An Api for Emotion recognition.

Overview

License: MIT Python 3.7|3.6|3.5|3.4 Deploy

PLAYEMO

Playemo was built from the ground-up with Flask, a python tool that makes it easy for developers to build APIs.


Use Cases

Is Python your language of choice? If so, we have a [fully-supported Python API client] that makes working with the playemo API an easy task!

There are many reasons to use the playemo API. The most common use case is to predict the emotion of a person from a single photograph. However, this can also be used as a facial detection engine which returns a cropped out image of the face detected in a single photograph.!

Authorization

All API requests require the use of an API key

To authenticate an API request, you should provide your the api_key=[API_KEY] as a GET parameter to authorize yourself to the API. But note that this is likely to leave traces in things like your history, if accessing the API through a browser.

GET /?api_key=12345678901234567890123456789012
Parameter Type Description
api_key string Required. Your Playemo API key

Responses

Many API endpoints return the JSON representation of the resources created or edited. However, if an invalid request is submitted, or some other error occurs, Playemo returns a JSON response in the following format:

{
  "error" : string,
  "success" : bool,
  "result"    : string
}

The error attribute contains a message commonly used to indicate errors or, in the case of deleting a resource, success that the resource was properly deleted.

The success attribute describes if the transaction was successful or not.

The result attribute contains any other metadata associated with the response. This will be an escaped string containing JSON data.

Status Codes

Playemo returns the following status codes in its API:

Status Code Description
200 OK
201 CREATED
400 BAD REQUEST
404 NOT FOUND
500 INTERNAL SERVER ERROR

Links

Please don't hesitate to file an issue if you see anything missing.

Screenshots

Home Page

Available Commands

In the project directory, you can run: python--version" : "check python version",

Since tensorflow supports python 3.7,3.6,3.5 or 3.4, i would advice you have python 3.6 installed on your machine.

pip install -r requirements.txt" : "required libaries installed",

This will install the the neccesarry libaries needed to run the application on your machine.

python app.py" : "python-scripts start",

The app is built using Flask so this command Runs the app in Development mode. Open http://localhost:5000 to view it in the browser. The page will reload if you make edits. You will also see any lint errors in the console.

Built With

  • Python
  • Flask
  • Mtcnn
  • TensorFlow
  • Keras
  • CSS
  • HTML

Future Updates

  • A playlist recommendation system based on Emotion predicted

Author

DERHNYEL

🤝 Support

Contributions, issues, and feature requests are welcome!

Give a ⭐️ if you like this project!

Owner
greek geek
greek geek
用强化学习DQN算法,训练AI模型来玩合成大西瓜游戏,提供Keras版本和PARL(paddle)版本

用强化学习玩合成大西瓜 代码地址:https://github.com/Sharpiless/play-daxigua-using-Reinforcement-Learning 用强化学习DQN算法,训练AI模型来玩合成大西瓜游戏,提供Keras版本、PARL(paddle)版本和pytorch版本

72 Dec 17, 2022
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022
The AugNet Python module contains functions for the fast computation of image similarity.

AugNet AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation arxiv link In our work, we propose AugNet, a new deep le

Ming 74 Dec 28, 2022
Official implementation of EfficientPose

EfficientPose This is the official implementation of EfficientPose. We based our work on the Keras EfficientDet implementation xuannianz/EfficientDet

2 May 17, 2022
Using Machine Learning to Create High-Res Fine Art

BIG.art: Using Machine Learning to Create High-Res Fine Art How to use GLIDE and BSRGAN to create ultra-high-resolution paintings with fine details By

Robert A. Gonsalves 13 Nov 27, 2022
Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

66 Dec 15, 2022
An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Semisupervised Multitask Learning This repository is an unofficial and slightly modified implementation of UM-Adapt[1] using PyTorch. This code primar

Abhinav Atrishi 11 Nov 25, 2022
3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

3D AffordanceNet This repository is the official experiment implementation of 3D AffordanceNet benchmark. 3D AffordanceNet is a 3D point cloud benchma

49 Dec 01, 2022
Council-GAN - Implementation for our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020)

Council-GAN Implementation of our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020) Paper Ori Nizan , Ayellet Tal, Breaking the Cycle

ori nizan 260 Nov 16, 2022
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022
Easy to use Python camera interface for NVIDIA Jetson

JetCam JetCam is an easy to use Python camera interface for NVIDIA Jetson. Works with various USB and CSI cameras using Jetson's Accelerated GStreamer

NVIDIA AI IOT 358 Jan 02, 2023
Image Captioning using CNN ,LSTM and Attention

Image Captioning using CNN ,LSTM and Attention This is a deeplearning model which tries to summarize an image into a text . Installation Install this

ASUTOSH GHANTO 1 Dec 16, 2021
Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA)

Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA). Master's thesis documents. Bibliography, experiments and reports.

Erick Cobos 73 Dec 04, 2022
Iranian Cars Detection using Yolov5s, PyTorch

Iranian Cars Detection using Yolov5 Train 1- git clone https://github.com/ultralytics/yolov5 cd yolov5 pip install -r requirements.txt 2- Dataset ../

Nahid Ebrahimian 22 Dec 05, 2022
Jiminy Cricket Environment (NeurIPS 2021)

Jiminy Cricket This is the repository for "What Would Jiminy Cricket Do? Towards Agents That Behave Morally" by Dan Hendrycks*, Mantas Mazeika*, Andy

Dan Hendrycks 15 Aug 29, 2022
(3DV 2021 Oral) Filtering by Cluster Consistency for Large-Scale Multi-Image Matching

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching (3DV 2021 Oral Presentation) Filtering by Cluster Consistency (FCC) is a very

Yunpeng Shi 11 Sep 28, 2022
Contextualized Perturbation for Textual Adversarial Attack, NAACL 2021

Contextualized Perturbation for Textual Adversarial Attack Introduction This is a PyTorch implementation of Contextualized Perturbation for Textual Ad

cookielee77 30 Jan 01, 2023
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

Cristian Challu 82 Jan 04, 2023
Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021)

Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021) This repository contains the code for our ICCV2021 paper by Jia-Ren Cha

Jia-Ren Chang 40 Dec 27, 2022