch.FloatTensor(speech_array).unsqueeze(0).to(args.device)
    level = torch.tensor([1]).to(args.device)
    person = torch.tensor([0]).to(args.device)
    prediction = model.predict(audio, level, person)
    prediction = prediction.squeeze().detach().cpu().numpy()
    if args.post_processing:
        output = np.zeros((prediction.shape[0], prediction.shape[1]))
        for i in range(prediction.shape[1]):
            output[:, i] = savgol_filter(prediction[:, i], 5, 2)
        output[:, 8] = 0
        output[:, 9] = 0
        i = random.randint(0, 60)
        while i < output.shape[0] - 7:
            eye_num = random.randint(1, 4)
            if eye_num == 1:
                output[i:i + 7, 8] = eye1
                output[i:i + 7, 9] = eye1
            elif eye_num == 2:
                output[i:i + 7, 8] = eye2
                output[i:i + 7, 9] = eye2
            elif eye_num == 3:
                output[i:i + 7, 8] = eye3
                output[i:i + 7, 9] = eye3
            else:
                output[i:i + 7, 8] = eye4
                output[i:i + 7, 9] = eye4
            time1 = random.randint(60, 180)
            i = i + time1
        np.save(os.path.join(result_path, "{}.npy".format(file_name)), output)  # with postprocessing (smoothing and blinking)
    else:
        np.save(os.path.join(result_path, "{}.npy".format(file_name)), prediction)  # without post-processing


def render_video(args):
    wav_name = args.wav_path.split('/')[-1].split('.')[0]
    image_path = os.path.join(args.result_path, wav_name)
    os.makedirs(image_path, exist_ok=True)
    image_temp = image_path + "/%d.png"
    output_path = os.path.join(args.result_path, wav_name + ".mp4")
    blender_path = args.blender_path
    python_path = "./render.py"
    blend_path = "./render.blend"
    cmd = '{} -t 64 -b {} -P {} -- "{}" "{}" '.format(blender_path, blend_path, python_path, args.result_path, wav_name)
    cmd = shlex.split(cmd)
    p = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
    while p.poll() is None:
        line = p.stdout.readline()
        line = line.strip()
        if line:
            print('[{}]'.format(line))
    if p.returncode == 0:
        print('Subprogram success')
    else:
        print('Subprogram failed')

    cmd = 'ffmpeg -r 30 -i "{}" -i "{}" -pix_fmt yuv420p -s 512x768 "{}" -y'.format(image_temp, args.wav_path, output_path)
    subprocess.call(cmd, shell=True)

    cmd = 'rm -rf "{}"'.format(image_path)
    subprocess.call(cmd, shell=True)

def main():
    parser = argparse.ArgumentParser(description='Morpheus: A Neural-driven Animatronic Face with Hybrid Actuation and Diverse Emotion Control')
    parser.add_argument("--wav_path", type=str, default="./audio/angry1.wav", help='path of the test data')
    parser.add_argument("--bs_dim", type=int, default=52, help='number of blendshapes:52')
    parser.add_argument("--feature_dim", type=int, default=832, help='64 for vocaset; 128 for BIWI')
    parser.add_argument("--period", type=int, default=30, help='period in PPE - 30 for vocaset; 25 for BIWI')
    parser.add_argument("--model_path", type=str, default="./pretrain_model/model.pth",
                        help='path of the trained models')
    parser.add_argument("--device", type=str, default="cuda:0", help='cuda:0 or cuda:1')
    parser.add_argument("--result_path", type=str, default="./result/", help='path of the result')
    parser.add_argument("--emo_guide", type=bool, default=True, help='emotion guided')
    parser.add_argument("--max_seq_len", type=int, default=5000, help='max_seq_len')
    parser.add_argument("--num_workers", type=int, default=0)
    parser.add_argument("--batch_size", type=int, default=1)
    parser.add_argument("--post_processing", type=bool, default=True, help='whether to use post processing')
    parser.add_argument("--blender_path", type=str, default="./blender/blender", help='path of blender')

    args = parser.parse_args()
    test(args)
    render_video(args)

if __name__ == "__main__":
    main()