I need to, given an indoor image of a room, to detect the materials of the floor and the wall (and maybe the roof too). I've started looking for open source repositories similar to this and found https://github.com/CSAILVision/unifiedparsing. Using that code I can identify materials for some objects like a sofa or a window but the problem is that (after some days of code debugging) I can't seem to extract the materials of the wall and the floor. Another problem of that code is that it takes a lot of time to run and that I don't really understand it in order to fix it.
So I'm thinking about building a classifier and I've been looking the ADE20k http://groups.csail.mit.edu/vision/datasets/ADE20K/ and the OpenSurfaces http://opensurfaces.cs.cornell.edu/ datasets. The problem is that I don't really know how to use them in order to accomplish my goal. I'm a Software Engineer and I've a basic idea of Deep Learning (I've applied ML for some projects) but it's difficult for me to establish a step by step plan to solve this.
I've been thinking about starting an online course on Deep Learning, but I need to solve this as soon as possible to show it (as a MVP) to a potential customer in order to validate the product and then invest more time improving it.
My first idea is the following one (let me know what you think):
Use this (https://github.com/CSAILVision/semantic-segmentation-pytorch) semantic segmentation library to extract the location of the floor and the walls.
The ade20k dataset has a .txt file related to each image containing each object on the image (the floor for example) and the attributes of the object (tile or wood for example). So I could get the location of the floor (step 1) and the material of the floor.
With the image-object-material I could train a deep learning algorithm to learn to classify new images.
I don't know about you, but it sounds weird to me (especially step 3). I think I'm pretty lost here and don't know how to go on.
What do you think? What would you do?