Python - Grayscale very low with a mask

Python - Grayscale very low with a mask

By : Ankit Shriwal
Date : November 21 2020, 07:31 AM
hop of those help? I'm working on pictures that have been converted to grayscale with: , By using OpenCV it works...
img = cv2.imread('test.png',0)
code :

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How to convert grayscale bitmap into alpha mask?

How to convert grayscale bitmap into alpha mask?

By : Brian
Date : March 29 2020, 07:55 AM
will be helpful for those in need ColorMatrix to rescue!
Quoting Android docs, ColorMatrix:
code :
final BitmapFactory.Options options = new BitmapFactory.Options();
options.inPreferredConfig = Bitmap.Config.ARGB_8888;
options.inScaled = false;

// Load source grayscale bitmap
Bitmap grayscale = BitmapFactory.decodeResource(getResources(), R.drawable.my_grayscale_mask, options);
// Place for  alpha mask. It's specifically ARGB_8888 not ALPHA_8, 
// ALPHA_8 for some reason didn't work out for me. 
Bitmap alpha = Bitmap.createBitmap(grayscale.getWidth(), grayscale.getHeight(),
float[] matrix = new float[] {
        0, 0, 0, 0, 0,
        0, 0, 0, 0, 0,
        0, 0, 0, 0, 0,
        1, 0, 0, 0, 0};
Paint grayToAlpha = new Paint();
grayToAlpha.setColorFilter(new ColorMatrixColorFilter(new ColorMatrix(matrix)));
Canvas alphaCanvas = new Canvas(alpha);
// Make sure nothing gets scaled during drawing
// Draw grayscale bitmap on to alpha canvas, using color filter that
// takes alpha from red channel
alphaCanvas.drawBitmap(grayscale, 0, 0, grayToAlpha);
// Bitmap alpha now has usable alpha channel!
Plot mask on top of grayscale image MATLAB

Plot mask on top of grayscale image MATLAB

By : xyz
Date : March 29 2020, 07:55 AM
this one helps. You can use imoverlay written by Steve Eddins.
In addition, you can check these blog posts by him:
How to absdiff() a grayscale image with a mask in CV_8UC1

How to absdiff() a grayscale image with a mask in CV_8UC1

By : user2578981
Date : March 29 2020, 07:55 AM
Hope this helps To achieve the desired color replacement, the following short snippet using simple thresholding should do the trick:
code :
    cv::Mat src = cv::imread("src.png", cv::IMREAD_GRAYSCALE);
    cv::Mat invert_mask = cv::imread("invert_mask.png", cv::IMREAD_GRAYSCALE);

    cv::Mat textInMask = src.clone().mul(invert_mask / 255);
    cv::threshold(textInMask, textInMask, 200, 255, cv::THRESH_BINARY);

    cv::Mat output = src.clone() + invert_mask - textInMask;
Best Interpolation for grayscale segmentation mask

Best Interpolation for grayscale segmentation mask

By : user3490923
Date : March 29 2020, 07:55 AM
wish help you to fix your issue If you only have two values anyway, pay attention if you use the bool dtype, as not all image processing operations will be supported. Something that is both easy and fast is to filter your image with a median filter to fill in gaps. (This is admittedly a little lazy but has produced good results for me in the past).
If you want to interpolate with your uint8 range, maybe thresholding afterwards will do the trick for your.
Applying a 2D numpy mask to grayscale image

Applying a 2D numpy mask to grayscale image

By : Robert Baker
Date : March 29 2020, 07:55 AM
it helps some times I have a 256×256 px 2D mask with pixel values of either 0 or 255, and a matching grayscale image. Both have shapes (256, 256). , Just to add a 3rd option and modify your gray scale image inplace:
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