Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/.
This is a crypto trading bot that scans the Binance Annoucements page for new coins, and places trades on Gateio.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Applications
Subcategory
Other
Keywords
Computer vision, Emotions
Stars
1,242
Forks
371
Contributors
5
Created date
2018-08-25
Updated date
2026-10-13
Stars in 1 day
0
Stars in 7 days
1
Rank
10,999
Previous rank
10,963
Rank change
-36
7-day stars rank
10,032
Previous 7-day stars rank
10,072
7-day stars rank change
40
Total stars rank
9,434
Previous total stars rank
9,421
Total stars rank change
-13
Country
Egypt
README summary
Project Name : Emotion-recognition Table of Content : 1. Dataset Description: Our Human face is having a mixed emotions so we are to demonstrate the probabilities of these emotions that we have. Emotion recognition is a technique used in software that allows a program to "read" the emotions on a human face using advanced image processing. Companies have been experimenting with combining sophisticated algorithms with image processing techniques that have emerged in the past ten years to understand more about what
an image or a video of a person's face tells us about how he/she is feeling and not just that but also showing the probabilities of mixed emotions a face could has. Installations: Install dependencies using requirements. txt Usage: The program will creat a window to display the scene capture by webcamera and a window representing the probabilities of detected emotions. py You can just use this with the provided pretrained model i have included in the path written
in the code file, i have choosen this specificaly since it scores the best accuracy, feel free to choose any but in this case you have to run the later file train_emotion_classifier If you just want to run this demo, the following content can be skipped Train python train_emotion_classifier. py Dataset: I have used this dataset Download it and put the csv in fer2013/fer2013/ -fer2013 emotion classification test accuracy: 66% Credits This work is inspired
from this great work and the resources of Adrian Rosebrock helped me alot! Ongoing Draw emotions faces next to the detected face. Issues & Suggestions If any issues and suggestions to me, you can create an issue. If you like this work please help me by giving me some stars.
Frequently asked questions
How many stars and forks are recorded?
otaha178/Emotion-recognition has 1,242 stars and 371 forks.
What is the repository rank?
otaha178/Emotion-recognition is ranked 10,999.
What language and category are recorded?
otaha178/Emotion-recognition has 1,242 stars, uses Python and belongs to Applications.
Which repository is closest in rank?
otaha178/Emotion-recognition is compared with 0xacx/chatGPT-shell-cli, ranked 11,002 with 1,241 stars.
Our Human face is having a mixed emotions so we are to demonstrate the probabilities of these emotions that we have.
What does Emotion Recognition mean?
Emotion recognition is a technique used in software that allows a program to "read" the emotions on a human face using advanced image processing. Companies have been experimenting with combining sophisticated algorithms with image processing techniques that have emerged in the past ten years to understand more about what an image or a video of a person's face tells us about how he/she is feeling and not just that but also showing the probabilities of mixed emotions a face could has.
Installations:
Install dependencies using requirements.txt
pip install -r requirements.txt
Usage:
The program will creat a window to display the scene capture by webcamera and a window representing the probabilities of detected emotions.
Demo
python real_time_video.py
You can just use this with the provided pretrained model i have included in the path written in the code file, i have choosen this specificaly since it scores the best accuracy, feel free to choose any but in this case you have to run the later file train_emotion_classifier
If you just want to run this demo, the following content can be skipped
