basegun-ml 1.0.1

A deeplearning package for the basegun weapon recongition app

Project description

Basegun_ml is a deeplearning package for the basegun weapon recongition app.

Usage

Classification

from basegun_ml.classification import get_typology, list_typologies
#After the import the model is already warmed-up for faster inference

#Convert image to bytes
with open("test.jpg", "rb") as file:
    image_bytes = file.read()

#Prediction of the weapon typology
typology,confidence_score,confidence_level=get_typology(image_bytes)

#Obtain the list of the different typologies
list_typologies()

Variables description

typology: it corresponds to the weapon class predicted from the image. The list of typologies can be obtained from the function

confidence_score: it corresponds to the confidence of the class prediction of the algorithm, the closer to 1 to more confident is the prediction

confidence_level: there are 3 level of confidence defined. According to this performance level the basegun user will have more information.

Measure length

from basegun_ml.measure import get_lengths

#Convert image to bytes
with open("test.jpg", "rb") as file:
    image_bytes = file.read()

#Get lengths
weapon_length,barrel_length,confidence_card=get_lengths(image_bytes)

Variables description

weapon_length: it corresponds to the weapon overall length predicted from the image.

barrel_length: it corresponds to the barrel length of the weapon predicted from the image.

confidence_card: it corresponds to the confidence score for the card prediction. A card is used as a reference for the measure module

Tests

Tests are available for the classification task and the measure length task

pytest tests/test_classification.py 
pytest tests/test_measure.py

Credits

• This project uses the Ultralytics Library
• The oriented bounding box detection is inspired from this YOLOV5 implementation