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test_wholeimage_swapspecific.py
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test_wholeimage_swapspecific.py
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'''
Author: Naiyuan liu
Github: https://github.com/NNNNAI
Date: 2021-11-23 17:03:58
LastEditors: Naiyuan liu
LastEditTime: 2021-11-24 19:19:47
Description:
'''
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
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 = opt.crop_size
torch.nn.Module.dump_patches = True
if crop_size == 512:
opt.which_epoch = 550000
opt.name = '512'
mode = 'ffhq'
else:
mode = 'None'
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),mode=mode)
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, size=(112,112))
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, size=(112,112))
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, size=(112,112))
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 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
if min_value < opt.id_thres:
swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]
reverse2wholeimage([b_align_crop_tenor_list[min_index]], [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,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
print(' ')
print('************ Done ! ************')
else:
print('The person you specified is not found on the picture: {}'.format(pic_b))