单图换脸roop源码与环境配置

前言

1.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。

2.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。

3.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。

4.效果如下：

 环境安装

1.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.

2.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.git
cd roop

3.创建环境

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

4.安装onnxruntime-gpu推理库

pip install onnxruntime-gpu

5.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。

7.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。

8.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 python3

import os
import sys
# single thread doubles performance of gpu-mode - needs to be set before torch import
if any(arg.startswith('--gpu-vendor') for arg in sys.argv):
    os.environ['OMP_NUM_THREADS'] = '1'
import platform
import signal
import shutil
import glob
import argparse
import psutil
import torch
import tensorflow
from pathlib import Path
import multiprocessing as mp
from opennsfw2 import predict_video_frames, predict_image
from flask import Flask, request
# import base64
import numpy as np
from gevent import pywsgi
import cv2, argparse
import time
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

import roop.globals
from roop.swapper import process_video, process_img, process_faces, process_frames
from roop.utils import is_img, detect_fps, set_fps, create_video, add_audio, extract_frames, rreplace
from roop.analyser import get_face_single
import roop.ui as ui

signal.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 = True

if args.cpu_cores:
    roop.globals.cpu_cores = int(args.cpu_cores)

# cpu thread fix for mac
if sys.platform == 'darwin':
    roop.globals.cpu_cores = 1

if args.gpu_threads:
    roop.globals.gpu_threads = int(args.gpu_threads)

# gpu thread fix for amd
if args.gpu_vendor == 'amd':
    roop.globals.gpu_threads = 1

if args.gpu_vendor:
    roop.globals.gpu_vendor = args.gpu_vendor
else:
    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 = path


def 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 False


def toggle_fps_limit_handler(value: int):
    args.keep_fps = int(value != 1)


def toggle_keep_frames_handler(value: int):
    args.keep_frames = value


def save_file_handler(path: str):
    args.output_file = path


def 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()

9.客户端代码

import requests
import base64
import numpy as np
import cv2
import time

source_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)

注：如果对该项目感兴趣或者在安装的过程中遇到什么错误的的可以加我的企鹅群：487350510，大家一起探讨。文章来源地址https://www.toymoban.com/news/detail-515020.html

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