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import paddle
import argparse
import cv2
import numpy as np
import os
from models.model import FaceSwap, l2_norm
from models.arcface import IRBlock, ResNet
from utils.align_face import back_matrix, dealign, align_img
from utils.util import paddle2cv, cv2paddle
from utils.prepare_data import LandmarkModel
def get_id_emb(id_net, id_img_path):
id_img = cv2.imread(id_img_path)
id_img = cv2.resize(id_img, (112, 112))
id_img = cv2paddle(id_img)
mean = paddle.to_tensor([[0.485, 0.456, 0.406]]).reshape((1, 3, 1, 1))
std = paddle.to_tensor([[0.229, 0.224, 0.225]]).reshape((1, 3, 1, 1))
id_img = (id_img - mean) / std
id_emb, id_feature = id_net(id_img)
id_emb = l2_norm(id_emb)
return id_emb, id_feature
def image_test(args):
paddle.set_device("gpu" if args.use_gpu else 'cpu')
faceswap_model = FaceSwap(args.use_gpu)
id_net = ResNet(block=IRBlock, layers=[3, 4, 23, 3])
id_net.set_dict(paddle.load('./checkpoints/arcface.pdparams'))
id_net.eval()
weight = paddle.load('./checkpoints/MobileFaceSwap_224.pdparams')
base_path = args.source_img_path.replace('.png', '').replace('.jpg', '').replace('.jpeg', '')
id_emb, id_feature = get_id_emb(id_net, base_path + '_aligned.png')
faceswap_model.set_model_param(id_emb, id_feature, model_weight=weight)
faceswap_model.eval()
if os.path.isfile(args.target_img_path):
img_list = [args.target_img_path]
else:
img_list = [os.path.join(args.target_img_path, x) for x in os.listdir(args.target_img_path) if x.endswith('png') or x.endswith('jpg') or x.endswith('jpeg')]
for img_path in img_list:
origin_att_img = cv2.imread(img_path)
base_path = img_path.replace('.png', '').replace('.jpg', '').replace('.jpeg', '')
att_img = cv2.imread(base_path + '_aligned.png')
att_img = cv2paddle(att_img)
import time
res, mask = faceswap_model(att_img)
res = paddle2cv(res)
if args.merge_result:
back_matrix = np.load(base_path + '_back.npy')
mask = np.transpose(mask[0].numpy(), (1, 2, 0))
res = dealign(res, origin_att_img, back_matrix, mask)
cv2.imwrite(os.path.join(args.output_dir, os.path.basename(img_path)), res)
def face_align(landmarkModel, image_path, merge_result=False, image_size=224):
if os.path.isfile(image_path):
img_list = [image_path]
else:
img_list = [os.path.join(image_path, x) for x in os.listdir(image_path) if x.endswith('png') or x.endswith('jpg') or x.endswith('jpeg')]
for path in img_list:
img = cv2.imread(path)
landmark = landmarkModel.get(img)
if landmark is not None:
base_path = path.replace('.png', '').replace('.jpg', '').replace('.jpeg', '')
aligned_img, back_matrix = align_img(img, landmark, image_size)
# np.save(base_path + '.npy', landmark)
cv2.imwrite(base_path + '_aligned.png', aligned_img)
if merge_result:
np.save(base_path + '_back.npy', back_matrix)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="MobileFaceSwap Test")
parser.add_argument('--source_img_path', type=str, help='path to the source image')
parser.add_argument('--target_img_path', type=str, help='path to the target images')
parser.add_argument('--output_dir', type=str, default='results', help='path to the output dirs')
parser.add_argument('--image_size', type=int, default=224,help='size of the test images (224 SimSwap | 256 FaceShifter)')
parser.add_argument('--merge_result', type=bool, default=True, help='output with whole image')
parser.add_argument('--need_align', type=bool, default=True, help='need to align the image')
parser.add_argument('--use_gpu', type=bool, default=False)
args = parser.parse_args()
if args.need_align:
landmarkModel = LandmarkModel(name='landmarks')
landmarkModel.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))
face_align(landmarkModel, args.source_img_path)
face_align(landmarkModel, args.target_img_path, args.merge_result, args.image_size)
os.makedirs(args.output_dir, exist_ok=True)
image_test(args)
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