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kaklik |
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namespace mimas { |
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template< typename T > |
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std::deque< T > gaussBlurFilter( T sigma, T maxError ) |
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{ |
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assert( sigma > 0 ); |
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std::deque< T > retval; |
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// Integral of gauss-curve from -0.5 to +0.5. |
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T integral = erf( 0.5 / ( sqrt( 2.0 ) * sigma ) ); |
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retval.push_back( integral ); |
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while ( 1.0 - integral > maxError ) { |
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// Compute integral of gauss-curve from -newSize2 to +newSize2. |
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const T |
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newSize2 = 0.5 * ( retval.size() + 2 ), |
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newIntegral = erf( newSize2 / ( sqrt( 2.0 ) * sigma ) ), |
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value = 0.5 * ( newIntegral - integral ); |
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retval.push_back( value ); |
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retval.push_front( value ); |
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integral = newIntegral; |
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}; |
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// Normalise to maintain power of image. |
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const T factor = 1.0 / integral; |
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for ( typename std::deque< T >::iterator i = retval.begin(); |
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i != retval.end(); i++ ) |
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*i *= factor; |
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return retval; |
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} |
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template< typename T > |
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boost::multi_array< T, 2 > gaussBlur |
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( const boost::const_multi_array_ref< T, 2 > &x, T sigma, T maxError ) |
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{ |
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// Split error-budget up for two operations. |
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std::deque< T > filter( gaussBlurFilter< T >( sigma, maxError / 2.0 ) ); |
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boost::multi_array< T, 2 > |
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filterx( boost::extents[ 1 ][ filter.size() ] ), |
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filtery( boost::extents[ filter.size()][ 1 ] ); |
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std::copy( filter.begin(), filter.end(), filterx.data() ); |
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std::copy( filter.begin(), filter.end(), filtery.data() ); |
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return correlate( correlate( x, filterx ), filtery ); |
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} |
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template< typename T > |
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std::deque< T > gaussGradientFilter( T sigma, T maxError ) |
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{ |
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assert( sigma > 0 ); |
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std::deque< T > retval; |
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// Integral of gauss-gradient from -0.5 to +0.5 is zero. |
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retval.push_back( 0.0 ); |
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// Set to absolute value of integral of gauss-gradient from 0.5 to infinity |
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T integral = |
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1.0 / ( sqrt( 2.0 * M_PI ) * sigma ) * exp( -0.125 / ( sigma * sigma ) ), |
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squareIntegral = 0.0; |
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while ( 2.0 * integral > maxError ) { |
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// Compute integral of gauss-curve from -newSize2 to +newSize2. |
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const T |
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newSize2 = 0.5 * ( retval.size() + 2 ), |
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newIntegral = 1.0 / ( sqrt( 2.0 * M_PI ) * sigma ) * exp( -newSize2 * newSize2 / ( 2.0 * sigma * sigma ) ), |
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value = integral - newIntegral; |
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retval.push_back( value ); |
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retval.push_front( -value ); |
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integral = newIntegral; |
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squareIntegral += value * value; |
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}; |
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// Normalise to maintain power of image. |
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const T factor = 1.0 / squareIntegral; |
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for ( typename std::deque< T >::iterator i = retval.begin(); |
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i != retval.end(); i++ ) |
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*i *= factor; |
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return retval; |
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} |
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template< typename T > |
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boost::multi_array< T, 2 > gaussGradientX |
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( const boost::const_multi_array_ref< T, 2 > &x, T sigma, T maxError ) |
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{ |
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// Split error-budget up for two operations. |
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std::deque< T > |
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filter1( gaussGradientFilter< T >( sigma, maxError / 2.0 ) ), |
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filter2( gaussBlurFilter< T >( sigma, maxError / 2.0 ) ); |
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boost::multi_array< T, 2 > |
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filterx( boost::extents[ 1 ][ filter1.size() ] ), |
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filtery( boost::extents[ filter2.size()][ 1 ] ); |
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std::copy( filter1.begin(), filter1.end(), filterx.data() ); |
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std::copy( filter2.begin(), filter2.end(), filtery.data() ); |
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return correlate( correlate( x, filterx ), filtery ); |
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} |
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template< typename T > |
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image< T > gaussGradientX( const image< T > &x, T sigma, |
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T maxError ) |
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{ |
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// Split error-budget up for two operations. |
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std::deque< T > |
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filter1( gaussGradientFilter< T >( sigma, maxError / 2.0 ) ), |
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filter2( gaussBlurFilter< T >( sigma, maxError / 2.0 ) ); |
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image< T > filterx, filtery; |
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filterx.init( filter1.size(), 1 ); |
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filtery.init( 1, filter2.size() ); |
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std::copy( filter1.begin(), filter1.end(), filterx.rawData() ); |
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std::copy( filter2.begin(), filter2.end(), filtery.rawData() ); |
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return correlate( correlate( x, filterx ), filtery ); |
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} |
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template< typename T > |
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boost::multi_array< T, 2 > gaussGradientY |
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( const boost::const_multi_array_ref< T, 2 > &x, T sigma, T maxError ) |
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{ |
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// Split error-budget up for two operations. |
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std::deque< T > |
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filter1( gaussBlurFilter< T >( sigma, maxError / 2.0 ) ), |
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filter2( gaussGradientFilter< T >( sigma, maxError / 2.0 ) ); |
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boost::multi_array< T, 2 > |
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filterx( boost::extents[ 1 ][ filter1.size() ] ), |
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filtery( boost::extents[ filter2.size()][ 1 ] ); |
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std::copy( filter1.begin(), filter1.end(), filterx.data() ); |
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std::copy( filter2.begin(), filter2.end(), filtery.data() ); |
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return correlate( correlate( x, filterx ), filtery ); |
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} |
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template< typename T > |
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image< T > gaussGradientY( const image< T > &x, T sigma, |
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T maxError ) |
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{ |
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// Split error-budget up for two operations. |
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std::deque< T > |
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filter1( gaussBlurFilter< T >( sigma, maxError / 2.0 ) ), |
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filter2( gaussGradientFilter< T >( sigma, maxError / 2.0 ) ); |
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image< T > filterx, filtery; |
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filterx.init( filter1.size(), 1 ); |
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filtery.init( 1, filter2.size() ); |
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std::copy( filter1.begin(), filter1.end(), filterx.rawData() ); |
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std::copy( filter2.begin(), filter2.end(), filtery.rawData() ); |
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return correlate( correlate( x, filterx ), filtery ); |
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} |
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}; |