efficiency.py

from skimage.measure import compare_ssim
import numpy as np
import cv2
import math

def psnr(img1 , img2):
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return 100
    PIXEL_MAX = 255.0
    PSNR = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
    return PSNR

def ssim(img1 , img2):
    (score, diff) = compare_ssim(img1, img2, full=True, multichannel=True)
    diff = (diff * 255).astype("uint8")
    return score

def meanIt(x):
    y = 0
    z = 0
    a,b,c = x.shape
    for i in range(a):
        for j in range(b):
            for k in range(c):
                y = y+x[i][j][k]
                z = z + 1
    return y/z

def callIt(x,mean_x,y,mean_y):
    z = 0
    a,b,c = x.shape
    for i in range(a):
        for j in range(b):
            for k in range(c):
                z = z + (x[i][j][k]-mean_x)*(y[i][j][k]-mean_y)
    return z

def correlationCoefficient(x,y):
    mean_x, mean_y  = meanIt(x), meanIt(y)
    cov   = callIt(x,mean_x,y,mean_y)
    var_x = callIt(x,mean_x,x,mean_x)
    var_y = callIt(y,mean_y,y,mean_y)
   # print(sum(cov))
   # print( math.sqrt(var_x))
    #print(math.sqrt(var_y))
    #return 2
    return (cov / math.sqrt(var_x)) / math.sqrt(var_y)

def epi(x1,y1):
    num = 0
    den = 0
    x = cv2.cvtColor(x1,cv2.COLOR_BGR2GRAY)
    y = cv2.cvtColor(y1,cv2.COLOR_BGR2GRAY)
    row,col = x.shape
    
    # row,col,ch = img.shape
    for i in range(row):
        for j in range(col-1):
            num = num + np.abs(y[i][j+1]-y[i][j])
            den = den + np.abs(x[i][j+1] - x[i][j])
    z = num/den
    return z