Proctoring library
Project description
AI Based Smart Exam Proctoring Package
It takes image (base64) as input: Provide Output as:
- Detection of Mobile phone.
- Detection of More than 1 person in the exam.
- 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:
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'}
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