在图像OpenCV上滑动窗口

Question

I am trying to define a window that scans across an image, I want to find the average RGB values in each window and output them.

I have managed to get the average RGB values for the entire image like this:

img = cv2.imread('images/0021.jpg')

mean = cv2.mean(img)

print mean[0]
print mean[1]
print mean[2]

Gives:

#Output
51.0028081597
63.1069849537
123.663025174

How could I apply this mean function to a moving window and output the values for each window?

EDIT:

Here is what I have now:

img = cv2.imread('images/0021.jpg')

def new(img):
    rows,cols = img.shape
    final = np.zeros((rows, cols, 3, 3))
    for x in (0,1,2):
        for y in (0,1,2):
            img1 = np.vstack((img[x:],img[:x]))
            img1 = np.column_stack((img1[:,y:],img1[:,:y]))
            final[x::3,y::3] = np.swapaxes(img1.reshape(rows/3,3,cols/3,-1),1,2)
            b,g,r = cv2.split(final)
            rgb_img = cv2.merge([r,g,b])
            mean = cv2.mean(rgb_img)
            print mean[0]
            print mean[1]
            print mean[2]

But now I am getting zero output.

Answer 1

I wrote a script similar to the given links. It basically divides your img to 3*3 parts and then computes mean (and standard deviation) of each part. With a little array optimization I think you can use it real time/on video.

PS: Divisions should be integer division

EDIT: now the script gives 9 outputs each represent a mean of its own region.

import numpy as np
import cv2

img=cv2.imread('aerial_me.jpg')
scale=3
y_len,x_len,_=img.shape

mean_values=[]
for y in range(scale):
for x in range(scale):
    cropped_image=img[(y*y_len)/scale:((y+1)*y_len)/scale,
                        (x*x_len)/scale:((x+1)*x_len)/scale]

    mean_val,std_dev=cv2.meanStdDev(cropped_image)
    mean_val=mean_val[:3]

    mean_values.append([mean_val])
mean_values=np.asarray(mean_values)
print mean_values.reshape(3,3,3)

The output is bgr mean values of each window:

[[[  69.63661573   66.75843063   65.02066449]
  [ 118.39233345  114.72655391  116.14441964]
  [ 159.26887164  143.40760348  144.63208436]]

 [[  75.50831044  107.45708276  103.0781851 ]
  [ 108.46450034  141.52005495  139.84878949]
  [ 122.67583265  154.86071992  153.67907072]]

 [[  83.67678571  131.45284169  128.27706902]
  [  86.57919815  129.09968235  128.64439389]
  [  90.1102402   135.33173999  132.86622807]]]
[Finished in 0.5s]

Answer 2

Filter with a kernel of shape equal to your window, and values all equal to 1/window_areas. The result is local average you seek (also known as a "box blur" operation).