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.videoswap_specific import video_swap import os def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0 transformer = transforms.Compose([ transforms.ToTensor(), #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) transformer_Arcface = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) # detransformer = transforms.Compose([ # transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]), # transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1]) # ]) if __name__ == '__main__': opt = TestOptions().parse() pic_specific = opt.pic_specific_path start_epoch, epoch_iter = 1, 0 crop_size = 224 torch.nn.Module.dump_patches = True model = create_model(opt) model.eval() app = Face_detect_crop(name='antelope', root='./insightface_func/models') app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640)) pic_a = opt.pic_a_path # img_a = Image.open(pic_a).convert('RGB') 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]) # pic_b = opt.pic_b_path # img_b_whole = cv2.imread(pic_b) # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size) # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB)) # img_b = transformer(img_b_align_crop_pil) # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2]) # convert numpy to tensor img_id = img_id.cuda() # img_att = img_att.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]) specific_person = specific_person.cuda() specific_person_downsample = F.interpolate(specific_person, scale_factor=0.5) specific_person_id_nonorm = model.netArc(specific_person_downsample) video_swap(opt.video_path, latend_id,specific_person_id_nonorm, opt.id_thres, \ model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo)