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.reverse2original import reverse2wholeimage import os from util.add_watermark import watermark_image import torch.nn as nn from util.norm import SpecificNorm import glob from parsing_model.model import BiSeNet 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 multisepcific_dir = opt.multisepcific_dir 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=0.6, det_size=(640,640)) with torch.no_grad(): # The specific person to be swapped(source) source_specific_id_nonorm_list = [] source_path = os.path.join(multisepcific_dir,'SRC_*') source_specific_images_path = sorted(glob.glob(source_path)) for source_specific_image_path in source_specific_images_path: specific_person_whole = cv2.imread(source_specific_image_path) 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) source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone()) # The person who provides id information (list) target_id_norm_list = [] target_path = os.path.join(multisepcific_dir,'DST_*') target_images_path = sorted(glob.glob(target_path)) for target_image_path in target_images_path: img_a_whole = cv2.imread(target_image_path) 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) target_id_norm_list.append(latend_id.clone()) assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), "The number of images in source and target directory must be same !!!" ############## 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([]) for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list: id_compare_values[-1].append(mse(b_align_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy()) b_align_crop_tenor_list.append(b_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] < opt.id_thres: swap_result = model(None, b_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(b_mat_list[tmp_index]) swap_result_ori_pic_list.append(b_align_crop_tenor_list[tmp_index]) else: pass if len(swap_result_list) !=0: if opt.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 reverse2wholeimage(swap_result_ori_pic_list, swap_result_list, swap_result_matrix_list, crop_size, img_b_whole, logoclass,\ os.path.join(opt.output_path, 'result_whole_swap_multispecific.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm) print(' ') print('************ Done ! ************') else: print('The people you specified are not found on the picture: {}'.format(pic_b))