How to mask image with binary mask [closed]

Use cv2.bitwise_and to mask an image with a binary mask. Any white pixels on the mask (values with 1) will be kept while black pixels (value with 0) will be ignored. Here's a example:

Input image (left), Mask (right)

Result after masking

Code

import cv2 import numpy as np # Load image, create mask, and draw white circle on mask image = cv2.imread('1.jpeg') mask = np.zeros(image.shape, dtype=np.uint8) mask = cv2.circle(mask, (260, 300), 225, (255,255,255), -1) # Mask input image with binary mask result = cv2.bitwise_and(image, mask) # Color background white result[mask==0] = 255 # Optional cv2.imshow('image', image) cv2.imshow('mask', mask) cv2.imshow('result', result) cv2.waitKey() 

Here are two other ways using Python Opencv. The first is similar to that of @nathancy. The second uses multiplication to do the masking. I use the same images as provided by @nathancy:

import cv2 import numpy as np # read image img = cv2.imread('pink_flower.png') #mask it - method 1: # read mask as grayscale in range 0 to 255 mask1 = cv2.imread('pink_flower_mask.png',0) result1 = img.copy() result1[mask1 == 0] = 0 result1[mask1 != 0] = img[mask1 != 0] # mask it - method 2: # read mask normally, but divide by 255.0, so range is 0 to 1 as float mask2 = cv2.imread('pink_flower_mask.png') / 255.0 # mask by multiplication, clip to range 0 to 255 and make integer result2 = (img * mask2).clip(0, 255).astype(np.uint8) cv2.imshow('image', img) cv2.imshow('mask1', mask1) cv2.imshow('masked image1', result1) cv2.imshow('masked image2', result2) cv2.waitKey(0) cv2.destroyAllWindows() # save results cv2.imwrite('pink_flower_masked1.png', result1) cv2.imwrite('pink_flower_masked2.png', result2) 

Results are the same for both methods:

The other answers did not work for me. Back then, I spent so much time to find a good masking function. Here are two simple answers with numpy only.

import numpy as np arr = np.arange(27).reshape(3,3,3) #3 channel image mask = np.zeros(shape=(3,3)) mask[1,1] = 1 # binary mask mask_3d = np.stack((mask,mask,mask),axis=0) #3 channel mask ## Answer 1 # Simply multiply the image array with the mask masked_arr = arr*mask_3d ## Answer 2 # Use the where function in numpy masked_arr = np.where(mask_3d==1,arr,mask_3d) #Both answer gives print(masked_arr) array([[[ 0., 0., 0.], [ 0., 4., 0.], [ 0., 0., 0.]], [[ 0., 0., 0.], [ 0., 13., 0.], [ 0., 0., 0.]], [[ 0., 0., 0.], [ 0., 22., 0.], [ 0., 0., 0.]]])