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Build AI Powered Emoji Detector
by kandikits Updated: Oct 20, 2022
AI-powered emoji detectors can be used by companies to increase engagement with their customers. They can create personalized content for their users, which will help them in building strong relationships 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 a 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.
Deployment Information
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.
- Download, extract and double-click the kit installer file to install the kit.
- After successful installation of the kit, locate the zip file 'Emojinator.zip'.
- Extract the zip file and navigate to the directory 'Emojinator'.
- Open the command prompt in the extracted directory 'Emojinator' and run the command 'jupyter notebook'.
- Locate and open the 'Emojinator-notebook.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Emoji Detector App.
For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community
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.
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.
opencv-pythonby opencv
Shell 2567 Version:63
Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.
opencv-pythonby opencv
Shell 2567 Version:63 License: Others (Non-SPDX)
Data Analysis/Manipulation
These libraries help in analyzing data and doing data manipulations.
pandasby pandas-dev
Python 33259 Version:v1.4.1
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
matplotlibby matplotlib
Python 15355 Version:v3.5.1
matplotlib: plotting with Python
matplotlibby matplotlib
Python 15355 Version:v3.5.1 License: No License
Machine Learning
Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution.
tensorflowby tensorflow
C++ 164372 Version:v2.9.0-rc1
An Open Source Machine Learning Framework for Everyone
tensorflowby tensorflow
C++ 164372 Version:v2.9.0-rc1 License: Permissive (Apache-2.0)
Utilities
The below utility library helps in storing huge amounts of numerical data and manipulate that data easily from NumPy.
Kit Solution Source
Emojinatorby kandi1clickkits
Python 0 Version:v1.0.0
A simple emoji classifier for humans. 🖖
Emojinatorby kandi1clickkits
Python 0 Version:v1.0.0 License: Permissive (MIT)
Support
If you need help using this kit, you may reach us at the OpenWeaver Community.