chenxuanhong
225 lines
9.6 KiB
Python
225 lines
9.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: tester_commonn.py
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# Created Date: Saturday July 3rd 2021
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Friday, 18th February 2022 5:00:28 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2021 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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import torch
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import torch.nn.functional as F
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from torchvision import transforms
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import numpy as np
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from PIL import Image
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from insightface_func.face_detect_crop_single import Face_detect_crop
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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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crop_mode = self.config["crop_mode"]
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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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# models
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self.__init_framework__()
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mode = crop_mode.lower()
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if mode == "vggface":
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mode = "none"
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self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models')
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self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode)
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id_img = cv2.imread(id_imgs)
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id_img_align_crop, _ = self.detect.get(id_img,512)
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id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],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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cos_loss = torch.nn.CosineSimilarity()
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font = cv2.FONT_HERSHEY_SIMPLEX
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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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cos_dict = {}
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average_cos = 0
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with torch.no_grad():
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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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try:
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attr_img_align_crop, mat = self.detect.get(attr_img_ori,512)
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except:
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continue
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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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cos_dis = 1 - cos_loss(latend_id, attr_id)
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mat = mat[0]
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results = self.network(attr_img, latend_id)
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results_arc = F.interpolate(results,size=(112,112), mode='bicubic')
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results_arc = self.arcface(results_arc)
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results_arc = F.normalize(results_arc, p=2, dim=1)
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results_cos_dis = 1 - cos_loss(latend_id, results_arc)
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average_cos += results_cos_dis
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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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img_white = np.full((512,512), 255, dtype=float)
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# inverse the Affine transformation matrix
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mat_rev = np.zeros([2,3])
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div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
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mat_rev[0][0] = mat[1][1]/div1
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mat_rev[0][1] = -mat[0][1]/div1
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mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
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div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
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mat_rev[1][0] = mat[1][0]/div2
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mat_rev[1][1] = -mat[0][0]/div2
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mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
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orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0])
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target_image = cv2.warpAffine(results, mat_rev, orisize)
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img_white = cv2.warpAffine(img_white, mat_rev, orisize)
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img_white[img_white>20] =255
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img_mask = img_white
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kernel = np.ones((40,40),np.uint8)
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img_mask = cv2.erode(img_mask,kernel,iterations = 1)
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kernel_size = (20, 20)
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blur_size = tuple(2*i+1 for i in kernel_size)
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img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
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img_mask /= 255
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img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
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target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255
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img1 = np.array(attr_img_ori, dtype=np.float)
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img1 = img_mask * target_image + (1-img_mask) * img1
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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())) |