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最新推荐文章于 2025-11-21 05:43:32 发布

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#git #github

部署运行你感兴趣的模型镜像

这是MultiSpecific.ipynb


## make sure you are using a runtime with GPU
## you can check at Runtime/Change runtime type in the top bar.
!nvidia-smi

!git clone https://github.com/neuralchen/SimSwap
!cd SimSwap && git pull

!pip install insightface==0.2.1 onnxruntime moviepy
!pip install googledrivedownloader
!pip install imageio==2.4.1

import os
os.chdir("SimSwap")
!ls


## You can upload filed manually
# from google.colab import drive
# drive.mount('/content/gdrive')


from google_drive_downloader import GoogleDriveDownloader

### it seems that google drive link may not be permenant, you can find this ID from our open url.
# GoogleDriveDownloader.download_file_from_google_drive(file_id='1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N',
#                                     dest_path='./arcface_model/arcface_checkpoint.tar')
# GoogleDriveDownloader.download_file_from_google_drive(file_id='1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI',
#                                     dest_path='./checkpoints.zip')

!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar
!wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip
!unzip ./checkpoints.zip  -d ./checkpoints
!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth

!wget --no-check-certificate "https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w" -O antelope.zip
!unzip ./antelope.zip -d ./insightface_func/models/

import cv2
import torch
import fractions
import numpy as np
from PIL import Image
import torch.nn.functional as F
from torchvision import transforms
from models.models import create_model
from options.test_options import TestOptions
from insightface_func.face_detect_crop_multi import Face_detect_crop
from util.videoswap_multispecific import video_swap
import os
import glob


def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0

transformer = transforms.Compose([
        transforms.ToTensor(),
        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

transformer_Arcface = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

     
!ls ./checkpoints



opt = TestOptions()
opt.initialize()
opt.parser.add_argument('-f') ## dummy arg to avoid bug
opt = opt.parse()
opt.multisepcific_dir = './demo_file/multispecific' ## or replace it with folder from your own google drive
                           ## and remember to follow the dir structure in usage.md
opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive
opt.output_path = './output/multi_test_multispecific.mp4'
opt.temp_path = './tmp'
opt.Arc_path = './arcface_model/arcface_checkpoint.tar'
opt.name = 'people'
opt.isTrain = False
opt.use_mask = True  ## new feature up-to-date

crop_size = opt.crop_size

下面是models.py

import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Parameter
from .config import device, num_classes


def create_model(opt):
    if opt.model == 'pix2pixHD':
        #from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
        from .fs_model import fsModel
        model = fsModel()
    else:
        from .ui_model import UIModel
        model = UIModel()

    model.initialize(opt)
    if opt.verbose:
        print("model [%s] was created" % (model.name()))

    if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)

    return model



class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            nn.PReLU(),
            nn.Linear(channel // reduction, channel),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


class IRBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
        super(IRBlock, self).__init__()
        self.bn0 = nn.BatchNorm2d(inplanes)
        self.conv1 = conv3x3(inplanes, inplanes)
        self.bn1 = nn.BatchNorm2d(inplanes)
        self.prelu = nn.PReLU()
        self.conv2 = conv3x3(inplanes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.use_se = use_se
        if self.use_se:
            self.se = SEBlock(planes)

    def forward(self, x):
        residual = x
        out = self.bn0(x)
        out = self.conv1(out)
        out = self.bn1(out)
        out = self.prelu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        if self.use_se:
            out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.prelu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, use_se=True):
        self.inplanes = 64
        self.use_se = use_se
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.prelu = nn.PReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.bn2 = nn.BatchNorm2d(512)
        self.dropout = nn.Dropout()
        self.fc = nn.Linear(512 * 7 * 7, 512)
        self.bn3 = nn.BatchNorm1d(512)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
        self.inplanes = planes
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, use_se=self.use_se))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.bn2(x)
        x = self.dropout(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        x = self.bn3(x)

        return x


class ArcMarginModel(nn.Module):
    def __init__(self, args):
        super(ArcMarginModel, self).__init__()

        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
        nn.init.xavier_uniform_(self.weight)

        self.easy_margin = args.easy_margin
        self.m = args.margin_m
        self.s = args.margin_s

        self.cos_m = math.cos(self.m)
        self.sin_m = math.sin(self.m)
        self.th = math.cos(math.pi - self.m)
        self.mm = math.sin(math.pi - self.m) * self.m

    def forward(self, input, label):
        x = F.normalize(input)
        W = F.normalize(self.weight)
        cosine = F.linear(x, W)
        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
        if self.easy_margin:
            phi = torch.where(cosine > 0, phi, cosine)
        else:
            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
        one_hot = torch.zeros(cosine.size(), device=device)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
        output *= self.s
        return output

下面是test_options.py

'''
Author: Naiyuan liu
Github: https://github.com/NNNNAI
Date: 2021-11-23 17:03:58
LastEditors: Naiyuan liu
LastEditTime: 2021-11-23 17:08:08
Description: 
'''
from .base_options import BaseOptions

class TestOptions(BaseOptions):
    def initialize(self):
        BaseOptions.initialize(self)
        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
        self.parser.add_argument("--Arc_path", type=str, default='arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
        self.parser.add_argument("--pic_a_path", type=str, default='G:/swap_data/ID/elon-musk-hero-image.jpeg', help="Person who provides identity information")
        self.parser.add_argument("--pic_b_path", type=str, default='G:/swap_data/ID/bengio.jpg', help="Person who provides information other than their identity")
        self.parser.add_argument("--pic_specific_path", type=str, default='./crop_224/zrf.jpg', help="The specific person to be swapped")
        self.parser.add_argument("--multisepcific_dir", type=str, default='./demo_file/multispecific', help="Dir for multi specific")
        self.parser.add_argument("--video_path", type=str, default='G:/swap_data/video/HSB_Demo_Trim.mp4', help="path for the video to swap")
        self.parser.add_argument("--temp_path", type=str, default='./temp_results', help="path to save temporarily images")
        self.parser.add_argument("--output_path", type=str, default='./output/', help="results path")
        self.parser.add_argument('--id_thres', type=float, default=0.03, help='how many test images to run')
        self.parser.add_argument('--no_simswaplogo', action='store_true', help='Remove the watermark')
        self.parser.add_argument('--use_mask', action='store_true', help='Use mask for better result')
        self.parser.add_argument('--crop_size', type=int, default=224, help='Crop of size of input image')
        
        self.isTrain = False

下面是face_detect_crop_multi.py

'''
Author: Naiyuan liu
Github: https://github.com/NNNNAI
Date: 2021-11-23 17:03:58
LastEditors: Naiyuan liu
LastEditTime: 2021-11-24 16:45:41
Description: 
'''
from __future__ import division
import collections
import numpy as np
import glob
import os
import os.path as osp
import cv2
from insightface.model_zoo import model_zoo
from insightface_func.utils import face_align_ffhqandnewarc as face_align

__all__ = ['Face_detect_crop', 'Face']

Face = collections.namedtuple('Face', [
    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
    'embedding_norm', 'normed_embedding',
    'landmark'
])

Face.__new__.__defaults__ = (None, ) * len(Face._fields)


class Face_detect_crop:
    def __init__(self, name, root='~/.insightface_func/models'):
        self.models = {}
        root = os.path.expanduser(root)
        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
        onnx_files = sorted(onnx_files)
        for onnx_file in onnx_files:
            if onnx_file.find('_selfgen_')>0:
                #print('ignore:', onnx_file)
                continue
            model = model_zoo.get_model(onnx_file)
            if model.taskname not in self.models:
                print('find model:', onnx_file, model.taskname)
                self.models[model.taskname] = model
            else:
                print('duplicated model task type, ignore:', onnx_file, model.taskname)
                del model
        assert 'detection' in self.models
        self.det_model = self.models['detection']


    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
        self.det_thresh = det_thresh
        self.mode = mode
        assert det_size is not None
        print('set det-size:', det_size)
        self.det_size = det_size
        for taskname, model in self.models.items():
            if taskname=='detection':
                model.prepare(ctx_id, input_size=det_size)
            else:
                model.prepare(ctx_id)

    def get(self, img, crop_size, max_num=0):
        bboxes, kpss = self.det_model.detect(img,
                                             threshold=self.det_thresh,
                                             max_num=max_num,
                                             metric='default')
        if bboxes.shape[0] == 0:
            return None
        ret = []
        # for i in range(bboxes.shape[0]):
        #     bbox = bboxes[i, 0:4]
        #     det_score = bboxes[i, 4]
        #     kps = None
        #     if kpss is not None:
        #         kps = kpss[i]
        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
        align_img_list = []
        M_list = []
        for i in range(bboxes.shape[0]):
            kps = None
            if kpss is not None:
                kps = kpss[i]
            M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
            align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
            align_img_list.append(align_img)
            M_list.append(M)

        # det_score = bboxes[..., 4]

        # best_index = np.argmax(det_score)

        # kps = None
        # if kpss is not None:
        #     kps = kpss[best_index]
        # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
        # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
        
        return align_img_list, M_list

下面是

import os 
import cv2
import glob
import torch
import shutil
import numpy as np
from tqdm import tqdm
from util.reverse2original import reverse2wholeimage
import moviepy.editor as mp
from moviepy.editor import AudioFileClip, VideoFileClip 
from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
import  time
from util.add_watermark import watermark_image
from util.norm import SpecificNorm
import torch.nn.functional as F
from parsing_model.model import BiSeNet

def _totensor(array):
    tensor = torch.from_numpy(array)
    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
    return img.float().div(255)

def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
    video_forcheck = VideoFileClip(video_path)
    if video_forcheck.audio is None:
        no_audio = True
    else:
        no_audio = False

    del video_forcheck

    if not no_audio:
        video_audio_clip = AudioFileClip(video_path)

    video = cv2.VideoCapture(video_path)
    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
    ret = True
    frame_index = 0

    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))

    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))

    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
    
    fps = video.get(cv2.CAP_PROP_FPS)
    if  os.path.exists(temp_results_dir):
            shutil.rmtree(temp_results_dir)

    spNorm =SpecificNorm()
    mse = torch.nn.MSELoss().cuda()

    if use_mask:
        n_classes = 19
        net = BiSeNet(n_classes=n_classes)
        net.cuda()
        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
        net.load_state_dict(torch.load(save_pth))
        net.eval()
    else:
        net =None

    # while ret:
    for frame_index in tqdm(range(frame_count)): 
        ret, frame = video.read()
        if  ret:
            detect_results = detect_model.get(frame,crop_size)

            if detect_results is not None:
                # print(frame_index)
                if not os.path.exists(temp_results_dir):
                        os.mkdir(temp_results_dir)
                frame_align_crop_list = detect_results[0]
                frame_mat_list = detect_results[1]

                id_compare_values = [] 
                frame_align_crop_tenor_list = []
                for frame_align_crop in frame_align_crop_list:

                    # BGR TO RGB
                    # frame_align_crop_RGB = frame_align_crop[...,::-1]

                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()

                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
                    id_compare_values.append([])
                    for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
                        id_compare_values[-1].append(mse(frame_align_crop_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)

                id_compare_values_array = np.array(id_compare_values).transpose(1,0)
                min_indexs = np.argmin(id_compare_values_array,axis=0)
                min_value = np.min(id_compare_values_array,axis=0)

                swap_result_list = [] 
                swap_result_matrix_list = []
                swap_result_ori_pic_list = []
                for tmp_index, min_index in enumerate(min_indexs):
                    if min_value[tmp_index] < id_thres:
                        swap_result = swap_model(None, frame_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
                        swap_result_list.append(swap_result)
                        swap_result_matrix_list.append(frame_mat_list[tmp_index])
                        swap_result_ori_pic_list.append(frame_align_crop_tenor_list[tmp_index])
                    else:
                        pass



                if len(swap_result_list) !=0:
                    
                    reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
                else:
                    if not os.path.exists(temp_results_dir):
                        os.mkdir(temp_results_dir)
                    frame = frame.astype(np.uint8)
                    if not no_simswaplogo:
                        frame = logoclass.apply_frames(frame)
                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)

            else:
                if not os.path.exists(temp_results_dir):
                    os.mkdir(temp_results_dir)
                frame = frame.astype(np.uint8)
                if not no_simswaplogo:
                    frame = logoclass.apply_frames(frame)
                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
        else:
            break

    video.release()

    # image_filename_list = []
    path = os.path.join(temp_results_dir,'*.jpg')
    image_filenames = sorted(glob.glob(path))

    clips = ImageSequenceClip(image_filenames,fps = fps)

    if not no_audio:
        clips = clips.set_audio(video_audio_clip)


    clips.write_videofile(save_path,audio_codec='aac')

下面是我平常最后生成视频用到的主函数

import cv2
import torch
import fractions
import numpy as np
from PIL import Image
import torch.nn.functional as F
from torchvision import transforms
from models.models import create_model
from options.test_options import TestOptions
from insightface_func.face_detect_crop_multi import Face_detect_crop
from util.videoswap import video_swap
from util.add_watermark import watermark_image

transformer = transforms.Compose([
        transforms.ToTensor(),
        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

transformer_Arcface = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

detransformer = transforms.Compose([
        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
    ])

opt = TestOptions()
opt.initialize()
opt.parser.add_argument('-f') ## dummy arg to avoid bug
opt = opt.parse()
opt.pic_a_path = './demo_file/Iron_man.jpg' ## or replace it with image from your own google drive
opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive
opt.output_path = './output/demo.mp4'
opt.temp_path = './tmp'
opt.Arc_path = './arcface_model/arcface_checkpoint.tar'
opt.isTrain = False
opt.use_mask = True  ## new feature up-to-date

crop_size = opt.crop_size

torch.nn.Module.dump_patches = True
model = create_model(opt)
model.eval()

app = Face_detect_crop(name='antelope', root='./insightface_func/models')
app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))

with torch.no_grad():
    pic_a = opt.pic_a_path
    # img_a = Image.open(pic_a).convert('RGB')
    img_a_whole = cv2.imread(pic_a)
    img_a_align_crop, _ = app.get(img_a_whole,crop_size)
    img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
    img_a = transformer_Arcface(img_a_align_crop_pil)
    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])

    # convert numpy to tensor
    img_id = img_id.cuda()

    #create latent id
    img_id_downsample = F.interpolate(img_id, size=(112,112))
    latend_id = model.netArc(img_id_downsample)
    latend_id = latend_id.detach().to('cpu')
    latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
    latend_id = latend_id.to('cuda')

    video_swap(opt.video_path, latend_id, model, app, opt.output_path, temp_results_dir=opt.temp_path, use_mask=opt.use_mask)

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