Compare commits
No commits in common. "Add20241007" and "main" have entirely different histories.
Add2024100
...
main
@ -1,42 +0,0 @@
|
||||
import os
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def transfer(input_path, quality=20, resize_factor=0.1):
|
||||
# 打开TIFF图像
|
||||
# img = Image.open(input_path)
|
||||
#
|
||||
# # 保存为JPEG，并设置压缩质量
|
||||
# img.save(output_path, 'JPEG', quality=quality)
|
||||
|
||||
# input_path = os.path.join(input_folder, filename)
|
||||
# 获取input_path的文件名
|
||||
|
||||
# 使用os.path.splitext获取文件名和后缀的元组
|
||||
# 使用os.path.basename获取文件名（包含后缀）
|
||||
filename_with_extension = os.path.basename(input_path)
|
||||
filename, file_extension = os.path.splitext(filename_with_extension)
|
||||
|
||||
# 使用os.path.dirname获取文件所在的目录路径
|
||||
output_folder = os.path.dirname(input_path)
|
||||
|
||||
output_path = os.path.join(output_folder, filename + '.jpg')
|
||||
|
||||
img = Image.open(input_path)
|
||||
|
||||
# 将图像缩小到原来的一半
|
||||
new_width = int(img.width * resize_factor)
|
||||
new_height = int(img.height * resize_factor)
|
||||
resized_img = img.resize((new_width, new_height))
|
||||
|
||||
# 保存为JPEG，并设置压缩质量
|
||||
# 转换为RGB模式，丢弃透明通道
|
||||
rgb_img = resized_img.convert('RGB')
|
||||
|
||||
# 保存为JPEG，并设置压缩质量
|
||||
# 压缩
|
||||
rgb_img.save(output_path, 'JPEG', quality=quality)
|
||||
|
||||
print(f'{output_path} 转换完成')
|
||||
|
||||
return output_path
|
||||
@ -1,33 +0,0 @@
|
||||
/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
|
||||
|
||||
|
||||
================================================================================
|
||||
The test result of TNO :
|
||||
19.png
|
||||
05.png
|
||||
21.png
|
||||
18.png
|
||||
15.png
|
||||
22.png
|
||||
14.png
|
||||
13.png
|
||||
08.png
|
||||
01.png
|
||||
02.png
|
||||
03.png
|
||||
25.png
|
||||
17.png
|
||||
11.png
|
||||
16.png
|
||||
06.png
|
||||
07.png
|
||||
09.png
|
||||
10.png
|
||||
12.png
|
||||
23.png
|
||||
24.png
|
||||
20.png
|
||||
04.png
|
||||
EN SD SF MI SCD VIF Qabf SSIM
|
||||
PFCFuse 7.01 40.67 15.39 1.53 1.76 0.64 0.53 0.95
|
||||
================================================================================
|
||||
6
net.py
6
net.py
@ -248,7 +248,11 @@ class DetailFeatureExtraction(nn.Module):
|
||||
super(DetailFeatureExtraction, self).__init__()
|
||||
INNmodules = [DetailNode() for _ in range(num_layers)]
|
||||
self.net = nn.Sequential(*INNmodules)
|
||||
self.enhancement_module = WTConv2d(32, 32)
|
||||
self.enhancement_module = nn.Sequential(
|
||||
nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
|
||||
)
|
||||
|
||||
def forward(self, x): # 1 64 128 128
|
||||
z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
|
||||
|
||||
@ -13,7 +13,7 @@ logging.basicConfig(level=logging.CRITICAL)
|
||||
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
||||
ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion10-06-22-17.pth"
|
||||
ckpt_path= r"/home/star/whaiDir/PFCFuse/models/PFCFusion10-05-20-46.pth"
|
||||
|
||||
for dataset_name in ["TNO"]:
|
||||
print("\n"*2+"="*80)
|
||||
|
||||
201
test_sar.py
201
test_sar.py
@ -1,201 +0,0 @@
|
||||
import argparse
|
||||
import sys
|
||||
import uuid
|
||||
|
||||
import cv2
|
||||
from PIL import Image
|
||||
|
||||
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)
|
||||
|
||||
path = os.path.dirname(sys.argv[0]) + "\\"
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
||||
# ckpt_path=r"models/CDDFuse_IVF.pth"
|
||||
ckpt_path = r"" + path + "models/CDDFuse_IVF.pth"
|
||||
|
||||
print(torch.cuda.is_available())
|
||||
|
||||
|
||||
def main(opt):
|
||||
# --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\
|
||||
|
||||
sar_path = transfer(opt.sarPath, 100, 0.15)
|
||||
|
||||
vi_path = transfer(opt.viPath, 100, 0.15)
|
||||
|
||||
output_path = opt.outputPath
|
||||
|
||||
print("\n" * 2 + "=" * 80)
|
||||
|
||||
print("The sar_path of " + sar_path + ' :')
|
||||
print("The vi_path of " + vi_path + ' :')
|
||||
|
||||
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():
|
||||
data_IR = image_read_cv2(sar_path, mode='GRAY')[np.newaxis, np.newaxis, ...] / 255.0
|
||||
data_VIS = image_read_cv2(vi_path, mode='GRAY')[np.newaxis, np.newaxis, ...] / 255.0
|
||||
data_VIS_BGR = cv2.imread(vi_path)
|
||||
_, data_VIS_Cr, data_VIS_Cb = cv2.split(cv2.cvtColor(data_VIS_BGR, cv2.COLOR_BGR2YCrCb))
|
||||
|
||||
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())
|
||||
fi = fi.astype(np.uint8)
|
||||
ycrcb_fi = np.dstack((fi, data_VIS_Cr, data_VIS_Cb))
|
||||
rgb_fi = cv2.cvtColor(ycrcb_fi, cv2.COLOR_YCrCb2RGB)
|
||||
|
||||
# 获取文件名（包含后缀）
|
||||
file_name_with_extension = os.path.basename(sar_path)
|
||||
# 分离文件名和文件后缀
|
||||
file_name, file_extension = os.path.splitext(file_name_with_extension)
|
||||
|
||||
|
||||
img_save(rgb_fi, "fusionSAR_" + file_name, output_path)
|
||||
print("输出文件路径：" + output_path + "fusionSAR_" + file_name + ".jpg")
|
||||
|
||||
# metric_result = np.zeros((8))
|
||||
# sarImagePath = sar_path
|
||||
# ir = image_read_cv2(sarImagePath, 'GRAY')
|
||||
# viImagePath = vi_path
|
||||
# vi = image_read_cv2(viImagePath, 'GRAY')
|
||||
#
|
||||
# fusionImagePath = os.path.join(output_path, "fusionSAR_{}.jpg".format(file_name))
|
||||
# fi = image_read_cv2(fusionImagePath, 'GRAY')
|
||||
# # 统计
|
||||
# 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(output_path))
|
||||
# print("\t\t EN\t SD\t SF\t MI\tSCD\tVIF\tQabf\tSSIM")
|
||||
# print("对比结果：" + 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)
|
||||
|
||||
|
||||
def transfer(input_path, quality=20, resize_factor=0.1):
|
||||
# 打开TIFF图像
|
||||
# img = Image.open(input_path)
|
||||
#
|
||||
# # 保存为JPEG，并设置压缩质量
|
||||
# img.save(output_path, 'JPEG', quality=quality)
|
||||
|
||||
# input_path = os.path.join(input_folder, filename)
|
||||
# 获取input_path的文件名
|
||||
|
||||
# 使用os.path.splitext获取文件名和后缀的元组
|
||||
# 使用os.path.basename获取文件名（包含后缀）
|
||||
filename_with_extension = os.path.basename(input_path)
|
||||
filename, file_extension = os.path.splitext(filename_with_extension)
|
||||
|
||||
# 使用os.path.dirname获取文件所在的目录路径
|
||||
output_folder = os.path.dirname(input_path)
|
||||
|
||||
output_path = os.path.join(output_folder, filename + '.jpg')
|
||||
|
||||
img = Image.open(input_path)
|
||||
|
||||
# 将图像缩小到原来的一半
|
||||
new_width = int(img.width * resize_factor)
|
||||
new_height = int(img.height * resize_factor)
|
||||
resized_img = img.resize((new_width, new_height))
|
||||
|
||||
# 保存为JPEG，并设置压缩质量
|
||||
# 转换为RGB模式，丢弃透明通道
|
||||
rgb_img = resized_img.convert('RGB')
|
||||
|
||||
# 保存为JPEG，并设置压缩质量
|
||||
# 压缩
|
||||
rgb_img.save(output_path, 'JPEG', quality=quality)
|
||||
|
||||
print(f'{output_path} 转换完成')
|
||||
|
||||
return output_path
|
||||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='python.exe --sarPath "sar绝对路径" --viPath "可见光路径" --outputPath "输出文件路径"')
|
||||
parser.add_argument('--sarPath', type=str, default="D:\\PythonProject\\MMIF-CDDFuse\\test_img\\Test\\ir\\NH49E011024.tif", required=True,
|
||||
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
|
||||
parser.add_argument('--viPath', type=str, default="D:\\PythonProject\\MMIF-CDDFuse\\test_img\\Test\\vi\\NH49E011024.tif", required=True,
|
||||
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
|
||||
parser.add_argument('--outputPath', type=str, default='results_detect', required=True,
|
||||
help="输出路径！") # 使用的也是图片的目标地址
|
||||
|
||||
opt = parser.parse_args()
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
print(torch.cuda.is_available())
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
||||
|
||||
|
||||
def add_prefix_to_files(directory_path, prefix):
|
||||
# 使用os.listdir获取目录中的所有文件
|
||||
files = os.listdir(directory_path)
|
||||
|
||||
for old_filename in files:
|
||||
# 构建新的文件名
|
||||
new_filename = f"{prefix}_{old_filename}"
|
||||
|
||||
# 构建旧文件路径和新文件路径
|
||||
old_path = os.path.join(directory_path, old_filename)
|
||||
new_path = os.path.join(directory_path, new_filename)
|
||||
|
||||
# 使用os.rename进行文件重命名
|
||||
os.rename(old_path, new_path)
|
||||
|
||||
print(f'{old_filename} 重命名为 {new_filename}')
|
||||
|
||||
# 替换为实际的目录路径和前缀
|
||||
# directory_path = '/path/to/your/directory'
|
||||
# new_prefix = 'new'
|
||||
#
|
||||
# # 执行批量重命名
|
||||
# add_prefix_to_files(directory_path, new_prefix)
|
||||
2
train.py
2
train.py
@ -222,7 +222,7 @@ for epoch in range(num_epochs):
|
||||
time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
|
||||
epoch_time = time.time() - prev_time
|
||||
prev_time = time.time()
|
||||
if i % 100 == 0:
|
||||
if step % 100 == 0:
|
||||
sys.stdout.write(
|
||||
"\r[Epoch %d/%d] [Batch %d/%d] [loss: %f] ETA: %.10s"
|
||||
% (
|
||||
|
||||
16
trainExe.py
16
trainExe.py
@ -1,4 +1,3 @@
|
||||
import os
|
||||
import subprocess
|
||||
import datetime
|
||||
|
||||
@ -9,15 +8,8 @@ command = "/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFu
|
||||
current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
output_file = f"/home/star/whaiDir/PFCFuse/logs/log_{current_time}.log"
|
||||
|
||||
try:
|
||||
# 运行命令并将输出重定向到文件
|
||||
with open(output_file, 'w') as file:
|
||||
result = subprocess.run(command.split(), stdout=file, stderr=subprocess.STDOUT, check=True)
|
||||
# 运行命令并将输出重定向到文件
|
||||
with open(output_file, 'w') as file:
|
||||
subprocess.run(command.split(), stdout=file, stderr=subprocess.STDOUT)
|
||||
|
||||
# 如果命令成功执行，则打印确认信息
|
||||
print(f"Command executed successfully. Output has been written to {output_file}")
|
||||
except subprocess.CalledProcessError as e:
|
||||
# 如果命令执行失败，则删除文件并打印错误信息
|
||||
if os.path.exists(output_file):
|
||||
os.remove(output_file)
|
||||
print(f"Command failed with return code {e.returncode}. No log file was created.")
|
||||
print(f"Command output has been written to {output_file}")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user