//First load your source image, here load as gray scale
cv::Mat srcImage = cv::imread("sourceImage.jpg", CV_LOAD_IMAGE_GRAYSCALE);
//Then define your mask image
cv::Mat mask = cv::Mat::zeros(srcImage.size(), srcImage.type());
//Define your destination image
cv::Mat dstImage = cv::Mat::zeros(srcImage.size(), srcImage.type());
//I assume you want to draw the circle at the center of your image, with a radius of 50
cv::circle(mask, cv::Point(mask.cols/2, mask.rows/2), 50, cv::Scalar(255, 0, 0), -1, 8, 0);
//Now you can copy your source image to destination image with masking
srcImage.copyTo(dstImage, mask);
Then do your further processing on your dstImage. Assume this is your source image:
Then the above code gives you this as gray scale input:
And this is the binary mask you created:
And this is your final result after masking operation:
//First load your source image, here load as gray scale
cv::Mat srcImage = cv::imread("sourceImage.jpg", CV_LOAD_IMAGE_GRAYSCALE);
//Then define your mask image
cv::Mat mask = cv::Mat::zeros(srcImage.size(), srcImage.type());
//Define your destination image
cv::Mat dstImage = cv::Mat::zeros(srcImage.size(), srcImage.type());
//I assume you want to draw the circle at the center of your image, with a radius of 50
cv::circle(mask, cv::Point(mask.cols/2, mask.rows/2), 50, cv::Scalar(255, 0, 0), -1, 8, 0);
//Now you can copy your source image to destination image with masking
srcImage.copyTo(dstImage, mask);
Then do your further processing on your dstImage. Assume this is your source image:
Then the above code gives you this as gray scale input:
And this is the binary mask you created:
And this is your final result after masking operation:
Since you are looking for a clear circular center with a shape inside, you could use Hough Transform to get that area- a careful selection of parameters will help you get this area perfectly.
A detailed tutorial is here: http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html
For setting pixels outside a region black:
Create a mask image :
cv::Mat mask(img_src.size(),img_src.type());
Mark the points inside with white color :
cv::circle( mask, center, radius, cv::Scalar(255,255,255),-1, 8, 0 );
You can now use bitwise_AND and thus get an output image with only the pixels enclosed in mask.
cv::bitwise_and(mask,img_src,output);
Simplest way, with 'Mat img' (image to be masked, input) and 'Mat masked' (masked image, output):
img.copyTo(masked, mask)
where 'Mat mask' is a matrix not necessarily binary (copyTo considers elements with zero value). Masked can be of any size and type; it is reallocated if needed.
See the doc.
You can simply use bitwise_and() function.
Check the documentation.
While @perrejba s answer is correct, it uses the legacy C-style functions. As the question is tagged C++, you may want to use a method instead:
inputMat.copyTo(outputMat, maskMat);
All objects are of type cv::Mat.
Please be aware that the masking is binary. Any non-zero value in the mask is interpreted as 'do copy'. Even if the mask is a greyscale image.
Also be aware that the .copyTo() function does not clear the output before copying.
If you want to permanently alter the original Image, you have to do an additional copy/clone/assignment. The copyTo() function is not defined for overlapping input/output images. So you can't use the same image as both input and output.
You don't apply a binary mask to an image. You (optionally) use a binary mask in a processing function call to tell the function which pixels of the image you want to process. If I'm completely misinterpreting your question, you should add more detail to clarify.
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.
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.
# 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.