robokudo.utils.non_maxima_suppression
=====================================

.. py:module:: robokudo.utils.non_maxima_suppression

.. autoapi-nested-parse::

   Implements Non-Maxima-Suppression (NMS).

   Non-Maxima-Suppression (NMS) is a technique used to reduce the number of bounding boxes
   in an image. It is used to remove redundant overlapping boxes that are likely to be
   detections of the same object. This is done by selecting the box with the highest
   confidence score and removing all other boxes that overlap with it more than a given
   threshold. This process is repeated until there are no more boxes left.

   Author: Lennart Heinbokel
   Created on: 2023-02-02



Functions
---------

.. autoapisummary::

   robokudo.utils.non_maxima_suppression._iou
   robokudo.utils.non_maxima_suppression.non_max_suppression_
   robokudo.utils.non_maxima_suppression.class_based_nms


Module Contents
---------------

.. py:function:: _iou(box1, box2)

   Calculates the intersection over union of two bounding boxes

   Args:
       box1: bounding box 1
       box2: bounding box 2

   Returns:
       intersection over union of the two bounding boxes


.. py:function:: non_max_suppression_(predictions, confidence_treshold=0.5, iou_threshold=0.4)

   Performs non-maxima suppression (NMS) on inference results

   Args:
       predictions: list containing tuples of bounding boxes, class probabilities and class labels
       confidence_tresholdhold: confidence threshold for keeping bounding boxes
       iou_threshold: intersection over union threshold for indicating whether two boxes overlap

   Returns:
       list of non-max suppressed bounding boxes, class probabilities and class labels


.. py:function:: class_based_nms(predictions, confidence_threshold=0.5, iou_threshold=0.4)

   Performs non-maximum suppression (NMS) on inference results, filtering only
       overlapping boxes of the same class. This is done to avoid removing boxes of
       different classes that overlap with each other.

   Args:
       predictions: list containing tuples of bounding boxes, class probabilities and class labels
       confidence_threshold: confidence threshold for keeping bounding boxes
       iou_threshold: intersection over union threshold for indicating whether two boxes overlap

   Returns:
       list of non-maximum suppressed bounding boxes, class probabilities and class labels