AI Based Smart Exam Proctoring Package

Related tags

Deep Learningproctoringpipgaze-trackinggaze-estimationproctorexamproctored-assessmentproctoring-ai
Overview

AI Based Smart Exam Proctoring Package

It takes image (base64) as input: Provide Output as:

  1. Detection of Mobile phone.
  2. Detection of More than 1 person in the exam.
  3. Gaze Estimation: Estimating the position of student body & eyes movements.

DOWNLOAD LINK OF YOLO V3 MODEL:

https://pjreddie.com/media/files/yolov3.weights

DOWNLOAD LINK OF shape_predictor_68_face_landmarks.dat MODEL:

https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat?raw=true

Install this package:

pip install proctoring

Code Sample Working

from proctoring.proctoring import get_analysis, yolov3_model_v3_path

# insert the path of yolov3 model [mandatory]
yolov3_model_v3_path("yolov3.weights_model_path")

# insert the image of base64 format
imgData = "base64_image_format"
proctorData = get_analysis(imgData, "shape_predictor_68_face_landmarks.dat_model_path")
print(proctorData)

Code Sample Output

{'mob_status': 'Not Mobile Phone detected', 'person_status': 'Normal', 'user_move1': 'Head up', 'user_move2': 'Head right', 'eye_movements': 'Blinking'}

LICENSE

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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