diff --git a/test.py b/test.py new file mode 100644 index 0000000..c705c09 --- /dev/null +++ b/test.py @@ -0,0 +1,132 @@ +import argparse +import sys +import uuid + +from matplotlib import image as mpimg, pyplot as plt + +from net import Sar_Restormer_Encoder,Vi_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_04-10-11-56.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\ + + ir_path = opt.irPath + vi_path = opt.viPath + output_path = opt.outputPath + + print("\n" * 2 + "=" * 80) + + model_name = "CDDFuse " + print("The ir_path of " + ir_path + ' :') + print("The vi_path of " + vi_path + ' :') + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + SAR_Encoder = nn.DataParallel(Sar_Restormer_Encoder()).to(device) + VI_Encoder = nn.DataParallel(Vi_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) + + SAR_Encoder.load_state_dict(torch.load(ckpt_path)['SAR_DIDF_Encoder']) + VI_Encoder.load_state_dict(torch.load(ckpt_path)['VI_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']) + SAR_Encoder.eval() + VI_Encoder.eval() + + Decoder.eval() + BaseFuseLayer.eval() + DetailFuseLayer.eval() + + with torch.no_grad(): + data_IR = image_read_cv2(ir_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_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 = VI_Encoder(data_VIS) + feature_I_B, feature_I_D, feature_I = SAR_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()) + + # 获取文件名（包含后缀） + file_name_with_extension = os.path.basename(ir_path) + # 分离文件名和文件后缀 + file_name, file_extension = os.path.splitext(file_name_with_extension) + + img_save(fi, "fusion_" + file_name, output_path) + print("输出文件路径：" + output_path + "fusion_" + file_name + ".png") + + metric_result = np.zeros((8)) + irImagePath = ir_path + ir = image_read_cv2(irImagePath, 'GRAY') + viImagePath = vi_path + vi = image_read_cv2(viImagePath, 'GRAY') + + fusionImagePath = os.path.join(output_path, "fusion_{}.png".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 parse_opt(): + parser = argparse.ArgumentParser( + description='python.exe --irPath "红外绝对路径" --viPath "可见光路径" --outputPath "输出文件路径"') + parser.add_argument('--irPath', type=str, default="D:\\PythonProject\\MMIF-CDDFuse\\test_img\\cus\\sar\\NH49E001013_10.tif", required=False, + 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\\cus\\opr\\NH49E001013_10.tif", required=False, + 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=False, + 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__': + opt = parse_opt() + main(opt)