:py:mod:`kwimage.algo._nms_backend.py_nms`
==========================================

.. py:module:: kwimage.algo._nms_backend.py_nms

.. autoapi-nested-parse::

   Fast R-CNN
   Copyright (c) 2015 Microsoft
   Licensed under The MIT License [see LICENSE for details]
   Written by Ross Girshick



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


Functions
~~~~~~~~~

.. autoapisummary::

   kwimage.algo._nms_backend.py_nms.py_nms



.. py:function:: py_nms(np_ltrb, np_scores, thresh, bias=1)

   Pure Python NMS baseline.

   .. rubric:: References

   https://github.com/rbgirshick/fast-rcnn/blob/master/lib/utils/nms.py

   .. rubric:: Example

   >>> np_ltrb = np.array([
   >>>     [0, 0, 100, 100],
   >>>     [0, 0, 100, 100],
   >>>     [100, 100, 10, 10],
   >>>     [10, 10, 100, 100],
   >>>     [50, 50, 100, 100],
   >>>     [100, 100, 150, 101],
   >>>     [120, 100, 180, 101],
   >>>     [150, 100, 200, 101],
   >>> ], dtype=np.float32)
   >>> np_scores = np.linspace(0, 1, len(np_ltrb))
   >>> thresh = 0.1
   >>> bias = 0.0
   >>> keep = sorted(py_nms(np_ltrb, np_scores, thresh, bias))
   >>> print('keep = {!r}'.format(keep))
   keep = [2, 4, 5, 7]

   .. rubric:: Example

   >>> from kwimage.algo._nms_backend.py_nms import *  # NOQA
   >>> np_ltrb = np.array([
   >>>     [0, 0, 100, 100],
   >>>     [100, 100, 10, 10],
   >>>     [10, 10, 100, 100],
   >>>     [50, 50, 100, 100],
   >>> ], dtype=np.float32)
   >>> np_scores = np.array([.1, .5, .9, .1])
   >>> keep = list(py_nms(np_ltrb, np_scores, thresh=0.0, bias=1.0))
   >>> print('keep@0.0 = {!r}'.format(keep))
   >>> keep = list(py_nms(np_ltrb, np_scores, thresh=0.2, bias=1.0))
   >>> print('keep@0.2 = {!r}'.format(keep))
   >>> keep = list(py_nms(np_ltrb, np_scores, thresh=0.5, bias=1.0))
   >>> print('keep@0.5 = {!r}'.format(keep))
   >>> keep = list(py_nms(np_ltrb, np_scores, thresh=1.0, bias=1.0))
   >>> print('keep@1.0 = {!r}'.format(keep))
   keep@0.0 = [2, 1]
   keep@0.2 = [2, 1]
   keep@0.5 = [2, 1, 3]
   keep@1.0 = [2, 1, 3, 0]