#!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: tester_ID_Pose.py # Created Date: Friday March 4th 2022 # Author: Liu Naiyuan # Email: chenxuanhongzju@outlook.com # Last Modified: Friday, 4th March 2022 5:33:47 pm # Modified By: Chen Xuanhong # Copyright (c) 2022 Shanghai Jiao Tong University ############################################################# import os import cv2 import time import glob from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F from torchvision import transforms from torch.utils import data import numpy as np import PIL from PIL import Image class TotalDataset(data.Dataset): """Dataset class for the vggface dataset with precalulated face landmarks.""" def __init__(self,image_dir,content_transform, img_size=224): self.image_dir= image_dir self.content_transform= content_transform self.img_size = img_size self.dataset = [] self.preprocess() self.num_images = len(self.dataset) def preprocess(self): """Preprocess the Face++ original frames.""" filenames = sorted(glob.glob(os.path.join(self.image_dir, '*'), recursive=False)) # self.total_num = len(lines) for filename in filenames: self.dataset.append(filename) print('Finished preprocessing the Face++ original frames dataset...') def __getitem__(self, index): """Return two src domain images and two dst domain images.""" src_filename = self.dataset[index] split_tmp = src_filename.split('/') save_filename = split_tmp[-1] src_image1 = self.content_transform(Image.open(src_filename)) return src_image1, save_filename def __len__(self): """Return the number of images.""" return len(self.dataset) def getLoader_sourceface(c_image_dir, img_size=224, batch_size=16, num_workers=8): """Build and return a data loader.""" c_transforms = [] c_transforms.append(T.ToTensor()) c_transforms.append(T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) # c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) c_transforms = T.Compose(c_transforms) content_dataset = TotalDataset(c_image_dir, c_transforms, 224) content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True) return content_data_loader, len(content_dataset) def getLoader_targetface(c_image_dir, img_size=224, batch_size=16, num_workers=8): """Build and return a data loader.""" c_transforms = [] c_transforms.append(transforms.ToTensor()) # c_transforms.append(T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) # c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) c_transforms = transforms.Compose(c_transforms) content_dataset = TotalDataset(c_image_dir, c_transforms, 224) content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True) return content_data_loader, len(content_dataset) class Tester(object): def __init__(self, config, reporter): self.config = config # logger self.reporter = reporter self.transformer_Arcface = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) def __init_framework__(self): ''' This function is designed to define the framework, and print the framework information into the log file ''' #===============build models================# print("build models...") # TODO [import models here] model_config = self.config["model_configs"] gscript_name = self.config["com_base"] + model_config["g_model"]["script"] class_name = model_config["g_model"]["class_name"] package = __import__(gscript_name, fromlist=True) gen_class = getattr(package, class_name) self.network = gen_class(**model_config["g_model"]["module_params"]) # TODO replace below lines to define the model framework self.network = gen_class(**model_config["g_model"]["module_params"]) self.network = self.network.eval() # print and recorde model structure self.reporter.writeInfo("Model structure:") self.reporter.writeModel(self.network.__str__()) arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) self.arcface = arcface1['model'].module self.arcface.eval() self.arcface.requires_grad_(False) model_path = os.path.join(self.config["project_checkpoints"], "step%d_%s.pth"%(self.config["checkpoint_step"], self.config["checkpoint_names"]["generator_name"])) self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) # train in GPU if self.config["cuda"] >=0: self.network = self.network.cuda() self.arcface = self.arcface.cuda() def test(self): save_dir = self.config["test_samples_path"] ckp_step = self.config["checkpoint_step"] version = self.config["version"] id_imgs = self.config["id_imgs"] attr_files = self.config["attr_files"] specified_save_path = self.config["specified_save_path"] self.arcface_ckpt= self.config["arcface_ckpt"] imgs_list = [] self.reporter.writeInfo("Version %s"%version) if os.path.isdir(specified_save_path): print("Input a legal specified save path!") save_dir = specified_save_path if os.path.isdir(attr_files): print("Input a dir....") imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) for item in imgs: imgs_list.append(item) print(imgs_list) else: print("Input an image....") imgs_list.append(attr_files) id_basename = os.path.basename(id_imgs) id_basename = os.path.splitext(os.path.basename(id_imgs))[0] source_loader, dataet_len = getLoader_sourceface( self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=opt.batchSize) target_loader, dataet_len = getLoader_targetface( self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=opt.batchSize) source_iter = iter(source_loader) target_iter = iter(target_loader) # models self.__init_framework__() id_img = cv2.imread(id_imgs) id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) id_img = self.transformer_Arcface(id_img_align_crop_pil) id_img = id_img.unsqueeze(0).cuda() #create latent id id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') latend_id = self.arcface(id_img) latend_id = F.normalize(latend_id, p=2, dim=1) # Start time import datetime print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) print('Start =================================== test...') start_time = time.time() self.network.eval() with torch.no_grad(): for profile_batch, filename_batch in tqdm(source_iter): profile_batch = profile_batch.cuda() profile_id_downsample = F.interpolate(profile_batch, (112,112), mode='bicubic') profile_latent_id = model.netArc(profile_id_downsample) profile_latent_id = F.normalize(profile_latent_id, p=2, dim=1) if init_batch ==True: wholeid_batch = profile_latent_id.cpu() init_batch = False else: wholeid_batch = torch.cat([wholeid_batch,profile_latent_id.cpu()],dim=0) target_source_pair_dict = np.load( self.config["env_config"]["dataset_paths"]["pairs_dict"] ,allow_pickle=True).item() for target_batch, filename_batch in tqdm(target_iter): target_index_list = [] init_id_batch = True for filename_tmp in filename_batch: source_index = int(filename_tmp.split('_')[0]) target_index = target_source_pair_dict[source_index] target_index_list.append(target_index) if init_id_batch: batch_id = wholeid_batch[target_index][None].cuda() init_id_batch = False else: batch_id = torch.cat([batch_id, wholeid_batch[target_index][None].cuda()],dim = 0) img_fakes = model(None, target_batch.cuda(), batch_id, None, True) for img_fake, target_index_tmp,filename_tmp in zip(img_fakes, target_index_list,filename_batch): filename_tmp_split = filename_tmp.split('_') final_filename = filename_tmp_split[0] + '_' +str(target_index_tmp) + '_' + filename_tmp_split[-1] save_path = os.path.join(simswap_eval_save_image_path,final_filename) save_image = postprocess(img_fake.cpu().numpy().transpose(1,2,0)) PIL.Image.fromarray(save_image).save(save_path,quality=95) for img in imgs_list: print(img) attr_img_ori= cv2.imread(img) attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) attr_id = self.arcface(attr_img_arc) attr_id = F.normalize(attr_id, p=2, dim=1) results = self.network(attr_img, latend_id) results = results * self.imagenet_std + self.imagenet_mean results = results.cpu().permute(0,2,3,1)[0,...] results = results.numpy() results = np.clip(results,0.0,1.0) final_img = img1.astype(np.uint8) attr_basename = os.path.splitext(os.path.basename(img))[0] final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) save_filename = os.path.join(save_dir, "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, attr_basename,ckp_step,version)) cv2.imwrite(save_filename, final_img) average_cos /= len(imgs_list) elapsed = time.time() - start_time elapsed = str(datetime.timedelta(seconds=elapsed)) print("Elapsed [{}]".format(elapsed)) print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) if __name__ == '__main__': opt = TestOptions().parse() with torch.no_grad(): source_loader, dataet_len = getLoader_sourceface('/home/gdp/harddisk/Data2/Faceswap/FaceForensics++_image_hififacestyle_source_Nonearcstyle', batch_size=opt.batchSize) target_loader, dataet_len = getLoader_targetface('/home/gdp/harddisk/Data2/Faceswap/FaceForensics++_image_target_even10_pro_withmat_Nonearcstyle_256', batch_size=opt.batchSize) simswap_eval_save_image_path = opt.output_path criterion = nn.L1Loss() if not os.path.exists(simswap_eval_save_image_path): os.makedirs(simswap_eval_save_image_path) torch.nn.Module.dump_patches = True model = create_model(opt) model.eval() source_iter = iter(source_loader) target_iter = iter(target_loader) init_batch = True for profile_batch, filename_batch in tqdm(source_iter): # src_batch, filename_batch = data_iter.next() profile_batch = profile_batch.cuda() profile_id_downsample = F.interpolate(profile_batch, (112,112)) profile_latent_id = model.netArc(profile_id_downsample) profile_latent_id = F.normalize(profile_latent_id, p=2, dim=1) if init_batch ==True: wholeid_batch = profile_latent_id.cpu() init_batch = False else: wholeid_batch = torch.cat([wholeid_batch,profile_latent_id.cpu()],dim=0) print(wholeid_batch.shape) # np.save("simswap_wholeid_batch.npy", wholeid_batch.detach().cpu().numpy()) target_source_pair_dict = np.load('/home/gdp/harddisk/Data2/Faceswap/npy_file/target_source_pair.npy' ,allow_pickle=True).item() for target_batch, filename_batch in tqdm(target_iter): target_index_list = [] init_id_batch = True for filename_tmp in filename_batch: source_index = int(filename_tmp.split('_')[0]) target_index = target_source_pair_dict[source_index] target_index_list.append(target_index) if init_id_batch: batch_id = wholeid_batch[target_index][None].cuda() init_id_batch = False else: batch_id = torch.cat([batch_id, wholeid_batch[target_index][None].cuda()],dim = 0) img_fakes = model(None, target_batch.cuda(), batch_id, None, True) for img_fake, target_index_tmp,filename_tmp in zip(img_fakes, target_index_list,filename_batch): filename_tmp_split = filename_tmp.split('_') final_filename = filename_tmp_split[0] + '_' +str(target_index_tmp) + '_' + filename_tmp_split[-1] save_path = os.path.join(simswap_eval_save_image_path,final_filename) save_image = postprocess(img_fake.cpu().numpy().transpose(1,2,0)) PIL.Image.fromarray(save_image).save(save_path,quality=95)