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'}
1 maintainer
Metadata
1 maintainer
Release files for proctoring 0.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| proctoring-0.0.1.tar.gz | 38.9 MB | Without provenance | Details for proctoring-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| proctoring-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for proctoring-0.0.1-py3-none-any.whl |
Total release size: 77.8 MB
Release files / proctoring-0.0.1.tar.gz
| Download URL | proctoring-0.0.1.tar.gz |
|---|---|
| Size | 38.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/3.7.1 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.6.2
|
Release files / proctoring-0.0.1-py3-none-any.whl
| Download URL | proctoring-0.0.1-py3-none-any.whl |
|---|---|
| Size | 38.9 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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twine/3.7.1 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.6.2
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