I'm using google ml kit with the object detection and tracking api, what I need that when the user capture an image my app will display similar products as the captured on, my question is should I train a model with Tensorflow or whatever to do that or I will not need for a custom model to track the similar products ?
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I'm building Flutter application that the user can upload image (clothes, shoes, etc..) and the app will display similar products as the captured one. so i learned a few about how to build a custom model with Tensorflow to implement it with the google ml kit into flutter app, but i don't know how to build the model to show similar, what i do is image classification that it will simply tells what image for which class belongs!
Example for what i need https://developers.google.com/ml-kit/case-studies/adidas but not in live camera.
any help with that?
I have a Flutter mobile app, using custom Mobilenet object detection model but I want to try using TensorFlow Lite Model Maker.
However I saw we can only use EfficientDet in order to do object detection on the Colab.
So my question is, has anyone ever used EfficientDet_Lite on Flutter ?
Thanks for your help.
I'm using flutter google ml kit in my app but it shows that object detection isn't available on the ios devices but the image labelling does, so I want to know the difference between them, and using this package is totally free right with unlimited requests ?
To know the difference between image labelling and object detection
I am a very newbie to Augmented Reality software. I want to design a simple app. As a part of this app, There will be a series of uniquely designed tags. These tags will be on some assets. In the application, I want to store some metadata for each asset. Imagine a DB table with fields like :(asset Id, name, var1, var2...) holding the asset meta-data.
So, when The augmented reality app detects a unique image then it will show its meta-data information, over the marker. It is that simple. In summary, I want to know how can I use image markers to differentiate assets? Sorry If I am asking a very basic question.
Regards,
Ferda
First of all your question is too broad. How are you planning to implement this application? First, you have to decide whether you will be using an Augmented Reality SDK or computer vision techniques?
My suggestions would be based on amount of devices you want to use this application or target platform, choosing 1 SDK from ARCore, Vuforia or ARKit. I am not familiar with ARKit but in ARCore and Vuforia, either augmented images or image targets are hold in an image database. So you can get the image id or name of any target you detected using your device. In conclusion you can visualize specific assets for specific images.
Below you can see ARCore Augmented Image database. As you can see every image has a name. In your code you can differentiate images using image.Name, then visualize corresponding meta data over the marker.
Also in both SDKs, you can define your own database but your images should not have repetitive features and have high contrast sections.
Vuforia has a similar concept as well. The difference between ARCore and Vuforia depends on what which devices you target and quality of image tracking. Vuforia can detect images better in my opinion. It is really fast in terms of detecting images.
I need some help for finding the person with Camera.
is it possible to fetch person from the groups if he has install applicator in that device from the camera?
the basic concept is when user rotate camera to any person camera will fetch image of that user and will search it and compare it with application database for if that user is register in application or not and display his information.
if its possible and you have some reference then please share with me.
Thank you for read question.
Core Image has a new CIFaceFeature to recognize faces in real time; you can start with these examples to take an overview:
SquareCam (from Apple)
iOS Facial Recognition
Easy Face detection with Core Image
Then you have to design the logic to compare and store images.