Module: MachineLearningWorkbench::Tools::Imaging

Defined in:
lib/machine_learning_workbench/tools/imaging.rb

Constant Summary collapse

Forkable = 
MachineLearningWorkbench::Tools::Execution
Norm = 
MachineLearningWorkbench::Tools::Normalization

Class Method Summary collapse

Class Method Details

.display(nmat, disp_size: [300, 300], shape: nil, in_fork: true) ⇒ Object

Show a NMatrix as image in a RMagick window

Parameters:

  • disp_size (defaults to: [300, 300]) —

    the size of the image to display

  • shape (defaults to: nil) —

    the true shape of the image (NMatrix could be flattened)

  • in_fork (bool) (defaults to: true) —

    whether to execute the display in fork (and continue running)



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# File 'lib/machine_learning_workbench/tools/imaging.rb', line 24

def self.display nmat, disp_size: [300, 300], shape: nil, in_fork: true
  img = nmat_to_img(nmat, shape: shape).resize(*disp_size)
  if in_fork
    MachineLearningWorkbench::Tools::Execution.in_fork { img.display }
  else
    img.display
  end
end

.nmat_from_png(fname, scale: nil, flat: false, dtype: nil) ⇒ Object

Create NMatrix from png by filename.

Parameters:

  • fname —

    the file name

  • scale (defaults to: nil) —

    optional rescaling of the image

  • flat (bool) (defaults to: false) —

    whether to return a flat array

  • dtype (defaults to: nil) —

    dtype for the NMatrix, leave ‘nil` for automatic detection



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# File 'lib/machine_learning_workbench/tools/imaging.rb', line 38

def self.nmat_from_png fname, scale: nil, flat: false, dtype: nil
  img = Magick::ImageList.new(fname).first
  img.scale!(scale) if scale
  shape = [img.columns, img.rows]
  pixels = img.export_pixels(0, 0, *shape, 'I') # 'I' for intensity
  raise "Sanity check" unless shape.reduce(:*)==pixels.size
  return pixels.to_nm(nil, dtype) if flat
  NMatrix.new shape, pixels, dtype: dtype
end

.nmat_to_img(nmat, shape: nil) ⇒ Object

Create RMagick::Image from NMatrix data



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# File 'lib/machine_learning_workbench/tools/imaging.rb', line 7

def self.nmat_to_img nmat, shape: nil
  shape ||= nmat.shape
  shape = [1, shape] if shape.kind_of?(Integer) || shape.size == 1
  # `Image::constitute` requires Float pixels to be in [0,1]
  pixels = Norm.feature_scaling nmat.round(4), to: [0,1]
  Magick::Image.constitute *shape, "I", pixels.to_flat_a
end

.nmat_to_png(nmat, fname, shape: nil) ⇒ Object

Create PNG file from NMatrix data



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# File 'lib/machine_learning_workbench/tools/imaging.rb', line 16

def self.nmat_to_png nmat, fname, shape: nil
  nmat_to_img(nmat, shape: shape).write fname
end