Emotion-detection | Real-time Facial Emotion Detection using deep learning | Computer Vision library

by atulapra Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | Emotion-detection Summary

Emotion-detection is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, OpenCV applications. Emotion-detection has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.

Support

Quality

Security

License

Reuse

Support

Emotion-detection has a medium active ecosystem.

It has 932 star(s) with 418 fork(s). There are 30 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 34 have been closed. On average issues are closed in 28 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Emotion-detection is current.

Quality

Emotion-detection has 0 bugs and 0 code smells.

Security

Emotion-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Emotion-detection code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Emotion-detection is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Emotion-detection releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Emotion-detection saves you 16844 person hours of effort in developing the same functionality from scratch.

It has 33459 lines of code, 2 functions and 3 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Emotion-detection and discovered the below as its top functions. This is intended to give you an instant insight into Emotion-detection implemented functionality, and help decide if they suit your requirements.

Plots the model accuracy .
Convert a string into an integer .

Get all kandi verified functions for this library.

Emotion-detection Key Features

No Key Features are available at this moment for Emotion-detection.

Emotion-detection Examples and Code Snippets

No Code Snippets are available at this moment for Emotion-detection.

Community Discussions

Trending Discussions on Emotion-detection

How to paste one picture on top of another into a defined region

Filter singled out predictions in my simple Python code

What is [0] and (1) in: r = np.random.randint((1), 24000, 1)[0]

How come opencv is creating 2 windows?

QUESTION

How to paste one picture on top of another into a defined region

Asked 2021-Oct-06 at 22:17

I have this simple Python code that captures video from the camera and makes predictions on the emotions of the face (took it from here in case you need to run it).

I like to put this video capture inside this frame (center is transparent) and display all. How can I do this?

...

ANSWER

Answered 2021-Oct-06 at 20:56

Short Implementation of Christoph's great solution in the comments.

Step 1: Use the paint tool to extract the coordinates of the black box region.

you can see the x,y coordinates at the bottom of your image.

Step 2:

a. resize every frame of the video to fit the black region size. width = x1 - x0 and height = y1 - y0.

b. replace the black region pixels with the resized frames.

code:

Source https://stackoverflow.com/questions/69453623

QUESTION

Filter singled out predictions in my simple Python code

Asked 2021-Oct-06 at 20:13

I have this simple Python code that makes predictions on the emotions of the face (took it from here in case you need to run it), and shows it on the rectangle around the face on the camera. But the problem is it has many noises. For instance, Fearful -- Sad -- -- Sad and such. I want to smooth out the predictions and filter out singled out predictions. How can I make a change that if n number of predictions in a row said Sad, then display it as Sad?

You'll only need to change the last few lines as the initial parts are all for predictions.

...

ANSWER

Answered 2021-Oct-05 at 15:01

I'd make a list of predictions and take the mode, something like this:

Source https://stackoverflow.com/questions/69444644

QUESTION

What is [0] and (1) in: r = np.random.randint((1), 24000, 1)[0]

Asked 2021-May-08 at 11:21

What is [0] and (1) in: r = np.random.randint((1), 24000, 1)[0]

Entire github code:

https://github.com/Pawandeep-prog/facial-emotion-detection-webapp/blob/main/facial-detection.py#L230

...

ANSWER

Answered 2021-May-08 at 11:18

Here np is numpy,

(1) denotes the minimum value of output

Source https://stackoverflow.com/questions/67432532

QUESTION

How come opencv is creating 2 windows?

Asked 2020-Apr-27 at 16:03

Found code from Github (https://github.com/atulapra/Emotion-detection) for emotion detection. I want to make some changes in it and want to combine with Tkinter for an easy user interaction. Something like this shown in image below. Here I wrote some code for tk window:

...

ANSWER

Answered 2020-Apr-27 at 16:03

To summarize our discussion into an answer that can be accepted:

Remove cv2.imshow to get rid of the second window
Use img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)) to display the frame with the detected face with the right colors.

Source https://stackoverflow.com/questions/61458770

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Emotion-detection

You can download it from GitHub.
You can use Emotion-detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: