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test.py
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132
test.py
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import argparse
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import sys
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import uuid
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from matplotlib import image as mpimg, pyplot as plt
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from net import Sar_Restormer_Encoder,Vi_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_04-10-11-56.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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ir_path = opt.irPath
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vi_path = opt.viPath
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output_path = opt.outputPath
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print("\n" * 2 + "=" * 80)
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model_name = "CDDFuse "
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print("The ir_path of " + ir_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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SAR_Encoder = nn.DataParallel(Sar_Restormer_Encoder()).to(device)
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VI_Encoder = nn.DataParallel(Vi_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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SAR_Encoder.load_state_dict(torch.load(ckpt_path)['SAR_DIDF_Encoder'])
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VI_Encoder.load_state_dict(torch.load(ckpt_path)['VI_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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SAR_Encoder.eval()
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VI_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(ir_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_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 = VI_Encoder(data_VIS)
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feature_I_B, feature_I_D, feature_I = SAR_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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# 获取文件名（包含后缀）
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file_name_with_extension = os.path.basename(ir_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(fi, "fusion_" + file_name, output_path)
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print("输出文件路径：" + output_path + "fusion_" + file_name + ".png")
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metric_result = np.zeros((8))
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irImagePath = ir_path
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ir = image_read_cv2(irImagePath, 'GRAY')
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viImagePath = vi_path
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vi = image_read_cv2(viImagePath, 'GRAY')
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fusionImagePath = os.path.join(output_path, "fusion_{}.png".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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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 parse_opt():
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parser = argparse.ArgumentParser(
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description='python.exe --irPath "红外绝对路径" --viPath "可见光路径" --outputPath "输出文件路径"')
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parser.add_argument('--irPath', type=str, default="D:\\PythonProject\\MMIF-CDDFuse\\test_img\\cus\\sar\\NH49E001013_10.tif", required=False,
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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\\cus\\opr\\NH49E001013_10.tif", required=False,
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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=False,
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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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opt = parse_opt()
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main(opt)
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