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lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod  view on Meta::CPAN

        \@values, \@outputs_t,
        undef,
        undef,
        $s
    );
    AssertOK($s);

    return { mesh \@keys, \@outputs_t };
};

undef;

my $tftensor_output_by_name = $RunSession->($session, $t);

my %pdl_output_by_name = map {
    $_ => FloatTFTensorToPDL( $tftensor_output_by_name->{$_} )
} keys $tftensor_output_by_name->%*;

undef;

my $min_score_thresh = 0.30;

my $which_detect = which( $pdl_output_by_name{detection_scores} > $min_score_thresh );

my %subset;

$subset{detection_boxes}   = $pdl_output_by_name{detection_boxes}->dice('X', $which_detect);
$subset{detection_classes} = $pdl_output_by_name{detection_classes}->dice($which_detect);
$subset{detection_scores}  = $pdl_output_by_name{detection_scores}->dice($which_detect);

$subset{detection_class_labels}->@* = map { $labels_map{$_} } $subset{detection_classes}->list;

p %subset;

use PDL::Graphics::Gnuplot;

my $plot_output_path = 'objects-detected.png';
my $gp = gpwin('pngcairo', font => ",12", output => $plot_output_path, aa => 2, size => [10] );

my @qual_cmap = ('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6');

$gp->options(
    map {
        my $idx = $_;
        my $lc_rgb = $qual_cmap[ $subset{detection_classes}->slice("($idx)")->squeeze % @qual_cmap ];

        my $box_corners_yx_norm = $subset{detection_boxes}->slice([],$idx,[0,0,0]);
        $box_corners_yx_norm->reshape(2,2);

        my $box_corners_yx_img = $box_corners_yx_norm * $pdl_images[0]->shape->slice('-1:-2');

        my $from_xy = join ",", $box_corners_yx_img->slice('-1:0,(0)')->list;
        my $to_xy   = join ",", $box_corners_yx_img->slice('-1:0,(1)')->list;
        my $label_xy = join ",", $box_corners_yx_img->at(1,1), $box_corners_yx_img->at(0,1);

        (
            [ object => [ "rect" =>
                from => $from_xy, to => $to_xy,
                qq{front fs empty border lc rgb "$lc_rgb" lw 5} ], ],
            [ label => [
                sprintf("%s: %.1f",
                    $subset{detection_class_labels}[$idx],
                    100*$subset{detection_scores}->at($idx,0) ) =>
                at => $label_xy, 'left',
                offset => 'character 0,-0.25',
                qq{font ",12" boxed front tc rgb "#ffffff"} ], ],
        )
    } 0..$subset{detection_boxes}->dim(1)-1
);

$gp->plot(
    topcmds => q{set style textbox opaque fc "#505050f0" noborder},
    square => 1,
    yrange => [$pdl_images[0]->dim(2),0],
    with => 'image', $pdl_images[0],
);

$gp->close;

IPerl->png( bytestream => path($plot_output_path)->slurp_raw ) if IN_IPERL;

use Filesys::DiskUsage qw/du/;

my $total = du( { 'human-readable' => 1, dereference => 1 },
    $model_archive_path, $model_base );

say "Disk space usage: $total"; undef;

__END__

=pod

=encoding UTF-8

=head1 NAME

AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubCenterNetObjDetect - Using TensorFlow to do object detection using a pre-trained model

=head1 SYNOPSIS

The following tutorial is based on the L<TensorFlow Hub Object Detection Colab notebook|https://www.tensorflow.org/hub/tutorials/tf2_object_detection>. It uses a pre-trained model based on the I<CenterNet> architecture trained on the I<COCO 2017> dat...

Some of this code is identical to that of C<InferenceUsingTFHubMobileNetV2Model> notebook. Please look there for an explanation for that code. As stated there, this will later be wrapped up into a high-level library to hide the details behind an API.

=head1 COLOPHON

The following document is either a POD file which can additionally be run as a Perl script or a Jupyter Notebook which can be run in L<IPerl|https://p3rl.org/Devel::IPerl> (viewable online at L<nbviewer|https://nbviewer.org/github/EntropyOrg/perl-AI-...

=over

=item *

C<PDL::Graphics::Gnuplot> requires C<gnuplot>.

=back

If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.

=head1 TUTORIAL

=head2 Load the library

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod  view on Meta::CPAN

  };
  
  undef;



  my $tftensor_output_by_name = $RunSession->($session, $t);
  
  my %pdl_output_by_name = map {
      $_ => FloatTFTensorToPDL( $tftensor_output_by_name->{$_} )
  } keys $tftensor_output_by_name->%*;
  
  undef;

=head2 Results summary

Then we use a score threshold to select the objects of interest.

  my $min_score_thresh = 0.30;
  
  my $which_detect = which( $pdl_output_by_name{detection_scores} > $min_score_thresh );
  
  my %subset;
  
  $subset{detection_boxes}   = $pdl_output_by_name{detection_boxes}->dice('X', $which_detect);
  $subset{detection_classes} = $pdl_output_by_name{detection_classes}->dice($which_detect);
  $subset{detection_scores}  = $pdl_output_by_name{detection_scores}->dice($which_detect);
  
  $subset{detection_class_labels}->@* = map { $labels_map{$_} } $subset{detection_classes}->list;
  
  p %subset;

The following uses the bounding boxes and class label information to draw boxes and labels on top of the image using Gnuplot.

  use PDL::Graphics::Gnuplot;
  
  my $plot_output_path = 'objects-detected.png';
  my $gp = gpwin('pngcairo', font => ",12", output => $plot_output_path, aa => 2, size => [10] );
  
  my @qual_cmap = ('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6');
  
  $gp->options(
      map {
          my $idx = $_;
          my $lc_rgb = $qual_cmap[ $subset{detection_classes}->slice("($idx)")->squeeze % @qual_cmap ];
  
          my $box_corners_yx_norm = $subset{detection_boxes}->slice([],$idx,[0,0,0]);
          $box_corners_yx_norm->reshape(2,2);
  
          my $box_corners_yx_img = $box_corners_yx_norm * $pdl_images[0]->shape->slice('-1:-2');
  
          my $from_xy = join ",", $box_corners_yx_img->slice('-1:0,(0)')->list;
          my $to_xy   = join ",", $box_corners_yx_img->slice('-1:0,(1)')->list;
          my $label_xy = join ",", $box_corners_yx_img->at(1,1), $box_corners_yx_img->at(0,1);
  
          (
              [ object => [ "rect" =>
                  from => $from_xy, to => $to_xy,
                  qq{front fs empty border lc rgb "$lc_rgb" lw 5} ], ],
              [ label => [
                  sprintf("%s: %.1f",
                      $subset{detection_class_labels}[$idx],
                      100*$subset{detection_scores}->at($idx,0) ) =>
                  at => $label_xy, 'left',
                  offset => 'character 0,-0.25',
                  qq{font ",12" boxed front tc rgb "#ffffff"} ], ],
          )
      } 0..$subset{detection_boxes}->dim(1)-1
  );
  
  $gp->plot(
      topcmds => q{set style textbox opaque fc "#505050f0" noborder},
      square => 1,
      yrange => [$pdl_images[0]->dim(2),0],
      with => 'image', $pdl_images[0],
  );
  
  $gp->close;
  
  IPerl->png( bytestream => path($plot_output_path)->slurp_raw ) if IN_IPERL;

=head1 RESOURCE USAGE

  use Filesys::DiskUsage qw/du/;
  
  my $total = du( { 'human-readable' => 1, dereference => 1 },
      $model_archive_path, $model_base );
  
  say "Disk space usage: $total"; undef;

=head1 CPANFILE

  requires 'AI::TensorFlow::Libtensorflow';
  requires 'AI::TensorFlow::Libtensorflow::DataType';
  requires 'Archive::Extract';
  requires 'Data::Printer';
  requires 'Data::Printer::Filter::PDL';
  requires 'FFI::Platypus::Buffer';
  requires 'FFI::Platypus::Memory';
  requires 'File::Which';
  requires 'Filesys::DiskUsage';
  requires 'HTML::Tiny';
  requires 'HTTP::Tiny';
  requires 'Imager';
  requires 'List::Util', '1.56';
  requires 'PDL';
  requires 'PDL::Graphics::Gnuplot';
  requires 'Path::Tiny';
  requires 'Syntax::Construct';
  requires 'Text::Table::Tiny';
  requires 'URI';
  requires 'constant';
  requires 'feature';
  requires 'lib::projectroot';
  requires 'strict';
  requires 'utf8';
  requires 'warnings';

=head1 AUTHOR

Zakariyya Mughal <zmughal@cpan.org>