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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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def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0 |
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transformer = transforms.Compose([ |
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transforms.ToTensor(), |
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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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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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if __name__ == '__main__': |
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opt = TestOptions().parse() |
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start_epoch, epoch_iter = 1, 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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with torch.no_grad(): |
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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 = transformer_Arcface(img_a) |
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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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pic_b = opt.pic_b_path |
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img_b = Image.open(pic_b).convert('RGB') |
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img_b = transformer(img_b) |
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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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img_id = img_id.cuda() |
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img_att = img_att.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 = 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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img_fake = model(img_id, img_att, latend_id, latend_id, True) |
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for i in range(img_id.shape[0]): |
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if i == 0: |
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row1 = img_id[i] |
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row2 = img_att[i] |
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row3 = img_fake[i] |
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else: |
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row1 = torch.cat([row1, img_id[i]], dim=2) |
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row2 = torch.cat([row2, img_att[i]], dim=2) |
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row3 = torch.cat([row3, img_fake[i]], dim=2) |
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full = row3.detach() |
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full = full.permute(1, 2, 0) |
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output = full.to('cpu') |
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output = np.array(output) |
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output = output[..., ::-1] |
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output = output*255 |
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cv2.imwrite(opt.output_path + 'result.jpg', output) |
