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test_wholeimage_swap_multispecific.py
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test_wholeimage_swap_multispecific.py
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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_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))