Get the average color inside a contour with Open CV

Question

So I decided to get started learning Open CV and Python together!

My first project is to detect moving objects on a relatively still background and then detect their average color to sort them. There are at least 10 objects to detect and I am processing a colored video.

So far I managed to remove the background, identify the contours (optionally get the center of each contour) but now I am struggling getting the average or mean color inside of each contour. There are some topics about this kind of question but most of them are written in C. Apparently I could use cv.mean() but I can't get a working mask to feed in this function. I guess it's not so difficult but I am stuck there... Cheers!

import numpy as np
import cv2

video_path = 'test.h264'

cap = cv2.VideoCapture(video_path)
fgbg = cv2.createBackgroundSubtractorMOG2()


while (cap.isOpened):

    ret, frame = cap.read()
    if ret==True:
        fgmask = fgbg.apply(frame)
        (contours, hierarchy) = cv2.findContours(fgmask, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

        for c in contours:
            if cv2.contourArea(c) > 2000:
                cv2.drawContours(frame, c, -1, (255,0,0), 3)
        cv2.imshow('foreground and background',fgmask)
        cv2.imshow('rgb',frame)

    key = cv2.waitKey(1) & 0xFF

    if key == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

Answer 1

You can create a mask by first creating a new image with the same dimensions as your input image and pixel values set to zero.

You then draw the contour(s) onto this image with pixel value 255. The resulting image can be used as a mask.

mask = np.zeros(frame.shape, np.uint8)
cv2.drawContours(mask, c, -1, 255, -1)

mask can then be used as a parameter to cv.mean like

mean = cv.mean(frame, mask=mask)

Just one word of caution, the mean of RGB colors does not always make sense. Maybe try converting to HSV color space and solely use the H channel for detecting the color of your objects.