I’m trying to use the output of SegNet class masks to isolate specific parts of an image to perform further analysis by ignoring regions of the image where certain classes are detected (e.g. only analyze regions where walls/floors are not detected). To do so, I’m planning to use the class mask to track detected classes, mark regions detected as ‘blacklisted’ classes, and tell the second round of processing to ignore those regions.
My concern is this: by default, using [grid_width , grid_height = net.GetGridSize()], I get a class mask that’s 13x16. My thought was to upscale that mask to my image size (224x224) and do a pixel-by-pixel check to eliminate pixels containing blacklisted classes. However, I read in another thread that this upscale doesn’t actually change the analysis and is… well, an upscale. Without a lot of knowledge of the workings of that process, I’m hoping to find if I’ll lose a significant amount of accuracy in upscaling the mask. Any guidance on this problem is much appreciated.