metrics.py

import torch
import numpy as np


def calc_ergas(img_tgt, img_fus):
    img_tgt = np.squeeze(img_tgt)
    img_fus = np.squeeze(img_fus)
    img_tgt = img_tgt.reshape(img_tgt.shape[0], -1)
    img_fus = img_fus.reshape(img_fus.shape[0], -1)

    rmse = np.mean((img_tgt-img_fus)**2, axis=1)
    rmse = rmse**0.5
    mean = np.mean(img_tgt, axis=1)

    ergas = np.mean((rmse/mean)**2)
    ergas = 100/4*ergas**0.5

    return ergas

def calc_psnr(img_tgt, img_fus):
    mse = np.mean((img_tgt-img_fus)**2)
    img_max = np.max(img_tgt)
    psnr = 10*np.log10(img_max**2/mse)

    return psnr

def calc_rmse(img_tgt, img_fus):
    rmse = np.sqrt(np.mean((img_tgt-img_fus)**2))

    return rmse

def calc_sam(img_tgt, img_fus):
    img_tgt = np.squeeze(img_tgt)
    img_fus = np.squeeze(img_fus)
    img_tgt = img_tgt.reshape(img_tgt.shape[0], -1)
    img_fus = img_fus.reshape(img_fus.shape[0], -1)
    img_tgt = img_tgt / np.max(img_tgt)
    img_fus = img_fus / np.max(img_fus)

    A = np.sqrt(np.sum(img_tgt**2, axis=0))
    B = np.sqrt(np.sum(img_fus**2, axis=0))
    AB = np.sum(img_tgt*img_fus, axis=0)

    sam = AB/(A*B)
    sam = np.arccos(sam)
    sam = np.mean(sam)*180/3.1415926535

    return sam