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：
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
