import os #os.environ['CUDA_VISIBLE_DEVICES'] = "0" from models.psp import pSp import torch import dlib import cv2 import PIL import argparse from tqdm import tqdm import numpy as np import torch.nn.functional as F import torchvision from torchvision import transforms, utils from argparse import Namespace from datasets import augmentations from scripts.align_all_parallel import align_face from latent_optimization import latent_optimization from utils.inference_utils import save_image, load_image, visualize, get_video_crop_parameter, tensor2cv2, tensor2label, labelcolormap class TestOptions(): def __init__(self): self.parser = argparse.ArgumentParser(description="StyleGANEX Image Translation") self.parser.add_argument("--data_path", type=str, default='./data/ILip77SbmOE.png', help="path of the target image") self.parser.add_argument("--ckpt", type=str, default='pretrained_models/styleganex_sr32.pt', help="path of the saved model") self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output images") self.parser.add_argument("--cpu", action="store_true", help="if true, only use cpu") self.parser.add_argument("--use_raw_data", action="store_true", help="if true, input image needs no pre-procssing") self.parser.add_argument("--resize_factor", type=int, default=32, help="super resolution resize factor") self.parser.add_argument("--number", type=int, default=4, help="output number of multi-modal translation") self.parser.add_argument("--parsing_model_ckpt", type=str, default='pretrained_models/faceparsing.pth', help="path of the parsing model") def parse(self): self.opt = self.parser.parse_args() args = vars(self.opt) print('Load options') for name, value in sorted(args.items()): print('%s: %s' % (str(name), str(value))) return self.opt if __name__ == "__main__": parser = TestOptions() args = parser.parse() print('*'*98) device = "cpu" if args.cpu else "cuda" transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), ]) ckpt = torch.load(args.ckpt, map_location='cpu') opts = ckpt['opts'] opts['checkpoint_path'] = args.ckpt opts['device'] = device opts = Namespace(**opts) pspex = pSp(opts).to(device).eval() pspex.latent_avg = pspex.latent_avg.to(device) image_path = args.data_path save_name = '%s/%s_%s'%(args.output_path, os.path.basename(image_path).split('.')[0], os.path.basename(args.ckpt).split('.')[0]) modelname = 'pretrained_models/shape_predictor_68_face_landmarks.dat' if not os.path.exists(modelname): import wget, bz2 wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2') zipfile = bz2.BZ2File(modelname+'.bz2') data = zipfile.read() open(modelname, 'wb').write(data) landmarkpredictor = dlib.shape_predictor(modelname) if opts.dataset_type == 'ffhq_seg_to_face' and not args.use_raw_data: from models.bisenet.model import BiSeNet maskpredictor = BiSeNet(n_classes=19) maskpredictor.load_state_dict(torch.load(args.parsing_model_ckpt)) maskpredictor.to(device).eval() to_tensor = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ]) if opts.dataset_type == 'ffhq_super_resolution': frame = cv2.imread(image_path) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if args.use_raw_data: x, y = frame.shape[0:2] tmp = PIL.Image.fromarray(np.uint8(frame)).resize((int(y) * args.resize_factor // 4, int(x) * args.resize_factor // 4)) frame = np.array(tmp) paras = get_video_crop_parameter(frame, landmarkpredictor) assert paras is not None, 'StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ You can try several times or use other videos until a face is detected, \ then switch back to the original video.' h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] if not args.use_raw_data: x1 = PIL.Image.fromarray(np.uint8(frame)) x1 = augmentations.BilinearResize(factors=[args.resize_factor // 4])(x1) x1.save(save_name + '_input.png') x1_up = x1.resize((W, H)) x2_up = align_face(np.array(x1_up), landmarkpredictor) x1_up = transforms.ToTensor()(x1_up).unsqueeze(dim=0).to(device) * 2 - 1 else: x1_up = transform(frame).unsqueeze(0).to(device) x2_up = align_face(frame, landmarkpredictor) x2_up = transform(x2_up).unsqueeze(dim=0).to(device) x1 = x1_up x2 = x2_up elif opts.dataset_type == 'ffhq_sketch_to_face': # no pre-processing supported, only accept one-channel sketch image x1 = transforms.ToTensor()(PIL.Image.open(image_path)).unsqueeze(0).to(device) x2 = None elif opts.dataset_type == 'ffhq_seg_to_face': if not args.use_raw_data: frame = cv2.imread(image_path) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) paras = get_video_crop_parameter(frame, landmarkpredictor) assert paras is not None, 'StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ You can try several times or use other videos until a face is detected, \ then switch back to the original video.' h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] # convert face image to segmentation mask x1 = to_tensor(frame).unsqueeze(0).to(device) # upsample image for precise segmentation x1 = F.interpolate(x1, scale_factor=2, mode='bilinear') x1 = maskpredictor(x1)[0] x1 = F.interpolate(x1, scale_factor=0.5).argmax(dim=1) cv2.imwrite(save_name+'_input.png', x1.squeeze(0).cpu().numpy()) x1 = F.one_hot(x1, num_classes=19).permute(0, 3, 1, 2).float().to(device) else: x1 = PIL.Image.open(image_path) x1 = augmentations.ToOneHot(opts.label_nc)(x1) x1 = transforms.ToTensor()(x1).unsqueeze(dim=0).float().to(device) x1_viz = transform(tensor2label(x1[0], 19)/192) save_image(x1_viz, save_name+'_input_viz.jpg') x2 = None else: assert False, 'The input model %s does not support image translation task'%(args.ckpt) print('Load models successfully!') with torch.no_grad(): if opts.dataset_type == 'ffhq_super_resolution': y_hat = torch.clamp(pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, resize=False), -1, 1) save_image(y_hat[0].cpu(), save_name+'.jpg') else: pspex.train() for i in range(args.number): y_hat = pspex(x1=x1, x2=x2, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=pspex.opts.use_skip, inject_latent = pspex.decoder.style(torch.randn(1, 512).to(device)).unsqueeze(1).repeat(1,18,1) * 0.7) y_hat = torch.clamp(y_hat, -1, 1) save_image(y_hat[0].cpu(), save_name+'_%d.jpg'%(i)) pspex.eval() print('Image translation successfully!')