Just-in-time compiler

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(Add sample program of demonstration.)
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# Load colour image.
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img = MultiArray.load_rgb24( "/home/engjw/Documents/pictures/miraculix.jpg" )
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# Display the image.
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img.show
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# Convert the image to grey-level floating point.
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gimg = img.to_sfloat
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# Display grey-level image.
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gimg.show
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# Downsample the image with a sampling-rate of 2 in each direction and a sampling-offset of 1.
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limg = gimg.downsample( [ 2, 2 ], [ 1, 1 ] )
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# Display low-resolution image.
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limg.show
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# Apply element-wise function to each pixel of the image.
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limg.collect { |x| x * x }
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# Display processed image.
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( limg.collect { |x| x * x } ).show
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# Display range of values.
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( limg.collect { |x| x * x } ).range
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# Display range after normalisation.
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( limg.collect { |x| x * x } ).normalise.range
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# Display normalised result.
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( limg.collect { |x| x * x } ).normalise.show
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# Show data as one-dimensional array.
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limg.data
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# Define an element-wise function for one-dimensional arrays.
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Sequence.define_unary_op( :sqr ) { |x| x * x }
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# Reuse this function for multidimensional arrays.
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MultiArray.define_unary_op( :sqr )
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# Call the function.
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limg.sqr
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# Measure time of operation.
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t = Time.new.to_f; limg.sqr; Time.new.to_f - t
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# Example function using libJIT.
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libjit = JITFunction.compile( JITType::LINT, JITType::LINT ) do |f|
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    ( JITTerm.param( f, 0 ) * JITTerm.const( f, JITType::LINT, 2 ) ).return_me
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end
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# Calling the function.
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libjit.call( 2 )
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# Define element-wise operation using JIT.
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Sequence.define_unary_jit_op( :sqr ) { |x| x * x }
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# Measure time of operation now.
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t = Time.new.to_f; limg.sqr; Time.new.to_f - t
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# Apply function and display result.
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gimg.sqr.normalise.show
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# Apply function twice and display result.
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gimg.sqr.sqr.normalise.show
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# Define a small filter.
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f = MultiArray.to_multiarray( [ [ -1, 0, 1 ], [ -2, 0, 2 ], [ -1, 0, 1 ] ] )
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# Correlate with this filter (slow).
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limg.correlate( f )
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# Correlate with filter using native datatypes to facilitate the JIT-based implementation.
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limg.correlate( f.to_sint )
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# Sobel-X operator.
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gimg.sobel_x
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# Display Sobel-X and Sobel-Y image.
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gimg.sobel_x.normalise.show
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gimg.sobel_y.normalise.show
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# Define an edge detector.
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class MultiArray
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  def edges
 +
    Math.sqrt( sobel_x.sqr + sobel_y.sqr )
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  end
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end
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# Compute edges.
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gimg.edges
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# Display edges.
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gimg.edges.normalise.show
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# Display edges black-on-white.
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( 255 - gimg.edges.normalise ).show
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# Same but more efficient.
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gimg.edges.normalise( 255..0 ).show
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=See Also=
 
=See Also=
 
* [[Hornetseye]]
 
* [[Hornetseye]]

Revision as of 00:03, 12 November 2008

HornetsEye makes use of libjit. This means that you can even define and compile methods during runtime. Here is a 46 MByte video demonstrating this capability. I recommend to watch the OGG video instead of the Shockwave video. It is of much higher quality.

Demonstration of using the just-in-time compiler with HornetsEye. You can also view the OGG-video of the JIT demo which is of higher quality. The video was created using recordMyDesktop 
# Load colour image.
img = MultiArray.load_rgb24( "/home/engjw/Documents/pictures/miraculix.jpg" )
# Display the image.
img.show
# Convert the image to grey-level floating point.
gimg = img.to_sfloat
# Display grey-level image.
gimg.show
# Downsample the image with a sampling-rate of 2 in each direction and a sampling-offset of 1.
limg = gimg.downsample( [ 2, 2 ], [ 1, 1 ] )
# Display low-resolution image.
limg.show
# Apply element-wise function to each pixel of the image.
limg.collect { |x| x * x }
# Display processed image.
( limg.collect { |x| x * x } ).show
# Display range of values.
( limg.collect { |x| x * x } ).range
# Display range after normalisation.
( limg.collect { |x| x * x } ).normalise.range
# Display normalised result.
( limg.collect { |x| x * x } ).normalise.show
# Show data as one-dimensional array.
limg.data
# Define an element-wise function for one-dimensional arrays.
Sequence.define_unary_op( :sqr ) { |x| x * x }
# Reuse this function for multidimensional arrays.
MultiArray.define_unary_op( :sqr )
# Call the function.
limg.sqr
# Measure time of operation.
t = Time.new.to_f; limg.sqr; Time.new.to_f - t
# Example function using libJIT.
libjit = JITFunction.compile( JITType::LINT, JITType::LINT ) do |f|
   ( JITTerm.param( f, 0 ) * JITTerm.const( f, JITType::LINT, 2 ) ).return_me
end
# Calling the function.
libjit.call( 2 )
# Define element-wise operation using JIT.
Sequence.define_unary_jit_op( :sqr ) { |x| x * x }
# Measure time of operation now.
t = Time.new.to_f; limg.sqr; Time.new.to_f - t
# Apply function and display result.
gimg.sqr.normalise.show
# Apply function twice and display result.
gimg.sqr.sqr.normalise.show
# Define a small filter.
f = MultiArray.to_multiarray( [ [ -1, 0, 1 ], [ -2, 0, 2 ], [ -1, 0, 1 ] ] )
# Correlate with this filter (slow).
limg.correlate( f )
# Correlate with filter using native datatypes to facilitate the JIT-based implementation.
limg.correlate( f.to_sint )
# Sobel-X operator.
gimg.sobel_x
# Display Sobel-X and Sobel-Y image.
gimg.sobel_x.normalise.show
gimg.sobel_y.normalise.show
# Define an edge detector.
class MultiArray
  def edges
    Math.sqrt( sobel_x.sqr + sobel_y.sqr )
  end
end
# Compute edges.
gimg.edges
# Display edges.
gimg.edges.normalise.show
# Display edges black-on-white.
( 255 - gimg.edges.normalise ).show
# Same but more efficient.
gimg.edges.normalise( 255..0 ).show

See Also

External Links

Retrieved from "https://vision.eng.shu.ac.uk/mmvl/mmvlwiki/index.php?title=Just-in-time_compiler&oldid=3096"
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