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