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

$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);

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


$gp->plot(
    topcmds => q{set style textbox opaque fc "#505050f0" noborder},
    square => 1,
    yrange => [$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__

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

  $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);

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

  
  $gp->plot(
      topcmds => q{set style textbox opaque fc "#505050f0" noborder},
      square => 1,
      yrange => [$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;

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 

        # $x scaled by 1e7; filled curve between $y and the x-axis
        $x / 1e7, $y, pdl(0)
    );
}

$gp->end_multi;

$gp->close;

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

# Some code that could be used for working with variants.
1 if <<'COMMENT';

use Bio::DB::HTS::VCF;

my $clinvar_tbi_path = "${clinvar_path}.tbi";
unless( -f $clinvar_tbi_path ) {
    system( qw(tabix), $clinvar_path );

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 

          # $x scaled by 1e7; filled curve between $y and the x-axis
          $x / 1e7, $y, pdl(0)
      );
  }
  
  $gp->end_multi;
  
  $gp->close;
  
  if( IN_IPERL ) {
      IPerl->png( bytestream => path($plot_output_path)->slurp_raw );
  }

B<DISPLAY>:

=for html <span style="display:inline-block;margin-left:1em;"><p><img						src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAIAAAA/et9qAAAgAElEQVR4nOzdd2AUVeIH8Ddb0jshBAIEpSo1GjoIpyAgCOqd3uGdoGBBUQQFRUVBRbkTf9gOBQucqFiwUhSSgJQYCCSBkJBAet1k...

=head2 Parts of the original notebook that fall outside the scope

In the orignal notebook, there are several more steps that have not been ported here:

=over

=item 1.

"Compute contribution scores":

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 

    "dog" => "https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg",
    #by Craig Pemberton, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "apple" => "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg",
    #by Abhijit Tembhekar from Mumbai, India, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
    "banana" => "https://upload.wikimedia.org/wikipedia/commons/1/1c/Bananas_white_background.jpg",
    #by fir0002  flagstaffotos [at] gmail.com		Canon 20D + Tamron 28-75mm f/2.8, GFDL 1.2 <http://www.gnu.org/licenses/old-licenses/fdl-1.2.html>, via Wikimedia Commons
    "turtle" => "https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg",
    #by Claudio Giovenzana, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "flamingo" => "https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg",
    #by Christian Mehlführer, User:Chmehl, CC BY 3.0 <https://creativecommons.org/licenses/by/3.0>, via Wikimedia Commons
    "piano" => "https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png",
    #by "Photo: © Copyright Steinway & Sons", CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "honeycomb" => "https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg",
    #by Merdal, CC BY-SA 3.0 <http://creativecommons.org/licenses/by-sa/3.0/>, via Wikimedia Commons
    "teapot" => "https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg",
    #by Mendhak, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons
);

my @image_names = sort keys %images_for_test_to_uri;

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 

say "All probabilities sum up to approximately 1" if $p_approx_batched->all->sclr;

use Filesys::DiskUsage qw/du/;

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

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

my @solid_channel_uris = (
    'https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Solid_red.svg/480px-Solid_red.svg.png',
    'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Green_00FF00_9x9.svg/480px-Green_00FF00_9x9.svg.png',
    'https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Solid_blue.svg/480px-Solid_blue.svg.png',
);
undef;

__END__

=pod

=encoding UTF-8

=head1 NAME

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 

      "dog" => "https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg",
      #by Craig Pemberton, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "apple" => "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg",
      #by Abhijit Tembhekar from Mumbai, India, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
      "banana" => "https://upload.wikimedia.org/wikipedia/commons/1/1c/Bananas_white_background.jpg",
      #by fir0002  flagstaffotos [at] gmail.com		Canon 20D + Tamron 28-75mm f/2.8, GFDL 1.2 <http://www.gnu.org/licenses/old-licenses/fdl-1.2.html>, via Wikimedia Commons
      "turtle" => "https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg",
      #by Claudio Giovenzana, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "flamingo" => "https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg",
      #by Christian Mehlführer, User:Chmehl, CC BY 3.0 <https://creativecommons.org/licenses/by/3.0>, via Wikimedia Commons
      "piano" => "https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png",
      #by "Photo: © Copyright Steinway & Sons", CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "honeycomb" => "https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg",
      #by Merdal, CC BY-SA 3.0 <http://creativecommons.org/licenses/by-sa/3.0/>, via Wikimedia Commons
      "teapot" => "https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg",
      #by Mendhak, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons
  );
  
  my @image_names = sort keys %images_for_test_to_uri;

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 

                          width => '50%',
                      })
                  ),
              )
          })
      );
  }

B<DISPLAY>:

=for html <span style="display:inline-block;margin-left:1em;"><p><table style="width: 100%"><tr><td><tt>apple</tt></td><td><a href="https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"><img alt="apple" src="https://upload.wikimedia.org/...

=head2 Download the test images and transform them into suitable input data

We now fetch these images and prepare them to be the in the needed format by using C<Imager> to resize and add padding. Then we turn the C<Imager> data into a C<PDL> ndarray. Since the C<Imager> data is stored as 32-bits with 4 channels in the order ...

We then take all the PDL ndarrays and concatenate them. Again, note that the dimension lists for the PDL ndarray and the TFTensor are reversed.

  sub imager_paste_center_pad {
      my ($inner, $padded_sz, @rest) = @_;

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 

  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg
  Rescaled image from [ 543 x 523 ] to [ 224 x 216 ]
  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg
  Rescaled image from [ 3000 x 1999 ] to [ 224 x 149 ]
  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg
  Rescaled image from [ 800 x 600 ] to [ 224 x 168 ]
  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png
  Rescaled image from [ 2059 x 2080 ] to [ 222 x 224 ]
  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg
  Rescaled image from [ 900 x 838 ] to [ 224 x 209 ]
  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/b/b0/Bengal_tiger_%28Panthera_tigris_tigris%29_female_3_crop.jpg
  Rescaled image from [ 4500 x 3000 ] to [ 224 x 149 ]
  Padded to [ 224 x 224 ]
  Downloaded https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg
  Rescaled image from [ 2000 x 1329 ] to [ 224 x 149 ]

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 

                      $probabilities_batched->at($label_index,$batch_idx),
              ) ];
          }
          say generate_table( rows => [ $header, @rows ], header_row => 1 );
          print "\n";
      }
  }

B<DISPLAY>:

=for html <span style="display:inline-block;margin-left:1em;"><p><table style="width: 100%"><tr><td><tt>apple</tt></td><td><a href="https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"><img alt="apple" src="https://upload.wikimedia.org/...

  my $p_approx_batched = $probabilities_batched->sumover->approx(1, 1e-5);
  p $p_approx_batched;
  say "All probabilities sum up to approximately 1" if $p_approx_batched->all->sclr;

B<STREAM (STDOUT)>:

  All probabilities sum up to approximately 1

B<STREAM (STDERR)>:

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN 


B<STREAM (STDOUT)>:

  Disk space usage: 27.45M

=head1 DEBUGGING

The following images can be used to test the C<load_image_to_pdl> function.

  my @solid_channel_uris = (
      'https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Solid_red.svg/480px-Solid_red.svg.png',
      'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Green_00FF00_9x9.svg/480px-Green_00FF00_9x9.svg.png',
      'https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Solid_blue.svg/480px-Solid_blue.svg.png',
  );
  undef;

=head1 CPANFILE

  requires 'AI::TensorFlow::Libtensorflow';
  requires 'AI::TensorFlow::Libtensorflow::DataType';
  requires 'Archive::Extract';
  requires 'Data::Printer';
  requires 'Data::Printer::Filter::PDL';



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