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Build AI Powered Emoji Detector
by kandikits
AI powered emoji detector can be used by the companies to increase engagement with their customers. They can create personalized content for their users, which will help them in building a strong relationship with their customers. The emoji detector will help you in analyzing your audience and their preferences so that you can deliver the right content. You can also use the technology to provide customer support to your customers by providing customized answers.
One of the most important aspects of AI Powered Emoji Detector is that it will help you in detecting any kind of emotions and expressions on your face OR hand gestures from web camera. It will help in detecting whether you are happy, sad or angry and so on. This technology is also used for predicting different kinds of expressions like happiness, fear, sadness etc.
kandi kit provides you with a fully deployable Use of AI Powered Emoji Detector. Source code included so that you can customize it for your requirement.
Training and Certification - How to build AI Powered Emoji Detector
Watch this self-guided tutorial on installing dependencies, loading pre-trained model, and testing real-time emoji detection to build your own AI-powered Emoji Detector.
Completed the training? Apply for your Participation Certificate and Achievement Certificate now!
Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source Champion and get a verified badge.
Kit Deployment Instructions
Follow below instructions to deploy and run the solution.
1. Download, extract and double-click kit installer file to install the kit.
2. After successful installation of the kit, locate the zip file 'Emojinator.zip'
3. Extract the zip file and navigate to the directory 'Emojinator'
4. Open command prompt in the extracted directory 'Emojinator' and run the command 'jupyter notebook'
5. Locate and open the 'Emojinator-notebook.ipynb' notebook from the Jupyter Notebook browser window.
6. Execute cells in the notebook
Note: Demo source code will be downloaded to local machine. It is also available here
Development Environment
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
Jupyter Notebook is used for our development.
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Image Preparation and Processing
These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm.
We use opencv library for capturing frames from live streaming videocam.
Data Analysis/Manipulation
These libraries help in analyzing data and doing data manipulations.
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Machine Learning
Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution.
Utilities
The below utility library helps in storing huge amounts of numerical data and manipulate that data easily from NumPy.
Kit Solution Source
Hand Emoji Detector created using this kit are added in this section.The entire solution is available as a package to download from the source code repository.
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Support
If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.