Apply function to masked region

Question

I have an image like that:

I have both the mask and the original image. I would like to calculate the colour temperature of ONLY the ducks region.

Right now, I'm iterating through each row and column of the image below and getting pixels where their values are not zero. But I think this isn't the right way to do this. Any suggestions?

What I did was:

xyzImg = cv2.cvtColor(resImage, cv2.COLOR_BGR2XYZ)
x,y,z = cv2.split(xyzImg)
xList=[]
yList=[]
zList=[]

rows=x.shape[0]
cols=x.shape[1]

for i in range(rows):
    for j in range(cols):
        if (x[i][j]!=0) and (y[i][j]!=0) and (z[i][j]!=0):
            xList.append(x[i][j])
            yList.append(y[i][j])
            zList.append(z[i][j])

xAvg = np.mean(xList)
yAvg = np.mean(yList)
zAvg = np.mean(zList)

xs = xAvg / (xAvg + yAvg + zAvg)
ys = yAvg / (xAvg + yAvg + zAvg)

xyChrome = np.array([xs,ys])

But this is very slow and I don't think its right...

Answer 1

The simplest way would be to use cv2.mean() function.

It takes two arguments src (having 1 to 4 channels) and mask and returns a vector with mean values for individual channels.

Refer to cv2::mask