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test_wholeimage_swapsingle.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 19:19:43
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_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
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 run_image_single(img_a, img_b, opt):
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()
spNorm =SpecificNorm()
app = Face_detect_crop(name='antelope', root='./insightface_func/models')
app.prepare(ctx_id= 0, det_thresh=0.1, det_size=(640,640),mode=mode)
with torch.no_grad():
# pic_a = opt.pic_a_path
# img_a_whole = cv2.imread(pic_a)
try:
img_a_align_crop, _ = app.get(img_a,crop_size)
except:
raise Exception('No face detected in image A')
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)
############## Forward Pass ######################
# pic_b = opt.pic_b_path
# img_b_whole = cv2.imread(pic_b)
try:
img_b_align_crop_list, b_mat_list = app.get(img_b,crop_size)
except:
raise Exception('No face detected in image B')
# 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,...].cuda()
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.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
output = reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b, logoclass, \
os.path.join(opt.output_path, 'result_whole_swapsingle.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
# print('************ Done ! ************')
return output
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()
spNorm =SpecificNorm()
app = Face_detect_crop(name='antelope', root='./insightface_func/models')
app.prepare(ctx_id= 0, det_thresh=0.1, det_size=(640,640),mode=mode)
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.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)
############## 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,...].cuda()
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.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(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, \
os.path.join(opt.output_path, 'result_whole_swapsingle.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
print(' ')
print('************ Done ! ************')