def main():
# meta imitator
test_opt = TestOptions().parse()
test_opt.bg_ks = 25
test_opt.front_warp = False
test_opt.post_tune = True
load_path = test_opt.load_path
exp_name = load_path.split("/")[-2]
print('-----------------------\n','EXP NAME: ',exp_name,'\n--------------------------')
test_opt.output_dir = mkdir('./outputs/results/demos/imitators/' + exp_name)
# source images from iPER
images_paths = ['./assets/src_imgs/plaid_shirt.jpg']
#'./assets/src_imgs/beachshorts.jpg',
#'./assets/src_imgs/fashion_woman/Sweaters-id_0000088807_4_full.jpg']
for src_img_path in tqdm(images_paths):
generate_actor_result(test_opt, src_img_path)
# clean other files
clean(test_opt.output_dir)
print('Completed! All demo videos are save in {}'.format(test_opt.output_dir))
def setup(opts):
shutil.move(opts['checkpoint_dir'], 'outputs/checkpoints')
shutil.move(opts['pretrained_renderer_dir'], 'assets/pretrains')
opt = TestOptions().parse()
opt.bg_ks = 13
opt.ft_ks = 3
opt.has_detector = True
opt.front_warp = True
swapper = Swapper(opt=opt)
imitator = Imitator(opt)
return swapper, imitator
def main():
# meta imitator
test_opt = TestOptions().parse()
test_opt.bg_ks = 25
test_opt.front_warp = False
test_opt.post_tune = True
test_opt.output_dir = mkdir('./outputs/results/demos/imitators')
# source images from iPER
images_paths = ['./assets/src_imgs/test.jpg']
for src_img_path in tqdm(images_paths):
generate_actor_result(test_opt, src_img_path)
# clean other files
clean(test_opt.output_dir)
print('Completed! All demo videos are save in {}'.format(
test_opt.output_dir))
def main():
# meta imitator
test_opt = TestOptions().parse()
test_opt.bg_ks = 25
test_opt.front_warp = False
test_opt.post_tune = True
test_opt.output_dir = mkdir('./outputs/results/demos/imitators')
# source images from iPER
images_paths = ['./assets/src_imgs/imper_A_Pose/009_5_1_000.jpg',
'./assets/src_imgs/imper_A_Pose/024_8_2_0000.jpg',
'./assets/src_imgs/fashion_woman/Sweaters-id_0000088807_4_full.jpg']
for src_img_path in tqdm(images_paths):
generate_actor_result(test_opt, src_img_path)
# clean other files
clean(test_opt.output_dir)
print('Completed! All demo videos are save in {}'.format(test_opt.output_dir))
"""
make video
"""
img_path_list = glob.glob("%s/imgs/*.jpg" % save_dir)
output_mp4_path = '%s/%s.mp4' % (save_dir, src_img_true_name)
make_video(output_mp4_path, img_path_list, save_frames_dir=None, fps=30)
clean(opt.output_dir)
clean(save_dir)
if __name__ == "__main__":
opt = TestOptions().parse()
opt.bg_ks = 31
opt.T_pose = False
opt.front_warp = False
opt.bg_replace = True
opt.post_tune = True
opt.output_dir = './outputs/results/demos/viewers'
src_path_list = [
('iPER', './assets/src_imgs/imper_Random_Pose/novel_3.jpg'),
('Fashion', './assets/src_imgs/fashion_woman/fashionWOMENDressesid0000271801_4full.jpg'),
('Fashion', './assets/src_imgs/fashion_man/Jackets_Vests-id_0000071603_4_full.jpg')
]
for dataset, src_path in src_path_list:
if dataset == 'Fashion':
opt.T_pose = True
from utils.visdom_visualizer import VisdomVisualizer
from utils.util import mkdir
def tensor2cv2(img_tensor):
img = (img_tensor[0].cpu().numpy().transpose(1, 2, 0) + 1) / 2
img = img[:, :, ::-1]
img = (img * 255).astype(np.uint8)
return img
if __name__ == "__main__":
opt = TestOptions().parse()
opt.bg_ks = 25
opt.front_warp = True
opt.post_tune = True
src_path_list = [
('iPER', './assets/src_imgs/imper_A_Pose/009_5_1_000.jpg'),
('Fashion',
'./assets/src_imgs/fashion_man/Jackets_Vests-id_0000008408_4_full.jpg'
),
('Fashion',
'./assets/src_imgs/fashion_woman/Sweaters-id_0000088807_4_full.jpg')
]
tgt_path_list = [
'./assets/src_imgs/fashion_woman/fashionWOMENBlouses_Shirtsid0000666802_4full.jpg',
'./assets/src_imgs/fashion_man/Sweatshirts_Hoodies-id_0000680701_4_full.jpg',
