Measurement index SSMI

# Test forecast and truth folder PSNR and SSIM
# Be careful ： You need two folders in the same order 
import cv2
import glob
import os
from util.metrics import PSNR
from util.metrics import SSIM

psnr_total = 0
ssim_total = 0

# predlist=glob.glob(os.path.join('../Datasets/GoPro/test/results/GoPro', "*"))# Predicted folder path 
# gtlist=glob.glob(os.path.join('../Datasets/GoPro/test/target', "*"))# Truth folder path 

predlist=glob.glob(os.path.join('../Datasets/CHAOS/test/results/CHAOS', "*"))# Predicted folder path 
gtlist=glob.glob(os.path.join('../Datasets/CHAOS/test/target', "*"))# Truth folder path 


for i in range(len(predlist)):
    print(i,os.path.basename(predlist[i]))
    pred_img = cv2.imread(predlist[i])
    pred_img = cv2.cvtColor(pred_img, cv2.COLOR_BGR2RGB)

    gt_img = cv2.imread(gtlist[i])
    gt_img = cv2.cvtColor(gt_img, cv2.COLOR_BGR2RGB)

    psnr = PSNR(pred_img, gt_img)
    ssim = SSIM(pred_img, gt_img)

    psnr_total+=psnr
    ssim_total+=ssim

psnr_average=psnr_total/len(predlist)
ssim_average=ssim_total/len(predlist)

print("PSNR = {:.2f}".format(psnr_total / len(predlist)))
print("SSIM = {:.4f}".format(ssim_total / len(predlist)))




# img=cv2.imread('E:\\others\\1.jpg')
# m,n,c=img.shape
# print(m,n,c)
# img = cv2.resize(img, (m, n), interpolation=cv2.INTER_CUBIC)
# m,n,c=img.shape
# print(m,n,c)