前言

roop是新开源了一个单图就可以进行视频换脸的项目，只需要一张所需面部的图像。不需要数据集，不需要训练。

大概的测试了一下，正脸换脸效果还不错，融合也比较自然。但如果人脸比较大，最终换出的效果可能会有些模糊。侧脸部分的幅度不宜过大，否则会出现人脸乱飘的情况。在多人场景下，也容易出现混乱。

使用简单，在处理单人视频和单人图像还是的换脸效果还是可以的，融合得也不错，适合制作一些小视频或单人图像。

效果如下：

 环境安装

我这里部署部署环境是win 10、cuda 11.7、cudnn 8.5、GPU是N卡的3060（6G显存），加anaconda3.

源码下载，如果用不了git，可以下载打包好的源码和模型。

git clone https://GitHub.com/s0md3v/roop.gitcd roop

创建环境

conda create --name roop python=3.10activate roopconda install PyTorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidiapip install -r requirements.txt

安装onnxruntime-gpu推理库

pip install onnxruntime-gpu

运行程序

Python run.py

运行，它会下载一个500多m的模型，国内的网可能下载得很慢，也可以单独下载之后放到roop根目录下。

报错

FFmpeg is not installed!

这个是缺少了FFmpeg，FFmpeg是一套可以用来记录、转换数字音频、视频，并能将其转化为流的开源计算机程序。简单说来就是我们可以用它来进行视频的编解码，可以将视频文件转化为视频流，也可以将视频流转存储为视频文件。还有一个重点就是它是开源的。去官网下载后，加到环境变量就可以了。

如果在本地的机子跑起来很慢，把它做成服务器的方式运行，这样就可以在网页或者以微信公众 号或者小程序的方式访问，服务器端代码：

#!/usr/bin/env python3import osimport sys# single thread doubles perfORMance of gpu-mode - needs to be set before torch importif any(arg.startswith('--gpu-vendor') for arg in sys.argv):    os.environ['OMP_NUM_THREADS'] = '1'import platformimport signalimport shutilimport globimport argparseimport psutilimport torchimport Tensorflowfrom pathlib import Pathimport multiprocessing as mpfrom opennsfw2 import predict_video_frames, predict_imagefrom flask import Flask, request# import base64import numpy as npfrom gevent import pywsgiimport cv2, argparseimport timeos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'import roop.globalsfrom roop.swapper import process_video, process_img, process_faces, process_framesfrom roop.utils import is_img, detect_fps, set_fps, create_video, add_audio, extract_frames, rreplacefrom roop.analyser import get_face_singleimport roop.ui as uisignal.signal(signal.SIGINT, lambda signal_number, frame: quit())parser = argparse.ArgumentParser()parser.add_argument('-f', '--face', help='use this face', dest='source_img')parser.add_argument('-t', '--target', help='replace this face', dest='target_path')parser.add_argument('-o', '--output', help='save output to this file', dest='output_file')parser.add_argument('--keep-fps', help='maintain original fps', dest='keep_fps', action='store_true', default=False)parser.add_argument('--keep-frames', help='keep frames directory', dest='keep_frames', action='store_true', default=False)parser.add_argument('--all-faces', help='swap all faces in frame', dest='all_faces', action='store_true', default=False)parser.add_argument('--max-memory', help='maximum amount of RAM in GB to be used', dest='max_memory', type=int)parser.add_argument('--cpu-cores', help='number of CPU cores to use', dest='cpu_cores', type=int, default=max(psutil.cpu_count() / 2, 1))parser.add_argument('--gpu-threads', help='number of threads to be use for the GPU', dest='gpu_threads', type=int, default=8)parser.add_argument('--gpu-vendor', help='choice your GPU vendor', dest='gpu_vendor', default='nvidia', choices=['apple', 'amd', 'intel', 'nvidia'])args = parser.parse_known_args()[0]if 'all_faces' in args:    roop.globals.all_faces = Trueif args.cpu_cores:    roop.globals.cpu_cores = int(args.cpu_cores)# cpu thread fix for Macif sys.platform == 'darwin':    roop.globals.cpu_cores = 1if args.gpu_threads:    roop.globals.gpu_threads = int(args.gpu_threads)# gpu thread fix for amdif args.gpu_vendor == 'amd':    roop.globals.gpu_threads = 1if args.gpu_vendor:    roop.globals.gpu_vendor = args.gpu_vendorelse:    roop.globals.providers = ['CPUExecutionProvider']sep = "/"if os.name == "nt":    sep = "\\"def limit_resources():    # prevent tensorflow memory leak    gpus = tensorflow.config.experimental.list_physical_devices('GPU')    for gpu in gpus:        tensorflow.config.experimental.set_memory_growth(gpu, True)    if args.max_memory:        memory = args.max_memory * 1024 * 1024 * 1024        if str(platform.system()).lower() == 'windows':            import ctypes            kernel32 = ctypes.windll.kernel32            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))        else:            import resource            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))def pre_check():    if sys.version_info < (3, 9):        quit('Python version is not supported - please upgrade to 3.9 or higher')    if not shutil.which('ffmpeg'):        quit('ffmpeg is not installed!')    model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), '../inswapper_128.onnx')    if not os.path.isfile(model_path):        quit('File "inswapper_128.onnx" does not exist!')    if roop.globals.gpu_vendor == 'apple':        if 'CoreMLExecutionProvider' not in roop.globals.providers:            quit("You are using --gpu=apple flag but CoreML isn't available or properly installed on your system.")    if roop.globals.gpu_vendor == 'amd':        if 'ROCMExecutionProvider' not in roop.globals.providers:            quit("You are using --gpu=amd flag but ROCM isn't available or properly installed on your system.")    if roop.globals.gpu_vendor == 'nvidia':        CUDA_VERSION = torch.version.cuda        CUDNN_VERSION = torch.backends.cudnn.version()        if not torch.cuda.is_available():            quit("You are using --gpu=nvidia flag but CUDA isn't available or properly installed on your system.")        if CUDA_VERSION > '11.8':            quit(f"CUDA version {CUDA_VERSION} is not supported - please downgrade to 11.8")        if CUDA_VERSION < '11.4':            quit(f"CUDA version {CUDA_VERSION} is not supported - please upgrade to 11.8")        if CUDNN_VERSION < 8220:            quit(f"CUDNN version {CUDNN_VERSION} is not supported - please upgrade to 8.9.1")        if CUDNN_VERSION > 8910:            quit(f"CUDNN version {CUDNN_VERSION} is not supported - please downgrade to 8.9.1")def get_video_frame(video_path, frame_number = 1):    cap = cv2.VideoCapture(video_path)    amount_of_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)    cap.set(cv2.CAP_PROP_POS_FRAMES, min(amount_of_frames, frame_number-1))    if not cap.isOpened():        print("Error opening video file")        return    ret, frame = cap.read()    if ret:        return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)    cap.release()def preview_video(video_path):    cap = cv2.VideoCapture(video_path)    if not cap.isOpened():        print("Error opening video file")        return 0    amount_of_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)    ret, frame = cap.read()    if ret:        frame = get_video_frame(video_path)    cap.release()    return (amount_of_frames, frame)def status(string):    value = "Status: " + string    if 'cli_mode' in args:        print(value)    else:        ui.update_status_label(value)def process_video_multi_cores(source_img, frame_paths):    n = len(frame_paths) // roop.globals.cpu_cores    if n > 2:        processes = []        for i in range(0, len(frame_paths), n):            p = POOL.apply_async(process_video, args=(source_img, frame_paths[i:i + n],))            processes.append(p)        for p in processes:            p.get()        POOL.close()        POOL.join()def select_face_handler(path: str):    args.source_img = pathdef select_target_handler(path: str):    args.target_path = path    return preview_video(args.target_path)def toggle_all_faces_handler(value: int):    roop.globals.all_faces = True if value == 1 else Falsedef toggle_fps_limit_handler(value: int):    args.keep_fps = int(value != 1)def toggle_keep_frames_handler(value: int):    args.keep_frames = valuedef save_file_handler(path: str):    args.output_file = pathdef create_test_preview(frame_number):    return process_faces(        get_face_single(cv2.imread(args.source_img)),        get_video_frame(args.target_path, frame_number)    )app = Flask(__name__)@app.route('/face_swap', methods=['POST'])def face_swap():    if request.method == 'POST':        args.source_img=request.form.get('source_img')        args.target_path = request.form.get('target_path')        args.output_file = request.form.get('output_path')        keep_fps = request.form.get('keep_fps')        if keep_fps == '0':            args.keep_fps = False        else:            args.keep_fps = True                Keep_frames = request.form.get('Keep_frames')        if Keep_frames == '0':            args.Keep_frames = False        else:            args.Keep_frames = True        all_faces = request.form.get('all_faces')        if all_faces == '0':            args.all_faces = False        else:            args.all_faces = True    if not args.source_img or not os.path.isfile(args.source_img):        print("\n[WARNING] Please select an image containing a face.")        return    elif not args.target_path or not os.path.isfile(args.target_path):        print("\n[WARNING] Please select a video/image to swap face in.")        return    if not args.output_file:        target_path = args.target_path        args.output_file = rreplace(target_path, "/", "/swapped-", 1) if "/" in target_path else "swapped-" + target_path    target_path = args.target_path    test_face = get_face_single(cv2.imread(args.source_img))    if not test_face:        print("\n[WARNING] No face detected in source image. Please try with another one.\n")        return    if is_img(target_path):        if predict_image(target_path) > 0.85:            quit()        process_img(args.source_img, target_path, args.output_file)        # status("swap successful!")        return 'ok'        seconds, probabilities = predict_video_frames(video_path=args.target_path, frame_interval=100)    if any(probability > 0.85 for probability in probabilities):        quit()    video_name_full = target_path.split("/")[-1]    video_name = os.path.splitext(video_name_full)[0]    output_dir = os.path.dirname(target_path) + "/" + video_name if os.path.dirname(target_path) else video_name    Path(output_dir).mkdir(exist_ok=True)    # status("detecting video's FPS...")    fps, exact_fps = detect_fps(target_path)        if not args.keep_fps and fps > 30:        this_path = output_dir + "/" + video_name + ".mp4"        set_fps(target_path, this_path, 30)        target_path, exact_fps = this_path, 30    else:        shutil.copy(target_path, output_dir)    # status("extracting frames...")    extract_frames(target_path, output_dir)    args.frame_paths = tuple(sorted(        glob.glob(output_dir + "/*.png"),        key=lambda x: int(x.split(sep)[-1].replace(".png", ""))    ))    # status("swapping in progress...")    if roop.globals.gpu_vendor is None and roop.globals.cpu_cores > 1:        global POOL        POOL = mp.Pool(roop.globals.cpu_cores)        process_video_multi_cores(args.source_img, args.frame_paths)    else:        process_video(args.source_img, args.frame_paths)    # status("creating video...")    create_video(video_name, exact_fps, output_dir)    # status("adding audio...")    add_audio(output_dir, target_path, video_name_full, args.keep_frames, args.output_file)    save_path = args.output_file if args.output_file else output_dir + "/" + video_name + ".mp4"    print("\n\nVideo saved as:", save_path, "\n\n")    # status("swap successful!")    return 'ok'if __name__ == "__main__":    print('Statrt server----------------')    server = pywsgi.WSGIServer(('127.0.0.1', 5020), app)    server.serve_forever()

客户端代码

import requestsimport base64import numpy as npimport cv2import timesource_img = "z1.jpg" #要换的脸target_path= "z2.mp4" #目标图像或者视频output_path = "zface2.mp4" #保存的目录和文件名keep_fps = '0' #视频,是否保持原帧率Keep_frames = '0' all_faces = '0' #data = {'source_img': source_img,'target_path' : target_path,'output_path':output_path,        'keep-fps' : keep_fps,'Keep_frames':Keep_frames,'all_faces':all_faces}resp = requests.post("Http://127.0.0.1:5020/face_swap", data=data)print(resp.content)

来源地址：https://blog.csdn.net/matt45m/article/details/131280825