:py:mod:`kwcoco.examples.draw_gt_and_predicted_boxes`
=====================================================

.. py:module:: kwcoco.examples.draw_gt_and_predicted_boxes


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


Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.examples.draw_gt_and_predicted_boxes.draw_true_and_pred_boxes



.. py:function:: draw_true_and_pred_boxes(true_fpath, pred_fpath, gid, viz_fpath)

   How do you generally visualize gt and predicted bounding boxes together?

   .. rubric:: Example

   >>> import kwcoco
   >>> import ubelt as ub
   >>> from os.path import join
   >>> from kwcoco.demo.perterb import perterb_coco
   >>> # Create a working directory
   >>> dpath = ub.ensure_app_cache_dir('kwcoco/examples/draw_true_and_pred_boxes')
   >>> # Lets setup some dummy true data
   >>> true_dset = kwcoco.CocoDataset.demo('shapes2')
   >>> true_dset.fpath = join(dpath, 'true_dset.kwcoco.json')
   >>> true_dset.dump(true_dset.fpath, newlines=True)
   >>> # Lets setup some dummy predicted data
   >>> pred_dset = perterb_coco(true_dset, box_noise=100, rng=421)
   >>> pred_dset.fpath = join(dpath, 'pred_dset.kwcoco.json')
   >>> pred_dset.dump(pred_dset.fpath, newlines=True)
   >>> #
   >>> # We now have our true and predicted data, lets visualize
   >>> true_fpath = true_dset.fpath
   >>> pred_fpath = pred_dset.fpath
   >>> print('dpath = {!r}'.format(dpath))
   >>> print('true_fpath = {!r}'.format(true_fpath))
   >>> print('pred_fpath = {!r}'.format(pred_fpath))
   >>> # Lets choose an image id to visualize and a path to write to
   >>> gid = 1
   >>> viz_fpath = join(dpath, 'viz_{}.jpg'.format(gid))
   >>> # The answer to the question is in the logic of this function
   >>> draw_true_and_pred_boxes(true_fpath, pred_fpath, gid, viz_fpath)