How to build an image generation model for interior room design? - tensorflow

I want to build an image generator model for interior room design.
This model should be able to generate an interior image of living room, bedroom, hall, kitchen, bathroom etc.
I have searched about it and found out the following websites.
https://interiorai.com/
https://image.computer/
And I made this picture when visiting https://image.computer.
[! Contemporary living room with a large TV and 3 windows]1
Above result was perfectly what I want. But free account was restricted to 10 credit images.
And input data don't have to be sentenced, just options are enough for me (e.g. Style: modern, type: living, equipments: [TV: wide, Curtain: white, Window: 3]).
So I decided to google pretrained model of interior design generator, and finally gave up.
I would like to build a tensorflow(or keras) model that acts just like image.computer. Please let me find a model or build model.
Thanks

Related

Using AI to detect damaged parts

I need to use computer vision to detect damaged parts of cars. I have the images of car before and after damage, how do I use computer vision/ AI to detect that, in this case, left headlight and bumper is damaged. I have a dataset of 70 similar image pairs.
I tried image processing, by overlaying the images on top of each to detect damage. But not all images in the dataset fits when overlaid.
I can Mask RCNN to detect the damaged region but how do I reduce it to the parts being damaged?
Before Damage
After Damage
Check out Mask R-CNN. You can train a model with multiple images of damage on cars. Just annotate your data, then train it. Once you have trained, you can then use the splash feature to only highlight areas that you want, i.e, damage. Its fairly easy to set up, and it seems perfect in your case.

Should deep learning classification be used to classify details such as liquid level in bottle

Can deep learning classification be used to precisely label/classify both the object and one of its features. For example to identify the bottle (like Grants Whiskey) and liquid level in the bottle (in 10 percent steps - like 50% full). Is this the problem that can be best solved utilizing some of deep learning frameworks (Tensorflow etc) or some other approach is more effective?
Well, this should be well possible if the liquor is well colored. If not (e.g. gin, vodka), I'd say you have no chance with today's technologies when observing the object from a natural view angle and distance.
For colored liquor, I'd train two detectors. One for detecting the bottle, and a second one to detect the liquor given the bottle. The ratio between the two will be your percentage.
Some of the proven state-of-the-art deep learning-based object detectors (just Google them):
Multibox
YOLO
Faster RCNN
Or non-deep-learning-based:
Deformable part model
EDIT:
I was ask to elaborate more. Here is an example:
The box detector e.g. draws a box in the image at [0.1, 0.2, 0.5, 0.6] (min_height, min_width, max_height, max_width) which is the relative location of your bottle.
Now you crop the bottle from the original image and feed it to the second detector. The second detector draws e.g. [0.2, 0.3, 0.7, 0.8] in your cropped bottle image, the location indicates the fluid it has detected. Now (0.7 - 0.2) * (0.8 - 0.3) = 0.25 is the relative area of the fluid with respect to the area of the bottle, which is what OP is asking for.
EDIT 2:
I entered this reply assuming OP wants to use deep learning. I'd agree other methods should be considered if OP is still unsure with deep learning. For bottle detection, deep learning-based methods have shown to outperform traditional methods by a large margin. Bottle detection happens to be one of the classes in the PASCAL VOC challenge. See results comparison here: http://rodrigob.github.io/are_we_there_yet/build/detection_datasets_results.html#50617363616c20564f43203230313020636f6d7034
For the liquid detection however, deep learning might be slightly overkill. E.g. if you know what color you are looking for, even a simple color filter will give you "something"....
The rule of thumb for deep learning is, if it is visible in the image, hence a expert can tell you the answer solely based on the image then the chances are very high that you can learn this with deep learning, given enough annotated data.
However you are quite unlikely to have the required data needed for such a task, therefore I would ask myself the question if i can simplify the problem. For example you could take gin, vodka and so on and use SIFT to find the bottle again in a new scene. Then RANSAC for bottle detection and cut the bottle out of the image.
Then I would try gradient features to find the edge with the liquid level. Finally you can calculate the percentage with (liquid edge - bottom) / (top bottle - bottom bottle).
Identifying the bottle label should not be hard to do - it's even available "on tap" for cheap (these guys actually use it to identify wine bottle labels on their website): https://aws.amazon.com/blogs/aws/amazon-rekognition-image-detection-and-recognition-powered-by-deep-learning/
Regarding the liquid level, this might be a problem AWS wouldn't be able to solve straight away - but I'm sure it's possible to make a custom CNN for it. Alternatively, you could use good old humans on Amazon Mechanical Turk to do the work for you.
(Note: I do not work for Amazon)

Food and non-food image on Clarifai API

I want to classify food and non-food image by using Clarifai API.
It seems that the API is trying to assume that all images are food images, by default. For example, U pushed an image of people or animals to Clarifai and return back to us the results "water, beer..", etc with very high probability. Is there any way to overcome this problem?.
‘Food’ model is able to analyze images and video around food (even down to the ingredient level)! It’s more speciifc and narrow, while ‘General’ model is used to analyze a more wide range of visual content.

Image Selection for Training Visual Recognition

I am training a classifier for recognizing certain objects in an image. I am using the Watson Visual Recognition API but I would assume that the same question applies to other recognition APIs as well.
I've collected 400 pictures of something - e.g. dogs.
Before I train Watson, I can delete pictures that may throw things off. Should I delete pictures of:
Multiple dogs
A dog with another animal
A dog with a person
A partially obscured dog
A dog wearing glasses
Also, would dogs on a white background make for better training samples?
Watson also takes negative examples. Would cats and other small animals be good negative examples? What else?
You are right that this is a general issue for all kinds of custom classifiers and recognizers - be it vize.it, clarifai, IBM Watson, or training a neural network on your own say in caffe. (Sorted by the number of example images you need to use.)
The important thing you need to ask is how are you going to use the classifier? What are the real images you will feed the machine to predict the objects shown? As a general rule, your training images should be as similar to predict-time images as possible - both in what they depict (kinds and variety of objects) and how they depict it (e.g. backgrounds). Neural networks are super-powerful and if you feed them enough images, they will learn even the hard cases.
Maybe you want to find dog images in user's folders - which will include family photos, screenshots and document scans. Reflect that variety in the training set. Ask the user if a dog with another animal should be tagged as a dog photo.
Maybe you want to find dog images on a wilderness photo trap. Just use various images taken by that photo trap (or several photo traps, if it's a whole network).
In short — tailor your sample images to the task at hand!

What are the types of problems TensorFlow can help solve? [closed]

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The TensorFlow home page describes its purpose as 'a software library for numerical computation'. Looking through the sample problems it looks like a problem is always formulated as follows:
Input
Model parameters
Desired output
Given some training data for 1) and 3), 2) can be computed.
I can see how this can be used to create bots, self-driving cars, image classifiers etc.
Given the broad definition of 'numerical computation', am I missing a class of other problems this can be used for? Can this be used for, say, more classical numerical computations such as the airflow around an aircraft or deformation of a structure under stress? Do you have any examples of how these classical problems would have to be formulated to fit the form above?
A nice discussion on what artificial neural networks could do, the fact that our brain is a neural network might imply that eventually an artificial neural network will be able to to the same tasks.
Some more examples of artificial neural networks used today: music creation, image based location, page rank, google voice, stock trade predictions, nasa star classifiaction, traffic management
Some fields i know of but do not have a good reference for:
optical quantum mechanics test set-up generator
medical diagnosis, reference only about safety
The Sharp LogiCook microwave oven, wiki, nasa mention
I think there are many millions of "problems" that can be solved with an ANN, deciding on the data representation (input,output) will be a challenge for some of these. some useful and useless examples i have been thinking about:
home thermostat that learns your wishes with certain weather types.
bakery production prediction
recognize go-stones on a board and map their locations
personal activity guesser and turn on appropriate device.
recognize person based on mouse movement
Given the right data and network these examples will work.
Dad has a pc controlling the heating system back home, i trained a network based on his 10years of heating data (outside temp, inside temp, humidity etc.) unfortunately i am not allowed to hook it up.
My aunt and uncle have a bakery, based on 6years of sales data i trained a network predicting how many breads and buns they should make. It showed me how important the correct inputs are. first i used the day of the year but when i switched to day of the week i saw a 15% increase in accuracy.
Currently i am working on a network that will detect a go board in a given image and map all 361 locations telling me if there is a black, white or no stone present.
Two examples that showed me how much information can be stored in a single neuron and of different ways to represent data:
Image example, neuron example (unfortunately you have to train both examples yourself so give them a little time.)
On to your example airflow around an aircraft.
I know none to nothing about airflow calculations and my try would be a really huge 3D input layer where you can "draw" an airplane and the direction and speed of the airflow.
It might work but it will require a tremendous amount of computation power, somebody knowing more about this specific topic probably knows a more abstract way of representing the data resulting in a more manageable network.
This nasa paper talks about a neural network for calculating airflow around a wing. Unfortunately i do not understand what kind of input they use, maybe it is more clear to you.