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import cog |
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import tempfile |
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from pathlib import Path |
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import argparse |
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import cv2 |
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import torch |
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from PIL import Image |
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import torch.nn.functional as F |
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from torchvision import transforms |
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from models.models import create_model |
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from options.test_options import TestOptions |
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from util.reverse2original import reverse2wholeimage |
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from util.norm import SpecificNorm |
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from test_wholeimage_swapmulti import _totensor |
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from insightface_func.face_detect_crop_multi import Face_detect_crop as Face_detect_crop_multi |
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from insightface_func.face_detect_crop_single import Face_detect_crop as Face_detect_crop_single |
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class Predictor(cog.Predictor): |
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def setup(self): |
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self.transformer_Arcface = transforms.Compose([ |
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transforms.ToTensor(), |
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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]) |
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@cog.input("source", type=Path, help="source image") |
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@cog.input("target", type=Path, help="target image") |
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@cog.input("mode", type=str, options=['single', 'all'], default='all', |
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help="swap a single face (the one with highest confidence by face detection) or all faces in the target image") |
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def predict(self, source, target, mode='all'): |
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app = Face_detect_crop_multi(name='antelope', root='./insightface_func/models') |
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if mode == 'single': |
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app = Face_detect_crop_single(name='antelope', root='./insightface_func/models') |
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app.prepare(ctx_id=0, det_thresh=0.6, det_size=(640, 640)) |
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options = TestOptions() |
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options.initialize() |
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opt = options.parser.parse_args(["--Arc_path", 'arcface_model/arcface_checkpoint.tar', "--pic_a_path", str(source), |
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"--pic_b_path", str(target), "--isTrain", False, "--no_simswaplogo"]) |
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str_ids = opt.gpu_ids.split(',') |
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opt.gpu_ids = [] |
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for str_id in str_ids: |
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id = int(str_id) |
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if id >= 0: |
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opt.gpu_ids.append(id) |
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if len(opt.gpu_ids) > 0: |
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torch.cuda.set_device(opt.gpu_ids[0]) |
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torch.nn.Module.dump_patches = True |
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model = create_model(opt) |
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model.eval() |
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crop_size = opt.crop_size |
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spNorm = SpecificNorm() |
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with torch.no_grad(): |
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pic_a = opt.pic_a_path |
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img_a_whole = cv2.imread(pic_a) |
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img_a_align_crop, _ = app.get(img_a_whole, crop_size) |
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img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0], cv2.COLOR_BGR2RGB)) |
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img_a = self.transformer_Arcface(img_a_align_crop_pil) |
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img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2]) |
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img_id = img_id.cuda() |
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img_id_downsample = F.interpolate(img_id, size=(112,112)) |
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latend_id = model.netArc(img_id_downsample) |
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latend_id = F.normalize(latend_id, p=2, dim=1) |
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pic_b = opt.pic_b_path |
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img_b_whole = cv2.imread(pic_b) |
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img_b_align_crop_list, b_mat_list = app.get(img_b_whole, crop_size) |
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swap_result_list = [] |
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b_align_crop_tenor_list = [] |
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for b_align_crop in img_b_align_crop_list: |
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b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop, cv2.COLOR_BGR2RGB))[None, ...].cuda() |
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swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0] |
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swap_result_list.append(swap_result) |
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b_align_crop_tenor_list.append(b_align_crop_tenor) |
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net = None |
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out_path = Path(tempfile.mkdtemp()) / "output.png" |
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reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, None, |
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str(out_path), opt.no_simswaplogo, |
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pasring_model=net, use_mask=opt.use_mask, norm=spNorm) |
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return out_path |
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