Create app_new.py

app_new.py

@@ -0,0 +1,413 @@
+import os
+import time
+import pdb
+import re
+
+import gradio as gr
+import spaces
+import numpy as np
+import sys
+import subprocess
+
+from huggingface_hub import snapshot_download
+import requests
+
+import argparse
+import os
+from omegaconf import OmegaConf
+import numpy as np
+import cv2
+import torch
+import glob
+import pickle
+from tqdm import tqdm
+import copy
+from argparse import Namespace
+import shutil
+import gdown
+import imageio
+import ffmpeg
+from moviepy.editor import *
+
+
+ProjectDir = os.path.abspath(os.path.dirname(__file__))
+CheckpointsDir = os.path.join(ProjectDir, "models")
+
+def print_directory_contents(path):
+    for child in os.listdir(path):
+        child_path = os.path.join(path, child)
+        if os.path.isdir(child_path):
+            print(child_path)
+
+def download_model():
+    if not os.path.exists(CheckpointsDir):
+        os.makedirs(CheckpointsDir)
+        print("Checkpoint Not Downloaded, start downloading...")
+        tic = time.time()
+        snapshot_download(
+            repo_id="TMElyralab/MuseTalk",
+            local_dir=CheckpointsDir,
+            max_workers=8,
+            local_dir_use_symlinks=True,
+            force_download=True, resume_download=False
+        )
+        # weight
+        os.makedirs(f"{CheckpointsDir}/sd-vae-ft-mse/")
+        snapshot_download(
+            repo_id="stabilityai/sd-vae-ft-mse",
+            local_dir=CheckpointsDir+'/sd-vae-ft-mse',
+            max_workers=8,
+            local_dir_use_symlinks=True,
+            force_download=True, resume_download=False
+        )
+        #dwpose
+        os.makedirs(f"{CheckpointsDir}/dwpose/")
+        snapshot_download(
+            repo_id="yzd-v/DWPose",
+            local_dir=CheckpointsDir+'/dwpose',
+            max_workers=8,
+            local_dir_use_symlinks=True,
+            force_download=True, resume_download=False
+        )
+        #vae
+        url = "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt"
+        response = requests.get(url)
+        # 确保请求成功
+        if response.status_code == 200:
+            # 指定文件保存的位置
+            file_path = f"{CheckpointsDir}/whisper/tiny.pt"
+            os.makedirs(f"{CheckpointsDir}/whisper/")
+            # 将文件内容写入指定位置
+            with open(file_path, "wb") as f:
+                f.write(response.content)
+        else:
+            print(f"请求失败，状态码：{response.status_code}")
+        #gdown face parse
+        url = "https://drive.google.com/uc?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812"
+        os.makedirs(f"{CheckpointsDir}/face-parse-bisent/")
+        file_path = f"{CheckpointsDir}/face-parse-bisent/79999_iter.pth"
+        gdown.download(url, file_path, quiet=False)
+        #resnet
+        url = "https://download.pytorch.org/models/resnet18-5c106cde.pth"
+        response = requests.get(url)
+        # 确保请求成功
+        if response.status_code == 200:
+            # 指定文件保存的位置
+            file_path = f"{CheckpointsDir}/face-parse-bisent/resnet18-5c106cde.pth"
+            # 将文件内容写入指定位置
+            with open(file_path, "wb") as f:
+                f.write(response.content)
+        else:
+            print(f"请求失败，状态码：{response.status_code}")
+
+
+        toc = time.time()
+
+        print(f"download cost {toc-tic} seconds")
+        print_directory_contents(CheckpointsDir)
+
+    else:
+        print("Already download the model.")
+
+
+
+
+
+download_model()  # for huggingface deployment.
+
+
+from musetalk.utils.utils import get_file_type,get_video_fps,datagen
+from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder,get_bbox_range
+from musetalk.utils.blending import get_image
+from musetalk.utils.utils import load_all_model
+
+
+
+
+
+
+@spaces.GPU(duration=600)
+@torch.no_grad()
+def inference(audio_path,video_path,bbox_shift,progress=gr.Progress(track_tqdm=True)):
+    args_dict={"result_dir":'./results/output', "fps":25, "batch_size":8, "output_vid_name":'', "use_saved_coord":False}#same with inferenece script
+    args = Namespace(**args_dict)
+
+    input_basename = os.path.basename(video_path).split('.')[0]
+    audio_basename  = os.path.basename(audio_path).split('.')[0]
+    output_basename = f"{input_basename}_{audio_basename}"
+    result_img_save_path = os.path.join(args.result_dir, output_basename) # related to video & audio inputs
+    crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input
+    os.makedirs(result_img_save_path,exist_ok =True)
+
+    if args.output_vid_name=="":
+        output_vid_name = os.path.join(args.result_dir, output_basename+".mp4")
+    else:
+        output_vid_name = os.path.join(args.result_dir, args.output_vid_name)
+    ############################################## extract frames from source video ##############################################
+    if get_file_type(video_path)=="video":
+        save_dir_full = os.path.join(args.result_dir, input_basename)
+        os.makedirs(save_dir_full,exist_ok = True)
+        # cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
+        # os.system(cmd)
+        # 读取视频
+        reader = imageio.get_reader(video_path)
+
+        # 保存图片
+        for i, im in enumerate(reader):
+            imageio.imwrite(f"{save_dir_full}/{i:08d}.png", im)
+        input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]')))
+        fps = get_video_fps(video_path)
+    else: # input img folder
+        input_img_list = glob.glob(os.path.join(video_path, '*.[jpJP][pnPN]*[gG]'))
+        input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+        fps = args.fps
+    #print(input_img_list)
+    ############################################## extract audio feature ##############################################
+    whisper_feature = audio_processor.audio2feat(audio_path)
+    whisper_chunks = audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps)
+    ############################################## preprocess input image  ##############################################
+    if os.path.exists(crop_coord_save_path) and args.use_saved_coord:
+        print("using extracted coordinates")
+        with open(crop_coord_save_path,'rb') as f:
+            coord_list = pickle.load(f)
+        frame_list = read_imgs(input_img_list)
+    else:
+        print("extracting landmarks...time consuming")
+        coord_list, frame_list = get_landmark_and_bbox(input_img_list, bbox_shift)
+        with open(crop_coord_save_path, 'wb') as f:
+            pickle.dump(coord_list, f)
+    bbox_shift_text=get_bbox_range(input_img_list, bbox_shift)
+    i = 0
+    input_latent_list = []
+    for bbox, frame in zip(coord_list, frame_list):
+        if bbox == coord_placeholder:
+            continue
+        x1, y1, x2, y2 = bbox
+        crop_frame = frame[y1:y2, x1:x2]
+        crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
+        latents = vae.get_latents_for_unet(crop_frame)
+        input_latent_list.append(latents)
+
+    # to smooth the first and the last frame
+    frame_list_cycle = frame_list + frame_list[::-1]
+    coord_list_cycle = coord_list + coord_list[::-1]
+    input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
+    ############################################## inference batch by batch ##############################################
+    print("start inference")
+    video_num = len(whisper_chunks)
+    batch_size = args.batch_size
+    gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size)
+    res_frame_list = []
+    for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))):
+        
+        tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch]
+        audio_feature_batch = torch.stack(tensor_list).to(unet.device) # torch, B, 5*N,384
+        audio_feature_batch = pe(audio_feature_batch)
+        
+        pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
+        recon = vae.decode_latents(pred_latents)
+        for res_frame in recon:
+            res_frame_list.append(res_frame)
+            
+    ############################################## pad to full image ##############################################
+    print("pad talking image to original video")
+    for i, res_frame in enumerate(tqdm(res_frame_list)):
+        bbox = coord_list_cycle[i%(len(coord_list_cycle))]
+        ori_frame = copy.deepcopy(frame_list_cycle[i%(len(frame_list_cycle))])
+        x1, y1, x2, y2 = bbox
+        try:
+            res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
+        except:
+    #                 print(bbox)
+            continue
+        
+        combine_frame = get_image(ori_frame,res_frame,bbox)
+        cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png",combine_frame)
+        
+    # cmd_img2video = f"ffmpeg -y -v fatal -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p temp.mp4"
+    # print(cmd_img2video)
+    # os.system(cmd_img2video)
+    # 帧率
+    fps = 25
+    # 图片路径
+    # 输出视频路径
+    output_video = 'temp.mp4'
+
+    # 读取图片
+    def is_valid_image(file):
+        pattern = re.compile(r'\d{8}\.png')
+        return pattern.match(file)
+
+    images = []
+    files = [file for file in os.listdir(result_img_save_path) if is_valid_image(file)]
+    files.sort(key=lambda x: int(x.split('.')[0]))
+
+    for file in files:
+        filename = os.path.join(result_img_save_path, file)
+        images.append(imageio.imread(filename))
+        
+
+    # 保存视频
+    imageio.mimwrite(output_video, images, 'FFMPEG', fps=fps, codec='libx264', pixelformat='yuv420p')
+
+    # cmd_combine_audio = f"ffmpeg -y -v fatal -i {audio_path} -i temp.mp4 {output_vid_name}"
+    # print(cmd_combine_audio)
+    # os.system(cmd_combine_audio)
+
+    input_video = './temp.mp4'
+    # Check if the input_video and audio_path exist
+    if not os.path.exists(input_video):
+        raise FileNotFoundError(f"Input video file not found: {input_video}")
+    if not os.path.exists(audio_path):
+        raise FileNotFoundError(f"Audio file not found: {audio_path}")
+    
+    # 读取视频
+    reader = imageio.get_reader(input_video)
+    fps = reader.get_meta_data()['fps']  # 获取原视频的帧率
+
+    # 将帧存储在列表中
+    frames = images
+
+    # 保存视频并添加音频
+    # imageio.mimwrite(output_vid_name, frames, 'FFMPEG', fps=fps, codec='libx264', audio_codec='aac', input_params=['-i', audio_path])
+    
+    # input_video = ffmpeg.input(input_video)
+    
+    # input_audio = ffmpeg.input(audio_path)
+    
+    print(len(frames))
+
+    # imageio.mimwrite(
+    #     output_video,
+    #     frames,
+    #     'FFMPEG',
+    #     fps=25,
+    #     codec='libx264',
+    #     audio_codec='aac',
+    #     input_params=['-i', audio_path],
+    #     output_params=['-y'],  # Add the '-y' flag to overwrite the output file if it exists
+    # )
+    # writer = imageio.get_writer(output_vid_name, fps = 25, codec='libx264', quality=10, pixelformat='yuvj444p')
+    # for im in frames:
+    #     writer.append_data(im)
+    # writer.close()
+
+
+
+
+    # Load the video
+    video_clip = VideoFileClip(input_video)
+
+    # Load the audio
+    audio_clip = AudioFileClip(audio_path)
+
+    # Set the audio to the video
+    video_clip = video_clip.set_audio(audio_clip)
+
+    # Write the output video
+    video_clip.write_videofile(output_vid_name, codec='libx264', audio_codec='aac',fps=25)
+
+    os.remove("temp.mp4")
+    #shutil.rmtree(result_img_save_path)
+    print(f"result is save to {output_vid_name}")
+    return output_vid_name,bbox_shift_text
+
+
+
+# load model weights
+audio_processor,vae,unet,pe  = load_all_model()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+timesteps = torch.tensor([0], device=device)
+
+
+
+
+def check_video(video):
+    if not isinstance(video, str):
+        return video # in case of none type
+    # Define the output video file name
+    dir_path, file_name = os.path.split(video)
+    if file_name.startswith("outputxxx_"):
+        return video
+    # Add the output prefix to the file name
+    output_file_name = "outputxxx_" + file_name
+
+    os.makedirs('./results',exist_ok=True)
+    os.makedirs('./results/output',exist_ok=True)
+    os.makedirs('./results/input',exist_ok=True)
+
+    # Combine the directory path and the new file name
+    output_video = os.path.join('./results/input', output_file_name)
+
+
+    # # Run the ffmpeg command to change the frame rate to 25fps
+    # command = f"ffmpeg -i {video} -r 25 -vcodec libx264 -vtag hvc1 -pix_fmt yuv420p crf 18   {output_video}  -y"
+
+    # 读取视频
+    reader = imageio.get_reader(video)
+    fps = reader.get_meta_data()['fps']  # 获取原视频的帧率
+
+    # 将帧存储在列表中
+    frames = [im for im in reader]
+
+    # 保存视频
+    imageio.mimwrite(output_video, frames, 'FFMPEG', fps=25, codec='libx264', quality=9, pixelformat='yuv420p')
+    return output_video
+
+
+
+
+css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height: 576px}"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(
+        "<div align='center'> <h1>MuseTalk: Real-Time High Quality Lip Synchronization with Latent Space Inpainting </span> </h1> \
+                    <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\
+                    </br>\
+                    Yue Zhang <sup>\*</sup>,\
+                    Minhao Liu<sup>\*</sup>,\
+                    Zhaokang Chen,\
+                    Bin Wu<sup>†</sup>,\
+                    Yingjie He,\
+                    Chao Zhan,\
+                    Wenjiang Zhou\
+                    (<sup>*</sup>Equal Contribution, <sup>†</sup>Corresponding Author, benbinwu@tencent.com)\
+                    Lyra Lab, Tencent Music Entertainment\
+                </h2> \
+                <a style='font-size:18px;color: #000000' href='https://github.com/TMElyralab/MuseTalk'>[Github Repo]</a>\
+                <a style='font-size:18px;color: #000000' href='https://github.com/TMElyralab/MuseTalk'>[Huggingface]</a>\
+                <a style='font-size:18px;color: #000000' href=''> [Technical report(Coming Soon)] </a>\
+                <a style='font-size:18px;color: #000000' href=''> [Project Page(Coming Soon)] </a>  </div>"
+    )
+
+    with gr.Row():
+        with gr.Column():
+            audio = gr.Audio(label="Driven Audio",type="filepath")
+            video = gr.Video(label="Reference Video",sources=['upload'])
+            bbox_shift = gr.Number(label="BBox_shift value, px", value=0)
+            bbox_shift_scale = gr.Textbox(label="BBox_shift recommend value lower bound,The corresponding bbox range is generated after the initial result is generated. \n If the result is not good, it can be adjusted according to this reference value", value="",interactive=False)
+
+            btn = gr.Button("Generate")
+        out1 = gr.Video()
+    
+    video.change(
+        fn=check_video, inputs=[video], outputs=[video]
+    )
+    btn.click(
+        fn=inference,
+        inputs=[
+            audio,
+            video,
+            bbox_shift,
+        ],
+        outputs=[out1,bbox_shift_scale]
+    )
+
+# Set the IP and port
+ip_address = "0.0.0.0"  # Replace with your desired IP address
+port_number = 7860  # Replace with your desired port number
+
+
+demo.queue().launch(
+    share=True, debug=True, show_error=True #server_name=ip_address, server_port=port_number
+)