AI-TensorFlow-Libtensorflow

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

$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);

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

        (
            [ 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],
);

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

  
  $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);

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

          (
              [ 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],
  );

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

my @tracks = (
    [ 'DNASE:CD14-positive monocyte female' =>   41 => $predictions_p->slice('(41)') ],
    [ 'DNASE:keratinocyte female'           =>   42 => $predictions_p->slice('(42)') ],
    [ 'CHIP:H3K27ac:keratinocyte female'    =>  706 => $predictions_p->slice('(706)')],
    [ 'CAGE:Keratinocyte - epidermal'       => 4799 => log10(1 + $predictions_p->slice('(4799)')) ],
);

use PDL::Graphics::Gnuplot;

my $plot_output_path = 'enformer-target-interval-tracks.png';
my $gp = gpwin('pngcairo', font => ",10", output => $plot_output_path, size => [10,2. * @tracks], aa => 2 );

$gp->multiplot( layout => [1, scalar @tracks], title => $target_interval );

$gp->options(
    offsets => [ graph => "0.01, 0, 0, 0" ],
    lmargin => "at screen 0.05",
);

my $x = zeroes($predictions_p->dim(1))->xlinvals($target_interval->start, $target_interval->end);

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

  my @tracks = (
      [ 'DNASE:CD14-positive monocyte female' =>   41 => $predictions_p->slice('(41)') ],
      [ 'DNASE:keratinocyte female'           =>   42 => $predictions_p->slice('(42)') ],
      [ 'CHIP:H3K27ac:keratinocyte female'    =>  706 => $predictions_p->slice('(706)')],
      [ 'CAGE:Keratinocyte - epidermal'       => 4799 => log10(1 + $predictions_p->slice('(4799)')) ],
  );
  
  use PDL::Graphics::Gnuplot;
  
  my $plot_output_path = 'enformer-target-interval-tracks.png';
  my $gp = gpwin('pngcairo', font => ",10", output => $plot_output_path, size => [10,2. * @tracks], aa => 2 );
  
  $gp->multiplot( layout => [1, scalar @tracks], title => $target_interval );
  
  $gp->options(
      offsets => [ graph => "0.01, 0, 0, 0" ],
      lmargin => "at screen 0.05",
  );
  
  my $x = zeroes($predictions_p->dim(1))->xlinvals($target_interval->start, $target_interval->end);