whaifree
82acfa83dc
- 新增dataprocessing.py脚本，实现图像数据处理功能，包括文件读取、格式转换、低对比度筛选等 - 新增H5Dataset类，用于加载和访问H5格式的图像数据集 - 在项目中配置远程服务器部署和代码自动上传 - 添加IDE配置文件，包括项目路径、模块管理、代码检查等设置
80 lines
3.8 KiB
Python
80 lines
3.8 KiB
Python
from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
|
|
import os
|
|
import numpy as np
|
|
from utils.Evaluator import Evaluator
|
|
import torch
|
|
import torch.nn as nn
|
|
from utils.img_read_save import img_save,image_read_cv2
|
|
import warnings
|
|
import logging
|
|
warnings.filterwarnings("ignore")
|
|
logging.basicConfig(level=logging.CRITICAL)
|
|
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
|
ckpt_path=r"models/CDDFuse_IVF.pth"
|
|
for dataset_name in ["TNO","RoadScene"]:
|
|
print("\n"*2+"="*80)
|
|
model_name="CDDFuse "
|
|
print("The test result of "+dataset_name+' :')
|
|
test_folder=os.path.join('test_img',dataset_name)
|
|
test_out_folder=os.path.join('test_result',dataset_name)
|
|
|
|
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
|
|
Decoder = nn.DataParallel(Restormer_Decoder()).to(device)
|
|
BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64, num_heads=8)).to(device)
|
|
DetailFuseLayer = nn.DataParallel(DetailFeatureExtraction(num_layers=1)).to(device)
|
|
|
|
Encoder.load_state_dict(torch.load(ckpt_path)['DIDF_Encoder'])
|
|
Decoder.load_state_dict(torch.load(ckpt_path)['DIDF_Decoder'])
|
|
BaseFuseLayer.load_state_dict(torch.load(ckpt_path)['BaseFuseLayer'])
|
|
DetailFuseLayer.load_state_dict(torch.load(ckpt_path)['DetailFuseLayer'])
|
|
Encoder.eval()
|
|
Decoder.eval()
|
|
BaseFuseLayer.eval()
|
|
DetailFuseLayer.eval()
|
|
|
|
with torch.no_grad():
|
|
for img_name in os.listdir(os.path.join(test_folder,"ir")):
|
|
|
|
data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
|
|
data_VIS = image_read_cv2(os.path.join(test_folder,"vi",img_name), mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
|
|
|
|
data_IR,data_VIS = torch.FloatTensor(data_IR),torch.FloatTensor(data_VIS)
|
|
data_VIS, data_IR = data_VIS.cuda(), data_IR.cuda()
|
|
|
|
feature_V_B, feature_V_D, feature_V = Encoder(data_VIS)
|
|
feature_I_B, feature_I_D, feature_I = Encoder(data_IR)
|
|
feature_F_B = BaseFuseLayer(feature_V_B + feature_I_B)
|
|
feature_F_D = DetailFuseLayer(feature_V_D + feature_I_D)
|
|
data_Fuse, _ = Decoder(data_VIS, feature_F_B, feature_F_D)
|
|
data_Fuse=(data_Fuse-torch.min(data_Fuse))/(torch.max(data_Fuse)-torch.min(data_Fuse))
|
|
fi = np.squeeze((data_Fuse * 255).cpu().numpy())
|
|
img_save(fi, img_name.split(sep='.')[0], test_out_folder)
|
|
|
|
|
|
eval_folder=test_out_folder
|
|
ori_img_folder=test_folder
|
|
|
|
metric_result = np.zeros((8))
|
|
for img_name in os.listdir(os.path.join(ori_img_folder,"ir")):
|
|
ir = image_read_cv2(os.path.join(ori_img_folder,"ir", img_name), 'GRAY')
|
|
vi = image_read_cv2(os.path.join(ori_img_folder,"vi", img_name), 'GRAY')
|
|
fi = image_read_cv2(os.path.join(eval_folder, img_name.split('.')[0]+".png"), 'GRAY')
|
|
metric_result += np.array([Evaluator.EN(fi), Evaluator.SD(fi)
|
|
, Evaluator.SF(fi), Evaluator.MI(fi, ir, vi)
|
|
, Evaluator.SCD(fi, ir, vi), Evaluator.VIFF(fi, ir, vi)
|
|
, Evaluator.Qabf(fi, ir, vi), Evaluator.SSIM(fi, ir, vi)])
|
|
|
|
metric_result /= len(os.listdir(eval_folder))
|
|
print("\t\t EN\t SD\t SF\t MI\tSCD\tVIF\tQabf\tSSIM")
|
|
print(model_name+'\t'+str(np.round(metric_result[0], 2))+'\t'
|
|
+str(np.round(metric_result[1], 2))+'\t'
|
|
+str(np.round(metric_result[2], 2))+'\t'
|
|
+str(np.round(metric_result[3], 2))+'\t'
|
|
+str(np.round(metric_result[4], 2))+'\t'
|
|
+str(np.round(metric_result[5], 2))+'\t'
|
|
+str(np.round(metric_result[6], 2))+'\t'
|
|
+str(np.round(metric_result[7], 2))
|
|
)
|
|
print("="*80) |