pfcfuse/test_sar.py

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)