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- 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_mutil import Face_detect_crop
- from util.reverse2original import reverse2wholeimage
- import os
- from util.add_watermark import watermark_image
- import torch.nn as nn
- from util.norm import SpecificNorm
- def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
- transformer_Arcface = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- def _totensor(array):
- tensor = torch.from_numpy(array)
- img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
- return img.float().div(255)
- def _toarctensor(array):
- tensor = torch.from_numpy(array)
- img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
- return img.float().div(255)
- if __name__ == '__main__':
- opt = TestOptions().parse()
- start_epoch, epoch_iter = 1, 0
- crop_size = 224
- torch.nn.Module.dump_patches = True
- logoclass = watermark_image('./simswaplogo/simswaplogo.png')
- model = create_model(opt)
- model.eval()
- mse = torch.nn.MSELoss().cuda()
- spNorm =SpecificNorm()
- app = Face_detect_crop(name='antelope', root='./insightface_func/models')
- app.prepare(ctx_id= 0, det_thresh=0.8, det_size=(640,640))
- pic_a = opt.pic_a_path
- pic_specific = opt.pic_specific_path
- # The person who provides id information
- 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, scale_factor=0.5)
- latend_id = model.netArc(img_id_downsample)
- latend_id = F.normalize(latend_id, p=2, dim=1)
- # The specific person to be swapped
- specific_person_whole = cv2.imread(pic_specific)
- specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
- specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB))
- specific_person = transformer_Arcface(specific_person_align_crop_pil)
- specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
- # convert numpy to tensor
- specific_person = specific_person.cuda()
- #create latent id
- specific_person_downsample = F.interpolate(specific_person, scale_factor=0.5)
- specific_person_id_nonorm = model.netArc(specific_person_downsample)
- # specific_person_id_norm = F.normalize(specific_person_id_nonorm, p=2, dim=1)
- ############## Forward Pass ######################
- pic_b = opt.pic_b_path
- img_b_whole = cv2.imread(pic_b)
- img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
- # detect_results = None
- swap_result_list = []
- id_compare_values = []
- b_align_crop_tenor_list = []
- for b_align_crop in img_b_align_crop_list:
- b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
- b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)
- b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, scale_factor=0.5)
- b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)
- id_compare_values.append(mse(b_align_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
- b_align_crop_tenor_list.append(b_align_crop_tenor)
- id_compare_values_array = np.array(id_compare_values)
- min_index = np.argmin(id_compare_values_array)
- min_value = id_compare_values_array[min_index]
- if min_value < opt.id_thres:
- swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]
- reverse2wholeimage([swap_result], [b_mat_list[min_index]], crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swapspecific.jpg'), opt.no_simswaplogo)
- print(' ')
- print('************ Done ! ************')
- else:
- print('The person you specified is not found on the picture: {}'.format(pic_b))
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