I want to build an app that will recognize what emojis have been used on the wallpaper.
So for instance this app will receive on input:
And on output should array of names of recognizing emojis return:
[
"Smiling Face with Sunglasses",
"Grinning Face with Smiling Eyes",
"Kissing Face with Closed Eyes"
]
Of course, the names of these emojis will come from the names of files of training images.
For example this file:
It will be called Grinning_Face_with_Smiling_Eyes.jpg
I would like to use AWS Rekognition Label or Google AutoML Vision, but they require a minimum of 10 images of each emoji for training.
As you know, I can only provide one image of each emoji, because there is no more option, they are in 2D ;)
Now my question is:
What should I do? How can I skip these requirements? Which service should I choose?
PS. In real business instead of emojis, there are covers of the books, which AI has to recognize. There is also one image per book-cover photo in 2D.
Related
I kind of reverse engineered an image filter. Actually it was just a pixel by pixel operation so I applied it over different images and by comparing each pixel of original image and filtered image (using PIL) I now know what RGB value from original image became what RGB in the filtered image. For example like RGB(0,0,1) became RGB(2,3,81) (suppose) etc and I know this for all 16,777,216 colors.
If what I did is correct then my question is how can I create a filter out of this data that can be used in flutter apps. One option is to use conditional statements but that's just theoretical as I'll have to manually write 16,777,216 statements just for this one filter. So is there any software or program or code or anything else that I can use to create a filter out of this data that can be used in flutter apps. This is important because ultimately I want to use this filter in my flutter app.
Any help would be much appreciated.
Thank you very much.
I want to build an app that will recognize what emojis have been used on the wallpaper.
So for instance this app will receive on input:
And on output should array of names of recognizing emojis return:
[
"Smiling Face with Sunglasses",
"Grinning Face with Smiling Eyes",
"Kissing Face with Closed Eyes"
]
I have prepared training data that consists of individual emojis.
For instance, I have rotated each emoji by 30 degrees, cut it by half, etc.
After training the model, the average precision is 0.8, which is quite nice, but it only works for one emoji per wallpaper. If I upload many types of emojis on one wallpaper, it does not recognize any objects.
My question is: why does it recognize one type of emoji per wallpaper, but if I put many types of emojis on one wallpaper, it does not work?
I am using Google ML Vision, and I have chosen Multi-Label Classification for the data set.
I am working on palmprint authentication, i have captured the palm images and have done the preprocessing and the ROI extraction.
Now i have to extract features from the ROI such as 'principle lines' and then later use this for matching.
So how do i extract these features and find accuracy of matching using these features? Any suggestion or the code regarding this shall be appreciated.
Captured palm image
ROI of palm
You have to enhance principal edges ; you can use firangi filter which is very good at this job
here is link to get code
http://www.mathworks.com/matlabcentral/fileexchange/24409-hessian-based-frangi-vesselness-filter/content/FrangiFilter2D.m
I have tried it on you image here is results
you can change options to get more refined results .I would like to mention that this is just extraction /enhancement of edges not features .I presume that this is your requirement.If you want feature extraction from your palm image i would suggest use
'local binary pattern'
https://en.wikipedia.org/wiki/Local_binary_patterns
I need to draw a map of the core metabolism of E.coli. Associated with each reaction in the map I have a number that indicates the flux through this reaction. I want the map to reflect these fluxes through the color of each reaction in the map.
I have tried using tools like Mathematica and Cytoscape, but it is very hard to get a nice layout of the metabolic network. I have seen maps of E.coli metabolism which look very nice on paper. What I need is a map like these, but where I can define the colors of the reactions.
There are some tools available, for example, metdraw.com. But when I upload my E.coli SBML model, the plot layout is a disaster. There used to be a web IPython notebook that one could use for some prebuilt models, where you just had to input the reaction fluxes. But now it's gone: http://nbviewer.ipython.org/github/opencobra/cobrapy/blob/master/documentation_builder/visbio.ipynb
See the image below for an example. Forget about the yellow bounding boxes delimiting compartments. I can spare those.
Some tracking of the broken link you posted brings me to Escher, which appears to be what visbio is now called:
https://github.com/zakandrewking/escher
For example see:
https://cobrapy.readthedocs.org/en/latest/escher.html
Escher is part of Cobrapy:
http://opencobra.github.io/cobrapy/
A software suite to model biological networks.
I'm a newbie to Matlab. I'm basically attempting to manually segment a set of images and then manually label those segments also. I looked into the imfreehand(), but I'm unable to do this using imfreehand().
Basically, I want to follow the following steps :
Manually segment various ROIs on the image (imfreehand only lets me draw one segment I think?)
Assign labels to all those segments
Save the segments and corresponding labels to be used further (not sure what format they would be stored in, I think imfreehand would give me the position and I could store that along with the labels?)
Hopefully use these labelled segments in the images to form a training dataset for a neural network.
If there is some other tool or software which would help me do this, then any pointers would be very much appreciated. (Also I am new to stackoverflow, so if there is any way I could improve on the question to make it clearer, please let me know!) Thanks!
Derek Hoiem, a computer vision research at the University of Illinois, wrote an object labelling tool which does pretty much exactly what you asked for. You can download it from his page:
http://www.cs.illinois.edu/homes/dhoiem/software/index.html