import os
import numpy as np
import cv2
import torch
from skimage.metrics import structural_similarity as compare_ssim
from skimage.metrics import peak_signal_noise_ratio, structural_similarity
from ct_utils import psnr_ssim

name = 'L096_8'
view_number = name.split('_')[-1]
recon_path = f'results_{name}/AAPM_256_ncsnpp_continuous/sparseview_CT_ADMM_TV_total/m{view_number}/rho10/lambda0.04/recon'
gt_path = f'results_{name}/AAPM_256_ncsnpp_continuous/sparseview_CT_ADMM_TV_total/m{view_number}/rho10/lambda0.04/label'
length = len([name for name in os.listdir(recon_path) if name.endswith('.png')])
# length = 100
ids = [i for i in range(length)]
recon, gt = [], []
for id in ids:
    # load png file 
    recon.append(cv2.imread(os.path.join(recon_path, f'{id}.png'), cv2.IMREAD_GRAYSCALE))
    # gt: 000.png. 001.png, ,,, 131.png
    gt.append(cv2.imread(os.path.join(gt_path, f'{id}.png'), cv2.IMREAD_GRAYSCALE))

recon_volume = np.stack(recon, axis=0) / 255
gt_volume = np.stack(gt, axis=0) / 255
# compute axial view    

psnr= peak_signal_noise_ratio(gt_volume, recon_volume)
print('PSNR:', psnr)
