from os import listdir, path
import numpy as np
import cv2
import os
import sys
import argparse
import json
import subprocess
import random
import string
from tqdm import tqdm
from glob import glob
import torch
import face_detection
from models import Wav2Lip
import platform
import audio

def ensure_no_alpha_channel(image):
    if image.shape[2] == 4:
        return cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
    return image

parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')

parser.add_argument('--checkpoint_path', type=str, help='Name of saved checkpoint to load weights from', required=True)
parser.add_argument('--face', type=str, help='Filepath of video/image that contains faces to use', required=True)
parser.add_argument('--audio', type=str, help='Filepath of video/audio file to use as raw audio source', required=True)
parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', default='results/result_voice.mp4')
parser.add_argument('--static', type=bool, help='If True, then use only first video frame for inference', default=False)
parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', default=25., required=False)
parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], help='Padding (top, bottom, left, right). Please adjust to include chin at least')
parser.add_argument('--face_det_batch_size', type=int, help='Batch size for face detection', default=16)
parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128)
parser.add_argument('--resize_factor', default=1, type=int, help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')
parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1], help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')
parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected. Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).')
parser.add_argument('--rotate', default=False, action='store_true', help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg. Use if you get a flipped result, despite feeding a normal looking video')
parser.add_argument('--nosmooth', default=False, action='store_true', help='Prevent smoothing face detections over a short temporal window')

args = parser.parse_args()
args.img_size = 96

if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
    args.static = True

def get_smoothened_boxes(boxes, T):
    for i in range(len(boxes)):
        if i + T > len(boxes):
            window = boxes[len(boxes) - T:]
        else:
            window = boxes[i : i + T]
        boxes[i] = np.mean(window, axis=0)
    return boxes

def face_detect(images):
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=device)
    batch_size = args.face_det_batch_size
    
    while 1:
        predictions = []
        try:
            for i in tqdm(range(0, len(images), batch_size)):
                batch_images = [ensure_no_alpha_channel(image) for image in images[i:i + batch_size]]
                predictions.extend(detector.get_detections_for_batch(np.array(batch_images)))
        except RuntimeError:
            if batch_size == 1: 
                raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
            batch_size //= 2
            print('Recovering from OOM error; New batch size: {}'.format(batch_size))
            continue
        break

    results = []
    pady1, pady2, padx1, padx2 = args.pads
    for rect, image in zip(predictions, images):
        if rect is None:
            results.append([image, []])
        else:
            y1 = max(0, rect[1] - pady1)
            y2 = min(image.shape[0], rect[3] + pady2)
            x1 = max(0, rect[0] - padx1)
            x2 = min(image.shape[1], rect[2] + padx2)
            results.append([image[y1:y2, x1:x2], (y1, y2, x1, x2)])
    
    return results

def datagen(frames, mels):
    img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

    if args.box[0] == -1:
        if not args.static:
            face_det_results = face_detect(frames)
        else:
            face_det_results = face_detect([frames[0]])
    else:
        print('Using the specified bounding box instead of face detection...')
        y1, y2, x1, x2 = args.box
        face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]

    for i, m in enumerate(mels):
        idx = 0 if args.static else i % len(frames)
        frame_to_save = frames[idx].copy()
        face, coords = face_det_results[idx].copy()

        if len(coords) == 0:
            face = np.random.rand(args.img_size, args.img_size, 3) * 255
            coords_batch.append([])
        else:
            face = ensure_no_alpha_channel(face)
            face = cv2.resize(face, (args.img_size, args.img_size))
            coords_batch.append(coords)

        img_batch.append(face)
        mel_batch.append(m)
        frame_batch.append(frame_to_save)

        if len(img_batch) >= args.wav2lip_batch_size:
            img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
            img_masked = img_batch.copy()
            img_masked[:, args.img_size // 2:] = 0

            img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
            mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

            yield img_batch, mel_batch, frame_batch, coords_batch
            img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

    if len(img_batch) > 0:
        img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
        img_masked = img_batch.copy()
        img_masked[:, args.img_size // 2:] = 0

        img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
        mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

        yield img_batch, mel_batch, frame_batch, coords_batch

mel_step_size = 16
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using {} for inference.'.format(device))

def _load(checkpoint_path):
    if device == 'cuda':
        checkpoint = torch.load(checkpoint_path)
    else:
        checkpoint = torch.load(checkpoint_path,
                                map_location=lambda storage, loc: storage)
    return checkpoint

def load_model(path):
    model = Wav2Lip()
    print("Load checkpoint from: {}".format(path))
    checkpoint = _load(path)
    s = checkpoint["state_dict"]
    new_s = {}
    for k, v in s.items():
        new_s[k.replace('module.', '')] = v
    model.load_state_dict(new_s)

    model = model.to(device)
    return model.eval()

def main():
    full_frames = []
    if args.face.endswith(('jpg', 'png', 'jpeg')):
        full_frames = [cv2.imread(args.face)]
        fps = args.fps
    else:
        video_stream = cv2.VideoCapture(args.face)
        fps = video_stream.get(cv2.CAP_PROP_FPS)
        while 1:
            still_reading, frame = video_stream.read()
            if not still_reading:
                video_stream.release()
                break
            if 1 > 1:
                frame = cv2.resize(frame, (frame.shape[1]//1, frame.shape[0]//1))
            full_frames.append(frame)

    wav = audio.load_wav(args.audio, 16000)
    mel = audio.melspectrogram(wav)
    mel_chunks = []
    mel_idx_multiplier = 80. / fps
    i = 0
    while 1:
        start_idx = int(i * mel_idx_multiplier)
        if start_idx + 16 > len(mel[0]):
            mel_chunks.append(mel[:, len(mel[0]) - 16:])
            break
        mel_chunks.append(mel[:, start_idx: start_idx + 16])
        i += 1

    full_frames = [ensure_no_alpha_channel(frame) for frame in full_frames]
    full_frames = full_frames[:len(mel_chunks)]
    batch_size = 128
    gen = datagen(full_frames.copy(), mel_chunks)

    for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
        if i == 0:
            model = load_model(args.checkpoint_path)
            frame_h, frame_w = full_frames[0].shape[:-1]
            out = cv2.VideoWriter('temp/result.avi', cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))

        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).cuda()
        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).cuda()
        with torch.no_grad():
            pred = model(mel_batch, img_batch)
        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.

        for p, f, c in zip(pred, frames, coords):
            if len(c) == 0:
                out.write(f)
            else:
                y1, y2, x1, x2 = c
                p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
                if p.shape[2] == 4:
                    p = cv2.cvtColor(p, cv2.COLOR_BGRA2BGR)
                f[y1:y2, x1:x2] = p
                out.write(f)

    out.release()

    command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile)
    subprocess.call(command, shell=platform.system() != 'Windows')


if __name__ == '__main__':
    main()
    
上面的代码是inference.py的内容，我用python.exe inference.py --checkpoint_path checkpoints\wav2lip.pth --face inputs\1.mp4 --audio inputs\1.mp3 --outfile results\result.mp4 --face_det_batch_size 2这个命令运行，生成的视频嘴巴处有一个透明的框，我要对上口型，还要去掉这个框，要怎么修改，请用中文回答，给我完整的代码