Testing Simplified
This is a module for offline proctoring and analytics of examinations. There are three main modules:
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Facial Recognition: Uses openCV to detect and track faces from the webcam. Can collect, train on, and recognize faces.
Dependencies - openCV, numpy
Steps to run: Delete tempfile from dataset/ Run detect.py first to store 100 each images of subjects Run training.py to train models for facial recognition Run recog.py to detect faces.
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Sentiment Analysis: Uses openCV, tensorflow, and keras to generate predictive models for sentiments and display emotions of person with webcam.
Dependencies - openCV, tensorflow, keras, pandas, numpy
Steps to run: Unzip dataset.zip into folder 'dataset' run trainer.py to generate new model, or use pretrained model.h5 run videoEmot.py to run sentiment analysis.
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Voice-tracking: Records surrounding for a set period of time and generates transcript of spoken words for proctoring.
Dependencies - pyaudio, speech-recognition, threading
Steps to run: Run voice.py to start recording and transcription.