masking bgr image using a 2d mask

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Try to use a mask with the same shape as the image (actually, this will be a 3D mask). After generating your image_mask, do

# create mask with same dimensions as image
mask = numpy.zeros_like(image)

# copy your image_mask to all dimensions(i.e.colors) of your image
for i in range(3):
   mask[: ,: , i] = image_mask.copy()

# apply the mask to your image
masked_image = image[mask]

Maybe this alternative approach would be easier in similar cases:

image[image_mask,: ] = np.nan

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Luckily, OpenCV is pip-installable:

$ pip install opencv - contrib - python

Your project folder should look like the following:

$ tree.--dirsfirst

0 directories, 2 files

Open the file in your project directory structure, and let’s get to work:

import the necessary packages
import numpy as np
import argparse
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type = str,
   default = "adrian.png",
   help = "path to the input image")
args = vars(ap.parse_args())

Let’s look at another example, but this time using a non-rectangular mask:

# now,
let 's make a circular mask with a radius of 100 pixels and
# apply the mask again
mask = np.zeros(image.shape[: 2], dtype = "uint8"), (145, 200), 100, 255, -1)
masked = cv2.bitwise_and(image, image, mask = mask)

# show the output images
cv2.imshow("Circular Mask", mask)
cv2.imshow("Mask Applied to Image", masked)

From there, open a shell and execute the following command:

$ python

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Your mask is not binary. It's showing severe jpeg artifacts. It may even be color or uint8. You need to make it a binary image:

mask = mask(: ,: , 1) > 128;