QGIS – Overlaying Binary Raster Over Colorized Raster Layer Without Affecting Color

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I'm looking to merge a raster layer of bounding boxes over top a standard RGB raster layer so that the boxes fit over certain features in the background raster, but they are now the same layer. The boxes are black (binarized), so the boxes should look black over the colour photo.

I've tried raster -> merge with each of the RGB bands plus the box layer, but it doesn't work. I've also messed around with raster calculator. I tried to make it so the box outlines were '0' and then multiplied that layer by the bands from the RGB photo, so the underlaying pixels would be black when merged back together, however, this doesn't seem to work.

This is what the output should look like -again- as one layer:

Best Answer

When rasterizing, set Assign a specified nodata value to output bands to Not set (instead of 0 as you have it). It will result in a raster with your boxes in white (value = 1) and the rest of the raster in black (value = 0) instead of no data.
Use the raster calculator and multiply the rasterized layer with the RGB raster layer. However, the values of the rasterized layer should be turned: box should become 0, rest become 1. Use this formula: ABS ( "Rasterized@1" -1)
Multiply this with each band of your RGB raster, than stick the three outputs together.

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[GIS] Use QGIS to identify and count pixel colour in a photo

You want to count the number of discrete pixel colours inside an area? As far as I know, that can't be done with zonal statistics.

e.g.

Polygon 1 - area is 1000 pixels
500 pixels are (122,135,21)
400 pixels are (22,132,178)
100 pixels are (2,156,99)

Doing that on an raw (unclassified) rgb image will give a HUGE number of possible colours. A polygon with an area of 1000 pixels could easily have 900 unique colours, for example. Most pixel colours will appear only once, and a few will appear twice.

A python/gdal script could be written to count unique colours, but it would be slow and memory-intensive for that very reason.

You can get around this by using a paletted image to "merge" similar colours into a single value. This means that greens would all appear as one value, greys another value, and so on.

(If you're familiar with Photoshop/GIMP, this is the same as reducing an image to a fixed palette size e.g. 16 colours, where each pixel is 'rounded' to the nearest colour from a representative palette of 16 colours)

Try "convert rgb to paletted" (raster > conversion > rgb to pct). More info here

This will classify pixels into groups of similar colour.

Now the problem is easier to address.

I took an aerial image and reduced it to 4 colours.

Then I ran gdalinfo to get a histogram of the values. In Linux, use the terminal, in Windows, use the OSGeo4W shell.

gdalinfo -hist /path/to/my.tiff

This gave me

313730340 154657 11795972 160083079 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

So there are 313,730,340 pixels with value 0. And only 154,657 with value 1.

You'll need to work out which value equates to which colour, though.

There's also a K-Means raster classifier in Orfeo Toolbox, this does a simplification of colours but uses a different algorithm.

Best Answer

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[GIS] Use QGIS to identify and count pixel colour in a photo

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