| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % FFFFF EEEEE AAA TTTTT U U RRRR EEEEE % |
| % F E A A T U U R R E % |
| % FFF EEE AAAAA T U U RRRR EEE % |
| % F E A A T U U R R E % |
| % F EEEEE A A T UUU R R EEEEE % |
| % % |
| % % |
| % MagickCore Image Feature Methods % |
| % % |
| % Software Design % |
| % John Cristy % |
| % July 1992 % |
| % % |
| % % |
| % Copyright 1999-2011 ImageMagick Studio LLC, a non-profit organization % |
| % dedicated to making software imaging solutions freely available. % |
| % % |
| % You may not use this file except in compliance with the License. You may % |
| % obtain a copy of the License at % |
| % % |
| % http://www.imagemagick.org/script/license.php % |
| % % |
| % Unless required by applicable law or agreed to in writing, software % |
| % distributed under the License is distributed on an "AS IS" BASIS, % |
| % WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. % |
| % See the License for the specific language governing permissions and % |
| % limitations under the License. % |
| % % |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % |
| % |
| % |
| */ |
| |
| /* |
| Include declarations. |
| */ |
| #include "magick/studio.h" |
| #include "magick/property.h" |
| #include "magick/animate.h" |
| #include "magick/blob.h" |
| #include "magick/blob-private.h" |
| #include "magick/cache.h" |
| #include "magick/cache-private.h" |
| #include "magick/cache-view.h" |
| #include "magick/client.h" |
| #include "magick/color.h" |
| #include "magick/color-private.h" |
| #include "magick/colorspace.h" |
| #include "magick/colorspace-private.h" |
| #include "magick/composite.h" |
| #include "magick/composite-private.h" |
| #include "magick/compress.h" |
| #include "magick/constitute.h" |
| #include "magick/deprecate.h" |
| #include "magick/display.h" |
| #include "magick/draw.h" |
| #include "magick/enhance.h" |
| #include "magick/exception.h" |
| #include "magick/exception-private.h" |
| #include "magick/feature.h" |
| #include "magick/gem.h" |
| #include "magick/geometry.h" |
| #include "magick/list.h" |
| #include "magick/image-private.h" |
| #include "magick/magic.h" |
| #include "magick/magick.h" |
| #include "magick/memory_.h" |
| #include "magick/module.h" |
| #include "magick/monitor.h" |
| #include "magick/monitor-private.h" |
| #include "magick/option.h" |
| #include "magick/paint.h" |
| #include "magick/pixel-private.h" |
| #include "magick/profile.h" |
| #include "magick/quantize.h" |
| #include "magick/random_.h" |
| #include "magick/segment.h" |
| #include "magick/semaphore.h" |
| #include "magick/signature-private.h" |
| #include "magick/string_.h" |
| #include "magick/thread-private.h" |
| #include "magick/timer.h" |
| #include "magick/utility.h" |
| #include "magick/version.h" |
| |
| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % G e t I m a g e C h a n n e l F e a t u r e s % |
| % % |
| % % |
| % % |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % |
| % GetImageChannelFeatures() returns features for each channel in the image in |
| % each of four directions (horizontal, vertical, left and right diagonals) |
| % for the specified distance. The features include the angular second |
| % moment, contrast, correlation, sum of squares: variance, inverse difference |
| % moment, sum average, sum varience, sum entropy, entropy, difference variance,% difference entropy, information measures of correlation 1, information |
| % measures of correlation 2, and maximum correlation coefficient. You can |
| % access the red channel contrast, for example, like this: |
| % |
| % channel_features=GetImageChannelFeatures(image,1,exception); |
| % contrast=channel_features[RedChannel].contrast[0]; |
| % |
| % Use MagickRelinquishMemory() to free the features buffer. |
| % |
| % The format of the GetImageChannelFeatures method is: |
| % |
| % ChannelFeatures *GetImageChannelFeatures(const Image *image, |
| % const size_t distance,ExceptionInfo *exception) |
| % |
| % A description of each parameter follows: |
| % |
| % o image: the image. |
| % |
| % o distance: the distance. |
| % |
| % o exception: return any errors or warnings in this structure. |
| % |
| */ |
| |
| static inline ssize_t MagickAbsoluteValue(const ssize_t x) |
| { |
| if (x < 0) |
| return(-x); |
| return(x); |
| } |
| |
| MagickExport ChannelFeatures *GetImageChannelFeatures(const Image *image, |
| const size_t distance,ExceptionInfo *exception) |
| { |
| typedef struct _ChannelStatistics |
| { |
| DoublePixelPacket |
| direction[4]; /* horizontal, vertical, left and right diagonals */ |
| } ChannelStatistics; |
| |
| CacheView |
| *image_view; |
| |
| ChannelFeatures |
| *channel_features; |
| |
| ChannelStatistics |
| **cooccurrence, |
| correlation, |
| *density_x, |
| *density_xy, |
| *density_y, |
| entropy_x, |
| entropy_xy, |
| entropy_xy1, |
| entropy_xy2, |
| entropy_y, |
| mean, |
| **Q, |
| *sum, |
| sum_squares, |
| variance; |
| |
| LongPixelPacket |
| gray, |
| *grays; |
| |
| MagickBooleanType |
| status; |
| |
| register ssize_t |
| i; |
| |
| size_t |
| length; |
| |
| ssize_t |
| y, |
| z; |
| |
| unsigned int |
| number_grays; |
| |
| assert(image != (Image *) NULL); |
| assert(image->signature == MagickSignature); |
| if (image->debug != MagickFalse) |
| (void) LogMagickEvent(TraceEvent,GetMagickModule(),"%s",image->filename); |
| if ((image->columns < (distance+1)) || (image->rows < (distance+1))) |
| return((ChannelFeatures *) NULL); |
| length=CompositeChannels+1UL; |
| channel_features=(ChannelFeatures *) AcquireQuantumMemory(length, |
| sizeof(*channel_features)); |
| if (channel_features == (ChannelFeatures *) NULL) |
| ThrowFatalException(ResourceLimitFatalError,"MemoryAllocationFailed"); |
| (void) ResetMagickMemory(channel_features,0,length* |
| sizeof(*channel_features)); |
| /* |
| Form grays. |
| */ |
| grays=(LongPixelPacket *) AcquireQuantumMemory(MaxMap+1UL,sizeof(*grays)); |
| if (grays == (LongPixelPacket *) NULL) |
| { |
| channel_features=(ChannelFeatures *) RelinquishMagickMemory( |
| channel_features); |
| (void) ThrowMagickException(exception,GetMagickModule(), |
| ResourceLimitError,"MemoryAllocationFailed","`%s'",image->filename); |
| return(channel_features); |
| } |
| for (i=0; i <= (ssize_t) MaxMap; i++) |
| { |
| grays[i].red=(~0U); |
| grays[i].green=(~0U); |
| grays[i].blue=(~0U); |
| grays[i].opacity=(~0U); |
| grays[i].index=(~0U); |
| } |
| status=MagickTrue; |
| image_view=AcquireCacheView(image); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (y=0; y < (ssize_t) image->rows; y++) |
| { |
| register const IndexPacket |
| *restrict indexes; |
| |
| register const PixelPacket |
| *restrict p; |
| |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(image_view,0,y,image->columns,1,exception); |
| if (p == (const PixelPacket *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| indexes=GetCacheViewVirtualIndexQueue(image_view); |
| for (x=0; x < (ssize_t) image->columns; x++) |
| { |
| grays[ScaleQuantumToMap(GetRedPixelComponent(p))].red= |
| ScaleQuantumToMap(GetRedPixelComponent(p)); |
| grays[ScaleQuantumToMap(GetGreenPixelComponent(p))].green= |
| ScaleQuantumToMap(GetGreenPixelComponent(p)); |
| grays[ScaleQuantumToMap(GetBluePixelComponent(p))].blue= |
| ScaleQuantumToMap(GetBluePixelComponent(p)); |
| if (image->matte != MagickFalse) |
| grays[ScaleQuantumToMap(GetOpacityPixelComponent(p))].opacity= |
| ScaleQuantumToMap(GetOpacityPixelComponent(p)); |
| if (image->colorspace == CMYKColorspace) |
| grays[ScaleQuantumToMap(GetIndexPixelComponent(indexes+x))].index= |
| ScaleQuantumToMap(GetIndexPixelComponent(indexes+x)); |
| p++; |
| } |
| } |
| image_view=DestroyCacheView(image_view); |
| if (status == MagickFalse) |
| { |
| grays=(LongPixelPacket *) RelinquishMagickMemory(grays); |
| channel_features=(ChannelFeatures *) RelinquishMagickMemory( |
| channel_features); |
| return(channel_features); |
| } |
| (void) ResetMagickMemory(&gray,0,sizeof(gray)); |
| for (i=0; i <= (ssize_t) MaxMap; i++) |
| { |
| if (grays[i].red != ~0U) |
| grays[gray.red++].red=grays[i].red; |
| if (grays[i].green != ~0U) |
| grays[gray.green++].green=grays[i].green; |
| if (grays[i].blue != ~0U) |
| grays[gray.blue++].blue=grays[i].blue; |
| if (image->matte != MagickFalse) |
| if (grays[i].opacity != ~0U) |
| grays[gray.opacity++].opacity=grays[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| if (grays[i].index != ~0U) |
| grays[gray.index++].index=grays[i].index; |
| } |
| /* |
| Allocate spatial dependence matrix. |
| */ |
| number_grays=gray.red; |
| if (gray.green > number_grays) |
| number_grays=gray.green; |
| if (gray.blue > number_grays) |
| number_grays=gray.blue; |
| if (image->matte != MagickFalse) |
| if (gray.opacity > number_grays) |
| number_grays=gray.opacity; |
| if (image->colorspace == CMYKColorspace) |
| if (gray.index > number_grays) |
| number_grays=gray.index; |
| cooccurrence=(ChannelStatistics **) AcquireQuantumMemory(number_grays, |
| sizeof(*cooccurrence)); |
| density_x=(ChannelStatistics *) AcquireQuantumMemory(2*(number_grays+1), |
| sizeof(*density_x)); |
| density_xy=(ChannelStatistics *) AcquireQuantumMemory(2*(number_grays+1), |
| sizeof(*density_xy)); |
| density_y=(ChannelStatistics *) AcquireQuantumMemory(2*(number_grays+1), |
| sizeof(*density_y)); |
| Q=(ChannelStatistics **) AcquireQuantumMemory(number_grays,sizeof(*Q)); |
| sum=(ChannelStatistics *) AcquireQuantumMemory(number_grays,sizeof(*sum)); |
| if ((cooccurrence == (ChannelStatistics **) NULL) || |
| (density_x == (ChannelStatistics *) NULL) || |
| (density_xy == (ChannelStatistics *) NULL) || |
| (density_y == (ChannelStatistics *) NULL) || |
| (Q == (ChannelStatistics **) NULL) || |
| (sum == (ChannelStatistics *) NULL)) |
| { |
| if (Q != (ChannelStatistics **) NULL) |
| { |
| for (i=0; i < (ssize_t) number_grays; i++) |
| Q[i]=(ChannelStatistics *) RelinquishMagickMemory(Q[i]); |
| Q=(ChannelStatistics **) RelinquishMagickMemory(Q); |
| } |
| if (sum != (ChannelStatistics *) NULL) |
| sum=(ChannelStatistics *) RelinquishMagickMemory(sum); |
| if (density_y != (ChannelStatistics *) NULL) |
| density_y=(ChannelStatistics *) RelinquishMagickMemory(density_y); |
| if (density_xy != (ChannelStatistics *) NULL) |
| density_xy=(ChannelStatistics *) RelinquishMagickMemory(density_xy); |
| if (density_x != (ChannelStatistics *) NULL) |
| density_x=(ChannelStatistics *) RelinquishMagickMemory(density_x); |
| if (cooccurrence != (ChannelStatistics **) NULL) |
| { |
| for (i=0; i < (ssize_t) number_grays; i++) |
| cooccurrence[i]=(ChannelStatistics *) |
| RelinquishMagickMemory(cooccurrence[i]); |
| cooccurrence=(ChannelStatistics **) RelinquishMagickMemory( |
| cooccurrence); |
| } |
| grays=(LongPixelPacket *) RelinquishMagickMemory(grays); |
| channel_features=(ChannelFeatures *) RelinquishMagickMemory( |
| channel_features); |
| (void) ThrowMagickException(exception,GetMagickModule(), |
| ResourceLimitError,"MemoryAllocationFailed","`%s'",image->filename); |
| return(channel_features); |
| } |
| (void) ResetMagickMemory(&correlation,0,sizeof(correlation)); |
| (void) ResetMagickMemory(density_x,0,2*(number_grays+1)*sizeof(*density_x)); |
| (void) ResetMagickMemory(density_xy,0,2*(number_grays+1)*sizeof(*density_xy)); |
| (void) ResetMagickMemory(density_y,0,2*(number_grays+1)*sizeof(*density_y)); |
| (void) ResetMagickMemory(&mean,0,sizeof(mean)); |
| (void) ResetMagickMemory(sum,0,number_grays*sizeof(*sum)); |
| (void) ResetMagickMemory(&sum_squares,0,sizeof(sum_squares)); |
| (void) ResetMagickMemory(density_xy,0,2*number_grays*sizeof(*density_xy)); |
| (void) ResetMagickMemory(&entropy_x,0,sizeof(entropy_x)); |
| (void) ResetMagickMemory(&entropy_xy,0,sizeof(entropy_xy)); |
| (void) ResetMagickMemory(&entropy_xy1,0,sizeof(entropy_xy1)); |
| (void) ResetMagickMemory(&entropy_xy2,0,sizeof(entropy_xy2)); |
| (void) ResetMagickMemory(&entropy_y,0,sizeof(entropy_y)); |
| (void) ResetMagickMemory(&variance,0,sizeof(variance)); |
| for (i=0; i < (ssize_t) number_grays; i++) |
| { |
| cooccurrence[i]=(ChannelStatistics *) AcquireQuantumMemory(number_grays, |
| sizeof(**cooccurrence)); |
| Q[i]=(ChannelStatistics *) AcquireQuantumMemory(number_grays,sizeof(**Q)); |
| if ((cooccurrence[i] == (ChannelStatistics *) NULL) || |
| (Q[i] == (ChannelStatistics *) NULL)) |
| break; |
| (void) ResetMagickMemory(cooccurrence[i],0,number_grays* |
| sizeof(**cooccurrence)); |
| (void) ResetMagickMemory(Q[i],0,number_grays*sizeof(**Q)); |
| } |
| if (i < (ssize_t) number_grays) |
| { |
| for (i--; i >= 0; i--) |
| { |
| if (Q[i] != (ChannelStatistics *) NULL) |
| Q[i]=(ChannelStatistics *) RelinquishMagickMemory(Q[i]); |
| if (cooccurrence[i] != (ChannelStatistics *) NULL) |
| cooccurrence[i]=(ChannelStatistics *) |
| RelinquishMagickMemory(cooccurrence[i]); |
| } |
| Q=(ChannelStatistics **) RelinquishMagickMemory(Q); |
| cooccurrence=(ChannelStatistics **) RelinquishMagickMemory(cooccurrence); |
| sum=(ChannelStatistics *) RelinquishMagickMemory(sum); |
| density_y=(ChannelStatistics *) RelinquishMagickMemory(density_y); |
| density_xy=(ChannelStatistics *) RelinquishMagickMemory(density_xy); |
| density_x=(ChannelStatistics *) RelinquishMagickMemory(density_x); |
| grays=(LongPixelPacket *) RelinquishMagickMemory(grays); |
| channel_features=(ChannelFeatures *) RelinquishMagickMemory( |
| channel_features); |
| (void) ThrowMagickException(exception,GetMagickModule(), |
| ResourceLimitError,"MemoryAllocationFailed","`%s'",image->filename); |
| return(channel_features); |
| } |
| /* |
| Initialize spatial dependence matrix. |
| */ |
| status=MagickTrue; |
| image_view=AcquireCacheView(image); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (y=0; y < (ssize_t) image->rows; y++) |
| { |
| register const IndexPacket |
| *restrict indexes; |
| |
| register const PixelPacket |
| *restrict p; |
| |
| register ssize_t |
| x; |
| |
| ssize_t |
| offset, |
| u, |
| v; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(image_view,-(ssize_t) distance,y,image->columns+ |
| 2*distance,distance+2,exception); |
| if (p == (const PixelPacket *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| indexes=GetCacheViewVirtualIndexQueue(image_view); |
| p+=distance; |
| indexes+=distance; |
| for (x=0; x < (ssize_t) image->columns; x++) |
| { |
| for (i=0; i < 4; i++) |
| { |
| switch (i) |
| { |
| case 0: |
| default: |
| { |
| /* |
| Horizontal adjacency. |
| */ |
| offset=(ssize_t) distance; |
| break; |
| } |
| case 1: |
| { |
| /* |
| Vertical adjacency. |
| */ |
| offset=(ssize_t) (image->columns+2*distance); |
| break; |
| } |
| case 2: |
| { |
| /* |
| Right diagonal adjacency. |
| */ |
| offset=(ssize_t) ((image->columns+2*distance)-distance); |
| break; |
| } |
| case 3: |
| { |
| /* |
| Left diagonal adjacency. |
| */ |
| offset=(ssize_t) ((image->columns+2*distance)+distance); |
| break; |
| } |
| } |
| u=0; |
| v=0; |
| while (grays[u].red != ScaleQuantumToMap(GetRedPixelComponent(p))) |
| u++; |
| while (grays[v].red != ScaleQuantumToMap(GetRedPixelComponent(p+offset))) |
| v++; |
| cooccurrence[u][v].direction[i].red++; |
| cooccurrence[v][u].direction[i].red++; |
| u=0; |
| v=0; |
| while (grays[u].green != ScaleQuantumToMap(GetGreenPixelComponent(p))) |
| u++; |
| while (grays[v].green != ScaleQuantumToMap(GetGreenPixelComponent(p+offset))) |
| v++; |
| cooccurrence[u][v].direction[i].green++; |
| cooccurrence[v][u].direction[i].green++; |
| u=0; |
| v=0; |
| while (grays[u].blue != ScaleQuantumToMap(GetBluePixelComponent(p))) |
| u++; |
| while (grays[v].blue != ScaleQuantumToMap((p+offset)->blue)) |
| v++; |
| cooccurrence[u][v].direction[i].blue++; |
| cooccurrence[v][u].direction[i].blue++; |
| if (image->matte != MagickFalse) |
| { |
| u=0; |
| v=0; |
| while (grays[u].opacity != ScaleQuantumToMap(GetOpacityPixelComponent(p))) |
| u++; |
| while (grays[v].opacity != ScaleQuantumToMap((p+offset)->opacity)) |
| v++; |
| cooccurrence[u][v].direction[i].opacity++; |
| cooccurrence[v][u].direction[i].opacity++; |
| } |
| if (image->colorspace == CMYKColorspace) |
| { |
| u=0; |
| v=0; |
| while (grays[u].index != ScaleQuantumToMap(GetIndexPixelComponent(indexes+x))) |
| u++; |
| while (grays[v].index != ScaleQuantumToMap(GetIndexPixelComponent(indexes+x+offset))) |
| v++; |
| cooccurrence[u][v].direction[i].index++; |
| cooccurrence[v][u].direction[i].index++; |
| } |
| } |
| p++; |
| } |
| } |
| grays=(LongPixelPacket *) RelinquishMagickMemory(grays); |
| image_view=DestroyCacheView(image_view); |
| if (status == MagickFalse) |
| { |
| for (i=0; i < (ssize_t) number_grays; i++) |
| cooccurrence[i]=(ChannelStatistics *) |
| RelinquishMagickMemory(cooccurrence[i]); |
| cooccurrence=(ChannelStatistics **) RelinquishMagickMemory(cooccurrence); |
| channel_features=(ChannelFeatures *) RelinquishMagickMemory( |
| channel_features); |
| (void) ThrowMagickException(exception,GetMagickModule(), |
| ResourceLimitError,"MemoryAllocationFailed","`%s'",image->filename); |
| return(channel_features); |
| } |
| /* |
| Normalize spatial dependence matrix. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| double |
| normalize; |
| |
| switch (i) |
| { |
| case 0: |
| default: |
| { |
| /* |
| Horizontal adjacency. |
| */ |
| normalize=2.0*image->rows*(image->columns-distance); |
| break; |
| } |
| case 1: |
| { |
| /* |
| Vertical adjacency. |
| */ |
| normalize=2.0*(image->rows-distance)*image->columns; |
| break; |
| } |
| case 2: |
| { |
| /* |
| Right diagonal adjacency. |
| */ |
| normalize=2.0*(image->rows-distance)*(image->columns-distance); |
| break; |
| } |
| case 3: |
| { |
| /* |
| Left diagonal adjacency. |
| */ |
| normalize=2.0*(image->rows-distance)*(image->columns-distance); |
| break; |
| } |
| } |
| for (y=0; y < (ssize_t) number_grays; y++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=0; x < (ssize_t) number_grays; x++) |
| { |
| cooccurrence[x][y].direction[i].red/=normalize; |
| cooccurrence[x][y].direction[i].green/=normalize; |
| cooccurrence[x][y].direction[i].blue/=normalize; |
| if (image->matte != MagickFalse) |
| cooccurrence[x][y].direction[i].opacity/=normalize; |
| if (image->colorspace == CMYKColorspace) |
| cooccurrence[x][y].direction[i].index/=normalize; |
| } |
| } |
| } |
| /* |
| Compute texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| register ssize_t |
| y; |
| |
| for (y=0; y < (ssize_t) number_grays; y++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=0; x < (ssize_t) number_grays; x++) |
| { |
| /* |
| Angular second moment: measure of homogeneity of the image. |
| */ |
| channel_features[RedChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].red* |
| cooccurrence[x][y].direction[i].red; |
| channel_features[GreenChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].green* |
| cooccurrence[x][y].direction[i].green; |
| channel_features[BlueChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].blue* |
| cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].opacity* |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].index* |
| cooccurrence[x][y].direction[i].index; |
| /* |
| Correlation: measure of linear-dependencies in the image. |
| */ |
| sum[y].direction[i].red+=cooccurrence[x][y].direction[i].red; |
| sum[y].direction[i].green+=cooccurrence[x][y].direction[i].green; |
| sum[y].direction[i].blue+=cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| sum[y].direction[i].opacity+=cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| sum[y].direction[i].index+=cooccurrence[x][y].direction[i].index; |
| correlation.direction[i].red+=x*y*cooccurrence[x][y].direction[i].red; |
| correlation.direction[i].green+=x*y* |
| cooccurrence[x][y].direction[i].green; |
| correlation.direction[i].blue+=x*y* |
| cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| correlation.direction[i].opacity+=x*y* |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| correlation.direction[i].index+=x*y* |
| cooccurrence[x][y].direction[i].index; |
| /* |
| Inverse Difference Moment. |
| */ |
| channel_features[RedChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].red/((y-x)*(y-x)+1); |
| channel_features[GreenChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].green/((y-x)*(y-x)+1); |
| channel_features[BlueChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].blue/((y-x)*(y-x)+1); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].opacity/((y-x)*(y-x)+1); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].index/((y-x)*(y-x)+1); |
| /* |
| Sum average. |
| */ |
| density_xy[y+x+2].direction[i].red+= |
| cooccurrence[x][y].direction[i].red; |
| density_xy[y+x+2].direction[i].green+= |
| cooccurrence[x][y].direction[i].green; |
| density_xy[y+x+2].direction[i].blue+= |
| cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| density_xy[y+x+2].direction[i].opacity+= |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| density_xy[y+x+2].direction[i].index+= |
| cooccurrence[x][y].direction[i].index; |
| /* |
| Entropy. |
| */ |
| channel_features[RedChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].red* |
| log10(cooccurrence[x][y].direction[i].red+MagickEpsilon); |
| channel_features[GreenChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].green* |
| log10(cooccurrence[x][y].direction[i].green+MagickEpsilon); |
| channel_features[BlueChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].blue* |
| log10(cooccurrence[x][y].direction[i].blue+MagickEpsilon); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].opacity* |
| log10(cooccurrence[x][y].direction[i].opacity+MagickEpsilon); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].index* |
| log10(cooccurrence[x][y].direction[i].index+MagickEpsilon); |
| /* |
| Information Measures of Correlation. |
| */ |
| density_x[x].direction[i].red+=cooccurrence[x][y].direction[i].red; |
| density_x[x].direction[i].green+=cooccurrence[x][y].direction[i].green; |
| density_x[x].direction[i].blue+=cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| density_x[x].direction[i].opacity+= |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| density_x[x].direction[i].index+= |
| cooccurrence[x][y].direction[i].index; |
| density_y[y].direction[i].red+=cooccurrence[x][y].direction[i].red; |
| density_y[y].direction[i].green+=cooccurrence[x][y].direction[i].green; |
| density_y[y].direction[i].blue+=cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| density_y[y].direction[i].opacity+= |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| density_y[y].direction[i].index+= |
| cooccurrence[x][y].direction[i].index; |
| } |
| mean.direction[i].red+=y*sum[y].direction[i].red; |
| sum_squares.direction[i].red+=y*y*sum[y].direction[i].red; |
| mean.direction[i].green+=y*sum[y].direction[i].green; |
| sum_squares.direction[i].green+=y*y*sum[y].direction[i].green; |
| mean.direction[i].blue+=y*sum[y].direction[i].blue; |
| sum_squares.direction[i].blue+=y*y*sum[y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| { |
| mean.direction[i].opacity+=y*sum[y].direction[i].opacity; |
| sum_squares.direction[i].opacity+=y*y*sum[y].direction[i].opacity; |
| } |
| if (image->colorspace == CMYKColorspace) |
| { |
| mean.direction[i].index+=y*sum[y].direction[i].index; |
| sum_squares.direction[i].index+=y*y*sum[y].direction[i].index; |
| } |
| } |
| /* |
| Correlation: measure of linear-dependencies in the image. |
| */ |
| channel_features[RedChannel].correlation[i]= |
| (correlation.direction[i].red-mean.direction[i].red* |
| mean.direction[i].red)/(sqrt(sum_squares.direction[i].red- |
| (mean.direction[i].red*mean.direction[i].red))*sqrt( |
| sum_squares.direction[i].red-(mean.direction[i].red* |
| mean.direction[i].red))); |
| channel_features[GreenChannel].correlation[i]= |
| (correlation.direction[i].green-mean.direction[i].green* |
| mean.direction[i].green)/(sqrt(sum_squares.direction[i].green- |
| (mean.direction[i].green*mean.direction[i].green))*sqrt( |
| sum_squares.direction[i].green-(mean.direction[i].green* |
| mean.direction[i].green))); |
| channel_features[BlueChannel].correlation[i]= |
| (correlation.direction[i].blue-mean.direction[i].blue* |
| mean.direction[i].blue)/(sqrt(sum_squares.direction[i].blue- |
| (mean.direction[i].blue*mean.direction[i].blue))*sqrt( |
| sum_squares.direction[i].blue-(mean.direction[i].blue* |
| mean.direction[i].blue))); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].correlation[i]= |
| (correlation.direction[i].opacity-mean.direction[i].opacity* |
| mean.direction[i].opacity)/(sqrt(sum_squares.direction[i].opacity- |
| (mean.direction[i].opacity*mean.direction[i].opacity))*sqrt( |
| sum_squares.direction[i].opacity-(mean.direction[i].opacity* |
| mean.direction[i].opacity))); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].correlation[i]= |
| (correlation.direction[i].index-mean.direction[i].index* |
| mean.direction[i].index)/(sqrt(sum_squares.direction[i].index- |
| (mean.direction[i].index*mean.direction[i].index))*sqrt( |
| sum_squares.direction[i].index-(mean.direction[i].index* |
| mean.direction[i].index))); |
| } |
| /* |
| Compute more texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=2; x < (ssize_t) (2*number_grays); x++) |
| { |
| /* |
| Sum average. |
| */ |
| channel_features[RedChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].red; |
| channel_features[GreenChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].green; |
| channel_features[BlueChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].blue; |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].index; |
| /* |
| Sum entropy. |
| */ |
| channel_features[RedChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].red* |
| log10(density_xy[x].direction[i].red+MagickEpsilon); |
| channel_features[GreenChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].green* |
| log10(density_xy[x].direction[i].green+MagickEpsilon); |
| channel_features[BlueChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].blue* |
| log10(density_xy[x].direction[i].blue+MagickEpsilon); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].opacity* |
| log10(density_xy[x].direction[i].opacity+MagickEpsilon); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].index* |
| log10(density_xy[x].direction[i].index+MagickEpsilon); |
| /* |
| Sum variance. |
| */ |
| channel_features[RedChannel].sum_variance[i]+= |
| (x-channel_features[RedChannel].sum_entropy[i])* |
| (x-channel_features[RedChannel].sum_entropy[i])* |
| density_xy[x].direction[i].red; |
| channel_features[GreenChannel].sum_variance[i]+= |
| (x-channel_features[GreenChannel].sum_entropy[i])* |
| (x-channel_features[GreenChannel].sum_entropy[i])* |
| density_xy[x].direction[i].green; |
| channel_features[BlueChannel].sum_variance[i]+= |
| (x-channel_features[BlueChannel].sum_entropy[i])* |
| (x-channel_features[BlueChannel].sum_entropy[i])* |
| density_xy[x].direction[i].blue; |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].sum_variance[i]+= |
| (x-channel_features[OpacityChannel].sum_entropy[i])* |
| (x-channel_features[OpacityChannel].sum_entropy[i])* |
| density_xy[x].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].sum_variance[i]+= |
| (x-channel_features[IndexChannel].sum_entropy[i])* |
| (x-channel_features[IndexChannel].sum_entropy[i])* |
| density_xy[x].direction[i].index; |
| } |
| } |
| /* |
| Compute more texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| register ssize_t |
| y; |
| |
| for (y=0; y < (ssize_t) number_grays; y++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=0; x < (ssize_t) number_grays; x++) |
| { |
| /* |
| Sum of Squares: Variance |
| */ |
| variance.direction[i].red+=(y-mean.direction[i].red+1)* |
| (y-mean.direction[i].red+1)*cooccurrence[x][y].direction[i].red; |
| variance.direction[i].green+=(y-mean.direction[i].green+1)* |
| (y-mean.direction[i].green+1)*cooccurrence[x][y].direction[i].green; |
| variance.direction[i].blue+=(y-mean.direction[i].blue+1)* |
| (y-mean.direction[i].blue+1)*cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| variance.direction[i].opacity+=(y-mean.direction[i].opacity+1)* |
| (y-mean.direction[i].opacity+1)* |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| variance.direction[i].index+=(y-mean.direction[i].index+1)* |
| (y-mean.direction[i].index+1)*cooccurrence[x][y].direction[i].index; |
| /* |
| Sum average / Difference Variance. |
| */ |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].red+= |
| cooccurrence[x][y].direction[i].red; |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].green+= |
| cooccurrence[x][y].direction[i].green; |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].blue+= |
| cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].opacity+= |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].index+= |
| cooccurrence[x][y].direction[i].index; |
| /* |
| Information Measures of Correlation. |
| */ |
| entropy_xy.direction[i].red-=cooccurrence[x][y].direction[i].red* |
| log10(cooccurrence[x][y].direction[i].red+MagickEpsilon); |
| entropy_xy.direction[i].green-=cooccurrence[x][y].direction[i].green* |
| log10(cooccurrence[x][y].direction[i].green+MagickEpsilon); |
| entropy_xy.direction[i].blue-=cooccurrence[x][y].direction[i].blue* |
| log10(cooccurrence[x][y].direction[i].blue+MagickEpsilon); |
| if (image->matte != MagickFalse) |
| entropy_xy.direction[i].opacity-= |
| cooccurrence[x][y].direction[i].opacity*log10( |
| cooccurrence[x][y].direction[i].opacity+MagickEpsilon); |
| if (image->colorspace == CMYKColorspace) |
| entropy_xy.direction[i].index-=cooccurrence[x][y].direction[i].index* |
| log10(cooccurrence[x][y].direction[i].index+MagickEpsilon); |
| entropy_xy1.direction[i].red-=(cooccurrence[x][y].direction[i].red* |
| log10(density_x[x].direction[i].red*density_y[y].direction[i].red+ |
| MagickEpsilon)); |
| entropy_xy1.direction[i].green-=(cooccurrence[x][y].direction[i].green* |
| log10(density_x[x].direction[i].green*density_y[y].direction[i].green+ |
| MagickEpsilon)); |
| entropy_xy1.direction[i].blue-=(cooccurrence[x][y].direction[i].blue* |
| log10(density_x[x].direction[i].blue*density_y[y].direction[i].blue+ |
| MagickEpsilon)); |
| if (image->matte != MagickFalse) |
| entropy_xy1.direction[i].opacity-=( |
| cooccurrence[x][y].direction[i].opacity*log10( |
| density_x[x].direction[i].opacity*density_y[y].direction[i].opacity+ |
| MagickEpsilon)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_xy1.direction[i].index-=( |
| cooccurrence[x][y].direction[i].index*log10( |
| density_x[x].direction[i].index*density_y[y].direction[i].index+ |
| MagickEpsilon)); |
| entropy_xy2.direction[i].red-=(density_x[x].direction[i].red* |
| density_y[y].direction[i].red*log10(density_x[x].direction[i].red* |
| density_y[y].direction[i].red+MagickEpsilon)); |
| entropy_xy2.direction[i].green-=(density_x[x].direction[i].green* |
| density_y[y].direction[i].green*log10(density_x[x].direction[i].green* |
| density_y[y].direction[i].green+MagickEpsilon)); |
| entropy_xy2.direction[i].blue-=(density_x[x].direction[i].blue* |
| density_y[y].direction[i].blue*log10(density_x[x].direction[i].blue* |
| density_y[y].direction[i].blue+MagickEpsilon)); |
| if (image->matte != MagickFalse) |
| entropy_xy2.direction[i].opacity-=(density_x[x].direction[i].opacity* |
| density_y[y].direction[i].opacity*log10( |
| density_x[x].direction[i].opacity*density_y[y].direction[i].opacity+ |
| MagickEpsilon)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_xy2.direction[i].index-=(density_x[x].direction[i].index* |
| density_y[y].direction[i].index*log10( |
| density_x[x].direction[i].index*density_y[y].direction[i].index+ |
| MagickEpsilon)); |
| } |
| } |
| channel_features[RedChannel].variance_sum_of_squares[i]= |
| variance.direction[i].red; |
| channel_features[GreenChannel].variance_sum_of_squares[i]= |
| variance.direction[i].green; |
| channel_features[BlueChannel].variance_sum_of_squares[i]= |
| variance.direction[i].blue; |
| if (image->matte != MagickFalse) |
| channel_features[RedChannel].variance_sum_of_squares[i]= |
| variance.direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[RedChannel].variance_sum_of_squares[i]= |
| variance.direction[i].index; |
| } |
| /* |
| Compute more texture features. |
| */ |
| (void) ResetMagickMemory(&variance,0,sizeof(variance)); |
| (void) ResetMagickMemory(&sum_squares,0,sizeof(sum_squares)); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=0; x < (ssize_t) number_grays; x++) |
| { |
| /* |
| Difference variance. |
| */ |
| variance.direction[i].red+=density_xy[x].direction[i].red; |
| variance.direction[i].green+=density_xy[x].direction[i].green; |
| variance.direction[i].blue+=density_xy[x].direction[i].blue; |
| if (image->matte != MagickFalse) |
| variance.direction[i].opacity+=density_xy[x].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| variance.direction[i].index+=density_xy[x].direction[i].index; |
| sum_squares.direction[i].red+=density_xy[x].direction[i].red* |
| density_xy[x].direction[i].red; |
| sum_squares.direction[i].green+=density_xy[x].direction[i].green* |
| density_xy[x].direction[i].green; |
| sum_squares.direction[i].blue+=density_xy[x].direction[i].blue* |
| density_xy[x].direction[i].blue; |
| if (image->matte != MagickFalse) |
| sum_squares.direction[i].opacity+=density_xy[x].direction[i].opacity* |
| density_xy[x].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| sum_squares.direction[i].index+=density_xy[x].direction[i].index* |
| density_xy[x].direction[i].index; |
| /* |
| Difference entropy. |
| */ |
| channel_features[RedChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].red* |
| log10(density_xy[x].direction[i].red+MagickEpsilon); |
| channel_features[GreenChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].green* |
| log10(density_xy[x].direction[i].green+MagickEpsilon); |
| channel_features[BlueChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].blue* |
| log10(density_xy[x].direction[i].blue+MagickEpsilon); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].opacity* |
| log10(density_xy[x].direction[i].opacity+MagickEpsilon); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].index* |
| log10(density_xy[x].direction[i].index+MagickEpsilon); |
| /* |
| Information Measures of Correlation. |
| */ |
| entropy_x.direction[i].red-=(density_x[x].direction[i].red* |
| log10(density_x[x].direction[i].red+MagickEpsilon)); |
| entropy_x.direction[i].green-=(density_x[x].direction[i].green* |
| log10(density_x[x].direction[i].green+MagickEpsilon)); |
| entropy_x.direction[i].blue-=(density_x[x].direction[i].blue* |
| log10(density_x[x].direction[i].blue+MagickEpsilon)); |
| if (image->matte != MagickFalse) |
| entropy_x.direction[i].opacity-=(density_x[x].direction[i].opacity* |
| log10(density_x[x].direction[i].opacity+MagickEpsilon)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_x.direction[i].index-=(density_x[x].direction[i].index* |
| log10(density_x[x].direction[i].index+MagickEpsilon)); |
| entropy_y.direction[i].red-=(density_y[y].direction[i].red* |
| log10(density_y[y].direction[i].red+MagickEpsilon)); |
| entropy_y.direction[i].green-=(density_y[y].direction[i].green* |
| log10(density_y[y].direction[i].green+MagickEpsilon)); |
| entropy_y.direction[i].blue-=(density_y[y].direction[i].blue* |
| log10(density_y[y].direction[i].blue+MagickEpsilon)); |
| if (image->matte != MagickFalse) |
| entropy_y.direction[i].opacity-=(density_y[y].direction[i].opacity* |
| log10(density_y[y].direction[i].opacity+MagickEpsilon)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_y.direction[i].index-=(density_y[y].direction[i].index* |
| log10(density_y[y].direction[i].index+MagickEpsilon)); |
| } |
| /* |
| Difference variance. |
| */ |
| channel_features[RedChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].red)- |
| (variance.direction[i].red*variance.direction[i].red))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| channel_features[GreenChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].green)- |
| (variance.direction[i].green*variance.direction[i].green))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| channel_features[BlueChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].blue)- |
| (variance.direction[i].blue*variance.direction[i].blue))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].opacity)- |
| (variance.direction[i].opacity*variance.direction[i].opacity))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].index)- |
| (variance.direction[i].index*variance.direction[i].index))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| /* |
| Information Measures of Correlation. |
| */ |
| channel_features[RedChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].red-entropy_xy1.direction[i].red)/ |
| (entropy_x.direction[i].red > entropy_y.direction[i].red ? |
| entropy_x.direction[i].red : entropy_y.direction[i].red); |
| channel_features[GreenChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].green-entropy_xy1.direction[i].green)/ |
| (entropy_x.direction[i].green > entropy_y.direction[i].green ? |
| entropy_x.direction[i].green : entropy_y.direction[i].green); |
| channel_features[BlueChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].blue-entropy_xy1.direction[i].blue)/ |
| (entropy_x.direction[i].blue > entropy_y.direction[i].blue ? |
| entropy_x.direction[i].blue : entropy_y.direction[i].blue); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].opacity-entropy_xy1.direction[i].opacity)/ |
| (entropy_x.direction[i].opacity > entropy_y.direction[i].opacity ? |
| entropy_x.direction[i].opacity : entropy_y.direction[i].opacity); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].index-entropy_xy1.direction[i].index)/ |
| (entropy_x.direction[i].index > entropy_y.direction[i].index ? |
| entropy_x.direction[i].index : entropy_y.direction[i].index); |
| channel_features[RedChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(entropy_xy2.direction[i].red- |
| entropy_xy.direction[i].red))))); |
| channel_features[GreenChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(entropy_xy2.direction[i].green- |
| entropy_xy.direction[i].green))))); |
| channel_features[BlueChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(entropy_xy2.direction[i].blue- |
| entropy_xy.direction[i].blue))))); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(entropy_xy2.direction[i].opacity- |
| entropy_xy.direction[i].opacity))))); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(entropy_xy2.direction[i].index- |
| entropy_xy.direction[i].index))))); |
| } |
| /* |
| Compute more texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(dynamic,4) shared(status) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| for (z=0; z < (ssize_t) number_grays; z++) |
| { |
| register ssize_t |
| y; |
| |
| ChannelStatistics |
| pixel; |
| |
| (void) ResetMagickMemory(&pixel,0,sizeof(pixel)); |
| for (y=0; y < (ssize_t) number_grays; y++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=0; x < (ssize_t) number_grays; x++) |
| { |
| /* |
| Contrast: amount of local variations present in an image. |
| */ |
| if (((y-x) == z) || ((x-y) == z)) |
| { |
| pixel.direction[i].red+=cooccurrence[x][y].direction[i].red; |
| pixel.direction[i].green+=cooccurrence[x][y].direction[i].green; |
| pixel.direction[i].blue+=cooccurrence[x][y].direction[i].blue; |
| if (image->matte != MagickFalse) |
| pixel.direction[i].opacity+= |
| cooccurrence[x][y].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| pixel.direction[i].index+=cooccurrence[x][y].direction[i].index; |
| } |
| /* |
| Maximum Correlation Coefficient. |
| */ |
| Q[z][y].direction[i].red+=cooccurrence[z][x].direction[i].red* |
| cooccurrence[y][x].direction[i].red/density_x[z].direction[i].red/ |
| density_y[x].direction[i].red; |
| Q[z][y].direction[i].green+=cooccurrence[z][x].direction[i].green* |
| cooccurrence[y][x].direction[i].green/ |
| density_x[z].direction[i].green/density_y[x].direction[i].red; |
| Q[z][y].direction[i].blue+=cooccurrence[z][x].direction[i].blue* |
| cooccurrence[y][x].direction[i].blue/density_x[z].direction[i].blue/ |
| density_y[x].direction[i].blue; |
| if (image->matte != MagickFalse) |
| Q[z][y].direction[i].opacity+= |
| cooccurrence[z][x].direction[i].opacity* |
| cooccurrence[y][x].direction[i].opacity/ |
| density_x[z].direction[i].opacity/ |
| density_y[x].direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| Q[z][y].direction[i].index+=cooccurrence[z][x].direction[i].index* |
| cooccurrence[y][x].direction[i].index/ |
| density_x[z].direction[i].index/density_y[x].direction[i].index; |
| } |
| } |
| channel_features[RedChannel].contrast[i]+=z*z*pixel.direction[i].red; |
| channel_features[GreenChannel].contrast[i]+=z*z*pixel.direction[i].green; |
| channel_features[BlueChannel].contrast[i]+=z*z*pixel.direction[i].blue; |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].contrast[i]+=z*z* |
| pixel.direction[i].opacity; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackChannel].contrast[i]+=z*z* |
| pixel.direction[i].index; |
| } |
| /* |
| Maximum Correlation Coefficient. |
| Future: return second largest eigenvalue of Q. |
| */ |
| channel_features[RedChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| channel_features[GreenChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| channel_features[BlueChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| if (image->matte != MagickFalse) |
| channel_features[OpacityChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[IndexChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| } |
| /* |
| Relinquish resources. |
| */ |
| sum=(ChannelStatistics *) RelinquishMagickMemory(sum); |
| for (i=0; i < (ssize_t) number_grays; i++) |
| Q[i]=(ChannelStatistics *) RelinquishMagickMemory(Q[i]); |
| Q=(ChannelStatistics **) RelinquishMagickMemory(Q); |
| density_y=(ChannelStatistics *) RelinquishMagickMemory(density_y); |
| density_xy=(ChannelStatistics *) RelinquishMagickMemory(density_xy); |
| density_x=(ChannelStatistics *) RelinquishMagickMemory(density_x); |
| for (i=0; i < (ssize_t) number_grays; i++) |
| cooccurrence[i]=(ChannelStatistics *) |
| RelinquishMagickMemory(cooccurrence[i]); |
| cooccurrence=(ChannelStatistics **) RelinquishMagickMemory(cooccurrence); |
| return(channel_features); |
| } |