80 lines
3.8 KiB
Python
80 lines
3.8 KiB
Python
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from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
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import os
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import numpy as np
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from utils.Evaluator import Evaluator
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import torch
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import torch.nn as nn
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from utils.img_read_save import img_save,image_read_cv2
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import warnings
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import logging
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warnings.filterwarnings("ignore")
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logging.basicConfig(level=logging.CRITICAL)
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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ckpt_path=r"models/CDDFuse_IVF.pth"
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for dataset_name in ["TNO","RoadScene"]:
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print("\n"*2+"="*80)
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model_name="CDDFuse "
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print("The test result of "+dataset_name+' :')
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test_folder=os.path.join('test_img',dataset_name)
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test_out_folder=os.path.join('test_result',dataset_name)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
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Decoder = nn.DataParallel(Restormer_Decoder()).to(device)
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BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64, num_heads=8)).to(device)
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DetailFuseLayer = nn.DataParallel(DetailFeatureExtraction(num_layers=1)).to(device)
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Encoder.load_state_dict(torch.load(ckpt_path)['DIDF_Encoder'])
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Decoder.load_state_dict(torch.load(ckpt_path)['DIDF_Decoder'])
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BaseFuseLayer.load_state_dict(torch.load(ckpt_path)['BaseFuseLayer'])
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DetailFuseLayer.load_state_dict(torch.load(ckpt_path)['DetailFuseLayer'])
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Encoder.eval()
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Decoder.eval()
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BaseFuseLayer.eval()
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DetailFuseLayer.eval()
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with torch.no_grad():
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for img_name in os.listdir(os.path.join(test_folder,"ir")):
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data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
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data_VIS = image_read_cv2(os.path.join(test_folder,"vi",img_name), mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
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data_IR,data_VIS = torch.FloatTensor(data_IR),torch.FloatTensor(data_VIS)
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data_VIS, data_IR = data_VIS.cuda(), data_IR.cuda()
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feature_V_B, feature_V_D, feature_V = Encoder(data_VIS)
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feature_I_B, feature_I_D, feature_I = Encoder(data_IR)
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feature_F_B = BaseFuseLayer(feature_V_B + feature_I_B)
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feature_F_D = DetailFuseLayer(feature_V_D + feature_I_D)
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data_Fuse, _ = Decoder(data_VIS, feature_F_B, feature_F_D)
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data_Fuse=(data_Fuse-torch.min(data_Fuse))/(torch.max(data_Fuse)-torch.min(data_Fuse))
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fi = np.squeeze((data_Fuse * 255).cpu().numpy())
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img_save(fi, img_name.split(sep='.')[0], test_out_folder)
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eval_folder=test_out_folder
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ori_img_folder=test_folder
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metric_result = np.zeros((8))
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for img_name in os.listdir(os.path.join(ori_img_folder,"ir")):
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ir = image_read_cv2(os.path.join(ori_img_folder,"ir", img_name), 'GRAY')
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vi = image_read_cv2(os.path.join(ori_img_folder,"vi", img_name), 'GRAY')
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fi = image_read_cv2(os.path.join(eval_folder, img_name.split('.')[0]+".png"), 'GRAY')
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metric_result += np.array([Evaluator.EN(fi), Evaluator.SD(fi)
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, Evaluator.SF(fi), Evaluator.MI(fi, ir, vi)
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, Evaluator.SCD(fi, ir, vi), Evaluator.VIFF(fi, ir, vi)
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, Evaluator.Qabf(fi, ir, vi), Evaluator.SSIM(fi, ir, vi)])
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metric_result /= len(os.listdir(eval_folder))
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print("\t\t EN\t SD\t SF\t MI\tSCD\tVIF\tQabf\tSSIM")
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print(model_name+'\t'+str(np.round(metric_result[0], 2))+'\t'
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+str(np.round(metric_result[1], 2))+'\t'
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+str(np.round(metric_result[2], 2))+'\t'
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+str(np.round(metric_result[3], 2))+'\t'
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+str(np.round(metric_result[4], 2))+'\t'
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+str(np.round(metric_result[5], 2))+'\t'
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+str(np.round(metric_result[6], 2))+'\t'
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+str(np.round(metric_result[7], 2))
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)
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print("="*80)
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