模型结构
DetailFeatureExtraction增加了一个增强残差 BaseFeatureExtraction增加了 x = self.WTConv2d(x)
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componets/whaiutil.py
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componets/whaiutil.py
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import os
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from PIL import Image
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def transfer(input_path, quality=20, resize_factor=0.1):
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# 打开TIFF图像
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# img = Image.open(input_path)
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#
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# # 保存为JPEG，并设置压缩质量
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# img.save(output_path, 'JPEG', quality=quality)
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# input_path = os.path.join(input_folder, filename)
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# 获取input_path的文件名
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# 使用os.path.splitext获取文件名和后缀的元组
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# 使用os.path.basename获取文件名（包含后缀）
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filename_with_extension = os.path.basename(input_path)
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filename, file_extension = os.path.splitext(filename_with_extension)
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# 使用os.path.dirname获取文件所在的目录路径
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output_folder = os.path.dirname(input_path)
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output_path = os.path.join(output_folder, filename + '.jpg')
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img = Image.open(input_path)
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# 将图像缩小到原来的一半
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new_width = int(img.width * resize_factor)
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new_height = int(img.height * resize_factor)
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resized_img = img.resize((new_width, new_height))
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# 保存为JPEG，并设置压缩质量
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# 转换为RGB模式，丢弃透明通道
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rgb_img = resized_img.convert('RGB')
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# 保存为JPEG，并设置压缩质量
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# 压缩
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rgb_img.save(output_path, 'JPEG', quality=quality)
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print(f'{output_path} 转换完成')
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return output_path
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logs/20241007_whai.log
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logs/20241007_whai.log
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/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
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================================================================================
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The test result of TNO :
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19.png
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05.png
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21.png
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18.png
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15.png
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22.png
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14.png
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13.png
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08.png
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01.png
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02.png
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03.png
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25.png
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17.png
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11.png
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16.png
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06.png
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07.png
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09.png
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10.png
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12.png
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23.png
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24.png
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20.png
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04.png
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EN SD SF MI SCD VIF Qabf SSIM
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PFCFuse 7.01 40.67 15.39 1.53 1.76 0.64 0.53 0.95
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================================================================================
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@ -13,7 +13,7 @@ logging.basicConfig(level=logging.CRITICAL)
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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ckpt_path= r"/home/star/whaiDir/PFCFuse/models/PFCFusion10-05-20-46.pth"
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ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion10-06-22-17.pth"
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for dataset_name in ["TNO"]:
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print("\n"*2+"="*80)
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201
test_sar.py
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test_sar.py
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import argparse
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import sys
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import uuid
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import cv2
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from PIL import Image
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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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path = os.path.dirname(sys.argv[0]) + "\\"
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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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ckpt_path = r"" + path + "models/CDDFuse_IVF.pth"
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print(torch.cuda.is_available())
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def main(opt):
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# --viPath D:\PythonProject\MMIF-CDDFuse\test_img\Test\vi\ir_2.png --irPath D:\PythonProject\MMIF-CDDFuse\test_img\Test\ir\ir_2.png --outputPath D:\PythonProject\MMIF-CDDFuse\test_img\Test\
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sar_path = transfer(opt.sarPath, 100, 0.15)
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vi_path = transfer(opt.viPath, 100, 0.15)
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output_path = opt.outputPath
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print("\n" * 2 + "=" * 80)
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print("The sar_path of " + sar_path + ' :')
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print("The vi_path of " + vi_path + ' :')
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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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data_IR = image_read_cv2(sar_path, mode='GRAY')[np.newaxis, np.newaxis, ...] / 255.0
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data_VIS = image_read_cv2(vi_path, mode='GRAY')[np.newaxis, np.newaxis, ...] / 255.0
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data_VIS_BGR = cv2.imread(vi_path)
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_, data_VIS_Cr, data_VIS_Cb = cv2.split(cv2.cvtColor(data_VIS_BGR, cv2.COLOR_BGR2YCrCb))
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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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fi = fi.astype(np.uint8)
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ycrcb_fi = np.dstack((fi, data_VIS_Cr, data_VIS_Cb))
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rgb_fi = cv2.cvtColor(ycrcb_fi, cv2.COLOR_YCrCb2RGB)
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# 获取文件名（包含后缀）
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file_name_with_extension = os.path.basename(sar_path)
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# 分离文件名和文件后缀
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file_name, file_extension = os.path.splitext(file_name_with_extension)
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img_save(rgb_fi, "fusionSAR_" + file_name, output_path)
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print("输出文件路径：" + output_path + "fusionSAR_" + file_name + ".jpg")
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# metric_result = np.zeros((8))
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# sarImagePath = sar_path
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# ir = image_read_cv2(sarImagePath, 'GRAY')
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# viImagePath = vi_path
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# vi = image_read_cv2(viImagePath, 'GRAY')
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#
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# fusionImagePath = os.path.join(output_path, "fusionSAR_{}.jpg".format(file_name))
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# fi = image_read_cv2(fusionImagePath, 'GRAY')
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# # 统计
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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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#
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# metric_result /= len(os.listdir(output_path))
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# print("\t\t EN\t SD\t SF\t MI\tSCD\tVIF\tQabf\tSSIM")
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# print("对比结果：" + 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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def transfer(input_path, quality=20, resize_factor=0.1):
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# 打开TIFF图像
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# img = Image.open(input_path)
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#
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# # 保存为JPEG，并设置压缩质量
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# img.save(output_path, 'JPEG', quality=quality)
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# input_path = os.path.join(input_folder, filename)
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# 获取input_path的文件名
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# 使用os.path.splitext获取文件名和后缀的元组
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# 使用os.path.basename获取文件名（包含后缀）
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filename_with_extension = os.path.basename(input_path)
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filename, file_extension = os.path.splitext(filename_with_extension)
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# 使用os.path.dirname获取文件所在的目录路径
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output_folder = os.path.dirname(input_path)
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output_path = os.path.join(output_folder, filename + '.jpg')
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img = Image.open(input_path)
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# 将图像缩小到原来的一半
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new_width = int(img.width * resize_factor)
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new_height = int(img.height * resize_factor)
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resized_img = img.resize((new_width, new_height))
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# 保存为JPEG，并设置压缩质量
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# 转换为RGB模式，丢弃透明通道
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rgb_img = resized_img.convert('RGB')
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# 保存为JPEG，并设置压缩质量
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# 压缩
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rgb_img.save(output_path, 'JPEG', quality=quality)
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print(f'{output_path} 转换完成')
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return output_path
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def parse_opt():
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parser = argparse.ArgumentParser(
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description='python.exe --sarPath "sar绝对路径" --viPath "可见光路径" --outputPath "输出文件路径"')
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parser.add_argument('--sarPath', type=str, default="D:\\PythonProject\\MMIF-CDDFuse\\test_img\\Test\\ir\\NH49E011024.tif", required=True,
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help="是否为多路径") # 这里全部都是使用图片的名字，默认是 项目路径 + 2.jpg
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parser.add_argument('--viPath', type=str, default="D:\\PythonProject\\MMIF-CDDFuse\\test_img\\Test\\vi\\NH49E011024.tif", required=True,
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help="完整目录路径！，可以为数组") # 这里全部都是使用图片的名字，默认是 项目路径 + 2.jpg
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parser.add_argument('--outputPath', type=str, default='results_detect', required=True,
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help="输出路径！") # 使用的也是图片的目标地址
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opt = parser.parse_args()
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return opt
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if __name__ == '__main__':
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print(torch.cuda.is_available())
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opt = parse_opt()
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main(opt)
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def add_prefix_to_files(directory_path, prefix):
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# 使用os.listdir获取目录中的所有文件
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files = os.listdir(directory_path)
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for old_filename in files:
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# 构建新的文件名
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new_filename = f"{prefix}_{old_filename}"
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# 构建旧文件路径和新文件路径
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old_path = os.path.join(directory_path, old_filename)
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new_path = os.path.join(directory_path, new_filename)
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# 使用os.rename进行文件重命名
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os.rename(old_path, new_path)
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print(f'{old_filename} 重命名为 {new_filename}')
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# 替换为实际的目录路径和前缀
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# directory_path = '/path/to/your/directory'
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# new_prefix = 'new'
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#
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# # 执行批量重命名
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# add_prefix_to_files(directory_path, new_prefix)
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16
trainExe.py
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trainExe.py
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import os
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import subprocess
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import datetime
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@ -8,8 +9,15 @@ command = "/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFu
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current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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output_file = f"/home/star/whaiDir/PFCFuse/logs/log_{current_time}.log"
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# 运行命令并将输出重定向到文件
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with open(output_file, 'w') as file:
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subprocess.run(command.split(), stdout=file, stderr=subprocess.STDOUT)
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try:
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# 运行命令并将输出重定向到文件
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with open(output_file, 'w') as file:
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result = subprocess.run(command.split(), stdout=file, stderr=subprocess.STDOUT, check=True)
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print(f"Command output has been written to {output_file}")
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# 如果命令成功执行，则打印确认信息
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print(f"Command executed successfully. Output has been written to {output_file}")
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except subprocess.CalledProcessError as e:
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# 如果命令执行失败，则删除文件并打印错误信息
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if os.path.exists(output_file):
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os.remove(output_file)
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print(f"Command failed with return code {e.returncode}. No log file was created.")
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