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
To learn how to perform image masking with OpenCV, just keep reading.,You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch,We’ll then implement a Python script to mask images with OpenCV.,Let’s learn how to apply image masking using OpenCV!
Luckily, OpenCV is pip-installable:
$ pip install opencv - contrib - python
Your project folder should look like the following:
$ tree.--dirsfirst .├──adrian.png└── opencv_masking.py 0 directories, 2 files
Open the opencv_masking.py 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")
cv2.circle(mask, (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)
cv2.waitKey(0)From there, open a shell and execute the following command:
$ python opencv_masking.py
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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;