test_wholeimage_swapsingle.py

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_single import Face_detect_crop
from util.reverse2original import reverse2wholeimage
import os
from util.add_watermark import watermark_image
from util.norm import SpecificNorm
from parsing_model.model import BiSeNet

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

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)
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()

    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():
        pic_a = opt.pic_a_path

        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.to(device)

        #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)


        ############## 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 = []

        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,...].to(device)

            swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
            swap_result_list.append(swap_result)
            b_align_crop_tenor_list.append(b_align_crop_tenor)

        if opt.use_mask:
            n_classes = 19
            net = BiSeNet(n_classes=n_classes)
            net.to(device)
            save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
            net.load_state_dict(torch.load(save_pth, map_location=device))
            net.eval()
        else:
            net =None

        reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, \
            os.path.join(opt.output_path), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)

        print(' ')

        print('************ Done ! ************')