Wants to input an rgb image and get the classified image as an rbg instead of greyscale

I am new to Matlab and I am finding it difficult to input a color image and give the classified output as color image. Instead I am only able to do for greyscale image. I have done the code for greyscale image. Please help me out. Can someone help me with the code to output as coloured image rather than a grayscale image. I have attached my code as well.

Answer 1

[Image Analyst]

Have you tried colormap() and colorbar() to apply a colormap to the gray levels in the classified image?

[renit anthony]

I have tried using those but I am unable to implement in this matlab code I have. I am only good with python.

[Image Analyst]

http://www.mathworks.com/matlabcentral/answers/8026-best-way-s-to-master-matlab

[renit anthony]

Thanks for the link .But can I know where to use these colormap and colorbar in my code ?

[Image Analyst]

Looking at your code, I'm not sure what kind of classification you're doing? It looks like you're doing deep learning and trying to classify images as to whether there is something in the image. This is different than doing segmentation and classifying each blob as some kind of class, like saying the blobs are either circles or rectangles.

[renit anthony]

I just want to know how to get a greyscale images color back using the original image using colormap.

[Image Analyst]

You can't. Once an RGB true color image has been indexed into a monochrome (indexed) image plus a colormap, it's been quantized and you will lose some of the original colors. You'll get a posterized-looking image if you use ind2rgb(grayImage, map), though it might look reasonable if you chose enough colors to use in the colormap, like a hundred or more. If you have only 16 or fewer colors in your colormap, it will look like a cartoon - posterized.

This has nothing whatsoever with deep learning or classification though.

Saying that this particular image has a stop sign in it classifies that image as having a stop sign in it. But it does not classify every single pixel in the image. For example, it does not say this pixel is from a stop sign, that pixel is from a roadway, and the other pixel is from the sky (or car, or lawn, or whatever). For that you'd need something like SegNet rather than a classification CNN. SegNet will classify every pixel in an image but CNN just says if your image has an object from a predefined set of objects in it.