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@@ -0,0 +1,75 @@
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+
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+import cv2
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+import torch
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+import fractions
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+import numpy as np
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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 insightface_func.face_detect_crop_single import Face_detect_crop
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+from util.videoswap import video_swap
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+import os
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+from moviepy.editor import AudioFileClip
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+
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+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
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+
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+transformer = 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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+
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+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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+
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+detransformer = transforms.Compose([
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+ transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
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+ transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
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+ ])
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+
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+
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+if __name__ == '__main__':
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+ opt = TestOptions().parse()
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+
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+ start_epoch, epoch_iter = 1, 0
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+ crop_size = 224
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+
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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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+
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+
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+ app = Face_detect_crop(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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+
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+ pic_a = opt.pic_a_path
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+ # img_a = Image.open(pic_a).convert('RGB')
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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 = 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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+
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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, b_mat = app.get(img_b_whole,crop_size)
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+ # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB))
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+ # img_b = transformer(img_b_align_crop_pil)
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+ # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
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+
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+ # convert numpy to tensor
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+ img_id = img_id.cuda()
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+ # img_att = img_att.cuda()
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+
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+ #create latent id
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+ img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
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+ latend_id = model.netArc(img_id_downsample)
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+ latend_id = latend_id.detach().to('cpu')
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+ latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
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+ latend_id = latend_id.to('cuda')
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+
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+ video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path)
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+
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