OpenCV - Apply mask to a color image


Question

How can I apply mask to a color image in latest python binding (cv2)? In previous python binding the simplest way was to use cv.Copy e.g.

cv.Copy(dst, src, mask)

But this function is not available in cv2 binding. Is there any workaround without using boilerplate code?

1
30
2/15/2017 7:33:38 PM

Accepted Answer

Here, you could use cv2.bitwise_and function if you already have the mask image.

For check the below code:

img = cv2.imread('lena.jpg')
mask = cv2.imread('mask.png',0)
res = cv2.bitwise_and(img,img,mask = mask)

The output will be as follows for a lena image, and for rectangular mask.

enter image description here

50
5/6/2012 10:49:02 AM

Well, here is a solution if you want the background to be other than a solid black color. We only need to invert the mask and apply it in a background image of the same size and then combine both background and foreground. A pro of this solution is that the background could be anything (even other image).

This example is modified from Hough Circle Transform. First image is the OpenCV logo, second the original mask, third the background + foreground combined.

apply mask and get a customized background

# http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.html
import cv2
import numpy as np

# load the image
img = cv2.imread('E:\\FOTOS\\opencv\\opencv_logo.png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# detect circles
gray = cv2.medianBlur(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), 5)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=50, minRadius=0, maxRadius=0)
circles = np.uint16(np.around(circles))

# draw mask
mask = np.full((img.shape[0], img.shape[1]), 0, dtype=np.uint8)  # mask is only 
for i in circles[0, :]:
    cv2.circle(mask, (i[0], i[1]), i[2], (255, 255, 255), -1)

# get first masked value (foreground)
fg = cv2.bitwise_or(img, img, mask=mask)

# get second masked value (background) mask must be inverted
mask = cv2.bitwise_not(mask)
background = np.full(img.shape, 255, dtype=np.uint8)
bk = cv2.bitwise_or(background, background, mask=mask)

# combine foreground+background
final = cv2.bitwise_or(fg, bk)

Note: It is better to use the opencv methods because they are optimized.


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