AI-powered emoji detectors can help increase engagement with their customers. It will help them to build 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.
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.
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.
Shell 3491 Version:72 License: Permissive (MIT)
These libraries help in analyzing data and doing data manipulations.
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution.
The below utility library helps in storing huge amounts of numerical data and manipulate that data easily from NumPy.