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

      ${ $pdl_raw->get_dataref } = $data;
      $pdl_raw->upd_data;
  
      $pdl_raw;
  }
  
  my @pdl_images = map {
      load_image_to_pdl(
          $images_for_test_to_uri{$_},
          $model_name_to_params{$model_name}{image_size}
      );
  } ($image_names[0]);
  
  my $pdl_image_batched = cat(@pdl_images);
  my $t = Uint8PDLTOTFTensor($pdl_image_batched);
  
  die "There should be 4 dimensions" unless $pdl_image_batched->ndims == 4;
  
  die "With the final dimension of length 1" unless $pdl_image_batched->dim(3) == 1;
  
  p $pdl_image_batched;
  p $t;

=head2 Run the model for inference

We can use the C<Run> method to run the session and get the multiple output C<TFTensor>s. The following uses the names in C<$outputs> mapping to help process the multiple outputs more easily.

  my $RunSession = sub {
      my ($session, $t) = @_;
      my @outputs_t;
  
      my @keys = keys %{ $outputs{out} };
      my @values = $outputs{out}->@{ @keys };
      $session->Run(
          undef,
          [ values %{$outputs{in} } ], [$t],
          \@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;

=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],
  );