在OpenCV中查找图像的方差-Python

Question

I am trying to find the variance of a greyscale image in OpenCV -Python. I first take the image read in and split it into sub-images, I want to calculate the variance of these sub-images ( cropped_img ).

I'm not sure how to calculate variance in python, I assumed that I could calculate the covariance matrix to find the variance using the rule:

Var(X) = Cov(X,X)

The thing is I can't get my head around how to use cv2.calcCovarMatrix() , and I can't find any examples in python.

I did find this example in C++ but I have never used the language and im struggling to convert it into python: calcCovarMatrix in multichannel image and unresolved assertion error

Here is my code:

#import packages
import numpy as np
import cv2

#Read in image as grey-scale
img = cv2.imread('images/0021.jpg', 0)

#Set scale of grid 
scale = 9

#Get x and y components of image
y_len,x_len = img.shape

covar = []
for y in range(scale):
    for x in range(scale):
        #Crop image 9*9 windows
        cropped_img=img[(y*y_len)/scale:((y+1)*y_len)/scale,
                          (x*x_len)/scale:((x+1)*x_len)/scale]

        #Here is where I need to calc variance
        cv2.calcCovarMatrix(cropped_img, covar, meanBGR, cv2.cv.CV_COVAR_NORMAL)
        #???
        cropped_img[:] = covar

cv2.imshow('output_var',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

If anyone has any ideas or if you have a better way to calculate variance then I would be extremely grateful.

Thanks.

EDIT: I also found this example in c; mean and variance of image in single pass , but it doesn't seem too efficient.

Answer 1

To get the variance of gray scale image in python you can use numpy.

import numpy as np

var = np.var(img)