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)
import cv2
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
CDDFuse_path=r"models/CDDFuse_IVF.pth"
CDDFuse_MIF_path=r"models/CDDFuse_MIF.pth"
for dataset_name in ["MRI_CT","MRI_PET","MRI_SPECT"]: 
    print("\n"*2+"="*80)
    print("The test result of "+dataset_name+" :")
    print("\t\t EN\t SD\t SF\t MI\tSCD\tVIF\tQabf\tSSIM")
    for ckpt_path in [CDDFuse_path,CDDFuse_MIF_path]: 
        model_name=ckpt_path.split('/')[-1].split('.')[0]
        test_folder=os.path.join('test_img',dataset_name) 
        test_out_folder=os.path.join('test_result',dataset_name)

        
        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():
            for img_name in os.listdir(os.path.join(test_folder,dataset_name.split('_')[0])):
                data_IR=image_read_cv2(os.path.join(test_folder,dataset_name.split('_')[1],img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
                data_VIS = image_read_cv2(os.path.join(test_folder,dataset_name.split('_')[0],img_name), 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 = 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)
                if ckpt_path==CDDFuse_path:
                    data_Fuse, _ = Decoder(data_IR+data_VIS, feature_F_B, feature_F_D)
                else:
                    data_Fuse, _ = Decoder(None, 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())
                img_save(fi, img_name.split(sep='.')[0], test_out_folder)
        eval_folder=test_out_folder  
        ori_img_folder=test_folder

        metric_result = np.zeros((8))
        for img_name in os.listdir(os.path.join(ori_img_folder,dataset_name.split('_')[0])):
                ir = image_read_cv2(os.path.join(ori_img_folder,dataset_name.split('_')[1], img_name), 'GRAY')
                vi = image_read_cv2(os.path.join(ori_img_folder,dataset_name.split('_')[0], img_name), 'GRAY')
                fi = image_read_cv2(os.path.join(eval_folder, img_name.split('.')[0]+".png"), 'GRAY')
                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(eval_folder))
        
        print(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)