import datetime

import cv2
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)

current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")


os.environ["CUDA_VISIBLE_DEVICES"] = "0"
ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion10-08-16-20.pth"

for dataset_name in ["TNO","RoadScene","sar"]:
    print("\n"*2+"="*80)
    model_name="PFCFuse    "
    print("The test result of "+dataset_name+' :')
    test_folder = os.path.join('test_img', dataset_name)
    test_out_folder=os.path.join('test_result',current_time,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)).to(device)
    DetailFuseLayer = nn.DataParallel(DetailFeatureExtraction(num_layers=1)).to(device)

    Encoder.load_state_dict(torch.load(ckpt_path)['DIDF_Encoder'],strict=False)
    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,"ir")):

            data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
            data_VIS = cv2.split(image_read_cv2(os.path.join(test_folder, "vi", img_name), mode='YCrCb'))[0][np.newaxis, np.newaxis, ...] / 255.0
            data_VIS_BGR = cv2.imread(os.path.join(test_folder, "vi", img_name))
            _, 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)
            img_save(rgb_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,"ir")):
            ir = image_read_cv2(os.path.join(ori_img_folder,"ir", img_name), 'GRAY')
            vi = image_read_cv2(os.path.join(ori_img_folder,"vi", 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("\t\t EN\t SD\t SF\t MI\tSCD\tVIF\tQabf\tSSIM")
    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)