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)