Reputation: 43
I need to find correlation between two images, using numpy, but basic math only. I have the problem:"* IndexError: index 5434 is out of bounds for axis 0 with size 5434*". And i have a code. Tell me what to do, pls.
img = PIL.Image.open("SR1.png").convert("L")
im = numpy.array(img)
img2 = PIL.Image.open("SR61.png").convert("L")
im2 = numpy.array(img2)
np.array(im,dtype=float)
np.array(im2,dtype=float)
import math
import cmath
def correlationCoefficient(X, Y, n) :
sum_X = 0
sum_Y = 0
sum_XY = 0
squareSum_X = 0
squareSum_Y = 0
i = 0
for i in range(n) :
sum_X = sum_X + X[i]
sum_Y = sum_Y + Y[i]
sum_XY = sum_XY + X[i] * Y[i]
squareSum_X = squareSum_X + X[i] * X[i]
squareSum_Y = squareSum_Y + Y[i] * Y[i]
i = i + 1
corr = (n * sum_XY - sum_X * sum_Y)/(cmath.sqrt((n * squareSum_X - sum_X * sum_X)* (n * squareSum_Y - sum_Y * sum_Y)))
return corr
X = im.flatten()
Y = im2.flatten()
n = len(X)
print ('{0:.6f}'.format(correlationCoefficient(X, Y, n)))
Upvotes: 3
Views: 2997
Reputation: 17804
You can use the function corrcoef
in numpy to find Pearson correlation. First you need to flatten
both image arrays:
np.corrcoef(im1.flatten(), im2.flatten())
Upvotes: 3
Reputation: 9806
Here's a vectorized version of your function:
import numpy as np
def correlationCoefficient(X, Y):
n = X.size
sum_X = X.sum()
sum_Y = Y.sum()
sum_XY = (X*Y).sum()
squareSum_X = (X*X).sum()
squareSum_Y = (Y*Y).sum()
corr = (n * sum_XY - sum_X * sum_Y)/(np.sqrt((n * squareSum_X - sum_X * sum_X)* (n * squareSum_Y - sum_Y * sum_Y)))
return corr
It is also important to normalize your image arrays to avoid overflow:
from PIL import Image
img1 = Image.open("1.jpg").convert("L")
im1 = np.array(img1)/255
img2 = Image.open("2.jpg").convert("L")
im2 = np.array(img2)/255
print ('{0:.6f}'.format(correlationCoefficient(im1, im2)))
Upvotes: 2