| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % 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 % |
| % Cristy % |
| % July 1992 % |
| % % |
| % % |
| % Copyright 1999-2017 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 % |
| % % |
| % https://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 "MagickCore/studio.h" |
| #include "MagickCore/animate.h" |
| #include "MagickCore/artifact.h" |
| #include "MagickCore/blob.h" |
| #include "MagickCore/blob-private.h" |
| #include "MagickCore/cache.h" |
| #include "MagickCore/cache-private.h" |
| #include "MagickCore/cache-view.h" |
| #include "MagickCore/channel.h" |
| #include "MagickCore/client.h" |
| #include "MagickCore/color.h" |
| #include "MagickCore/color-private.h" |
| #include "MagickCore/colorspace.h" |
| #include "MagickCore/colorspace-private.h" |
| #include "MagickCore/composite.h" |
| #include "MagickCore/composite-private.h" |
| #include "MagickCore/compress.h" |
| #include "MagickCore/constitute.h" |
| #include "MagickCore/display.h" |
| #include "MagickCore/draw.h" |
| #include "MagickCore/enhance.h" |
| #include "MagickCore/exception.h" |
| #include "MagickCore/exception-private.h" |
| #include "MagickCore/feature.h" |
| #include "MagickCore/gem.h" |
| #include "MagickCore/geometry.h" |
| #include "MagickCore/list.h" |
| #include "MagickCore/image-private.h" |
| #include "MagickCore/magic.h" |
| #include "MagickCore/magick.h" |
| #include "MagickCore/matrix.h" |
| #include "MagickCore/memory_.h" |
| #include "MagickCore/module.h" |
| #include "MagickCore/monitor.h" |
| #include "MagickCore/monitor-private.h" |
| #include "MagickCore/morphology-private.h" |
| #include "MagickCore/option.h" |
| #include "MagickCore/paint.h" |
| #include "MagickCore/pixel-accessor.h" |
| #include "MagickCore/profile.h" |
| #include "MagickCore/property.h" |
| #include "MagickCore/quantize.h" |
| #include "MagickCore/quantum-private.h" |
| #include "MagickCore/random_.h" |
| #include "MagickCore/resource_.h" |
| #include "MagickCore/segment.h" |
| #include "MagickCore/semaphore.h" |
| #include "MagickCore/signature-private.h" |
| #include "MagickCore/string_.h" |
| #include "MagickCore/thread-private.h" |
| #include "MagickCore/timer.h" |
| #include "MagickCore/utility.h" |
| #include "MagickCore/version.h" |
| |
| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % C a n n y E d g e I m a g e % |
| % % |
| % % |
| % % |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % |
| % CannyEdgeImage() uses a multi-stage algorithm to detect a wide range of |
| % edges in images. |
| % |
| % The format of the CannyEdgeImage method is: |
| % |
| % Image *CannyEdgeImage(const Image *image,const double radius, |
| % const double sigma,const double lower_percent, |
| % const double upper_percent,ExceptionInfo *exception) |
| % |
| % A description of each parameter follows: |
| % |
| % o image: the image. |
| % |
| % o radius: the radius of the gaussian smoothing filter. |
| % |
| % o sigma: the sigma of the gaussian smoothing filter. |
| % |
| % o lower_percent: percentage of edge pixels in the lower threshold. |
| % |
| % o upper_percent: percentage of edge pixels in the upper threshold. |
| % |
| % o exception: return any errors or warnings in this structure. |
| % |
| */ |
| |
| typedef struct _CannyInfo |
| { |
| double |
| magnitude, |
| intensity; |
| |
| int |
| orientation; |
| |
| ssize_t |
| x, |
| y; |
| } CannyInfo; |
| |
| static inline MagickBooleanType IsAuthenticPixel(const Image *image, |
| const ssize_t x,const ssize_t y) |
| { |
| if ((x < 0) || (x >= (ssize_t) image->columns)) |
| return(MagickFalse); |
| if ((y < 0) || (y >= (ssize_t) image->rows)) |
| return(MagickFalse); |
| return(MagickTrue); |
| } |
| |
| static MagickBooleanType TraceEdges(Image *edge_image,CacheView *edge_view, |
| MatrixInfo *canny_cache,const ssize_t x,const ssize_t y, |
| const double lower_threshold,ExceptionInfo *exception) |
| { |
| CannyInfo |
| edge, |
| pixel; |
| |
| MagickBooleanType |
| status; |
| |
| register Quantum |
| *q; |
| |
| register ssize_t |
| i; |
| |
| q=GetCacheViewAuthenticPixels(edge_view,x,y,1,1,exception); |
| if (q == (Quantum *) NULL) |
| return(MagickFalse); |
| *q=QuantumRange; |
| status=SyncCacheViewAuthenticPixels(edge_view,exception); |
| if (status == MagickFalse) |
| return(MagickFalse); |
| if (GetMatrixElement(canny_cache,0,0,&edge) == MagickFalse) |
| return(MagickFalse); |
| edge.x=x; |
| edge.y=y; |
| if (SetMatrixElement(canny_cache,0,0,&edge) == MagickFalse) |
| return(MagickFalse); |
| for (i=1; i != 0; ) |
| { |
| ssize_t |
| v; |
| |
| i--; |
| status=GetMatrixElement(canny_cache,i,0,&edge); |
| if (status == MagickFalse) |
| return(MagickFalse); |
| for (v=(-1); v <= 1; v++) |
| { |
| ssize_t |
| u; |
| |
| for (u=(-1); u <= 1; u++) |
| { |
| if ((u == 0) && (v == 0)) |
| continue; |
| if (IsAuthenticPixel(edge_image,edge.x+u,edge.y+v) == MagickFalse) |
| continue; |
| /* |
| Not an edge if gradient value is below the lower threshold. |
| */ |
| q=GetCacheViewAuthenticPixels(edge_view,edge.x+u,edge.y+v,1,1, |
| exception); |
| if (q == (Quantum *) NULL) |
| return(MagickFalse); |
| status=GetMatrixElement(canny_cache,edge.x+u,edge.y+v,&pixel); |
| if (status == MagickFalse) |
| return(MagickFalse); |
| if ((GetPixelIntensity(edge_image,q) == 0.0) && |
| (pixel.intensity >= lower_threshold)) |
| { |
| *q=QuantumRange; |
| status=SyncCacheViewAuthenticPixels(edge_view,exception); |
| if (status == MagickFalse) |
| return(MagickFalse); |
| edge.x+=u; |
| edge.y+=v; |
| status=SetMatrixElement(canny_cache,i,0,&edge); |
| if (status == MagickFalse) |
| return(MagickFalse); |
| i++; |
| } |
| } |
| } |
| } |
| return(MagickTrue); |
| } |
| |
| MagickExport Image *CannyEdgeImage(const Image *image,const double radius, |
| const double sigma,const double lower_percent,const double upper_percent, |
| ExceptionInfo *exception) |
| { |
| #define CannyEdgeImageTag "CannyEdge/Image" |
| |
| CacheView |
| *edge_view; |
| |
| CannyInfo |
| element; |
| |
| char |
| geometry[MagickPathExtent]; |
| |
| double |
| lower_threshold, |
| max, |
| min, |
| upper_threshold; |
| |
| Image |
| *edge_image; |
| |
| KernelInfo |
| *kernel_info; |
| |
| MagickBooleanType |
| status; |
| |
| MagickOffsetType |
| progress; |
| |
| MatrixInfo |
| *canny_cache; |
| |
| ssize_t |
| y; |
| |
| assert(image != (const Image *) NULL); |
| assert(image->signature == MagickCoreSignature); |
| if (image->debug != MagickFalse) |
| (void) LogMagickEvent(TraceEvent,GetMagickModule(),"%s",image->filename); |
| assert(exception != (ExceptionInfo *) NULL); |
| assert(exception->signature == MagickCoreSignature); |
| /* |
| Filter out noise. |
| */ |
| (void) FormatLocaleString(geometry,MagickPathExtent, |
| "blur:%.20gx%.20g;blur:%.20gx%.20g+90",radius,sigma,radius,sigma); |
| kernel_info=AcquireKernelInfo(geometry,exception); |
| if (kernel_info == (KernelInfo *) NULL) |
| ThrowImageException(ResourceLimitError,"MemoryAllocationFailed"); |
| edge_image=MorphologyImage(image,ConvolveMorphology,1,kernel_info,exception); |
| kernel_info=DestroyKernelInfo(kernel_info); |
| if (edge_image == (Image *) NULL) |
| return((Image *) NULL); |
| if (TransformImageColorspace(edge_image,GRAYColorspace,exception) == MagickFalse) |
| { |
| edge_image=DestroyImage(edge_image); |
| return((Image *) NULL); |
| } |
| (void) SetImageAlphaChannel(edge_image,OffAlphaChannel,exception); |
| /* |
| Find the intensity gradient of the image. |
| */ |
| canny_cache=AcquireMatrixInfo(edge_image->columns,edge_image->rows, |
| sizeof(CannyInfo),exception); |
| if (canny_cache == (MatrixInfo *) NULL) |
| { |
| edge_image=DestroyImage(edge_image); |
| return((Image *) NULL); |
| } |
| status=MagickTrue; |
| edge_view=AcquireVirtualCacheView(edge_image,exception); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(edge_image,edge_image,edge_image->rows,1) |
| #endif |
| for (y=0; y < (ssize_t) edge_image->rows; y++) |
| { |
| register const Quantum |
| *magick_restrict p; |
| |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(edge_view,0,y,edge_image->columns+1,2, |
| exception); |
| if (p == (const Quantum *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| for (x=0; x < (ssize_t) edge_image->columns; x++) |
| { |
| CannyInfo |
| pixel; |
| |
| double |
| dx, |
| dy; |
| |
| register const Quantum |
| *magick_restrict kernel_pixels; |
| |
| ssize_t |
| v; |
| |
| static double |
| Gx[2][2] = |
| { |
| { -1.0, +1.0 }, |
| { -1.0, +1.0 } |
| }, |
| Gy[2][2] = |
| { |
| { +1.0, +1.0 }, |
| { -1.0, -1.0 } |
| }; |
| |
| (void) ResetMagickMemory(&pixel,0,sizeof(pixel)); |
| dx=0.0; |
| dy=0.0; |
| kernel_pixels=p; |
| for (v=0; v < 2; v++) |
| { |
| ssize_t |
| u; |
| |
| for (u=0; u < 2; u++) |
| { |
| double |
| intensity; |
| |
| intensity=GetPixelIntensity(edge_image,kernel_pixels+u); |
| dx+=0.5*Gx[v][u]*intensity; |
| dy+=0.5*Gy[v][u]*intensity; |
| } |
| kernel_pixels+=edge_image->columns+1; |
| } |
| pixel.magnitude=hypot(dx,dy); |
| pixel.orientation=0; |
| if (fabs(dx) > MagickEpsilon) |
| { |
| double |
| slope; |
| |
| slope=dy/dx; |
| if (slope < 0.0) |
| { |
| if (slope < -2.41421356237) |
| pixel.orientation=0; |
| else |
| if (slope < -0.414213562373) |
| pixel.orientation=1; |
| else |
| pixel.orientation=2; |
| } |
| else |
| { |
| if (slope > 2.41421356237) |
| pixel.orientation=0; |
| else |
| if (slope > 0.414213562373) |
| pixel.orientation=3; |
| else |
| pixel.orientation=2; |
| } |
| } |
| if (SetMatrixElement(canny_cache,x,y,&pixel) == MagickFalse) |
| continue; |
| p+=GetPixelChannels(edge_image); |
| } |
| } |
| edge_view=DestroyCacheView(edge_view); |
| /* |
| Non-maxima suppression, remove pixels that are not considered to be part |
| of an edge. |
| */ |
| progress=0; |
| (void) GetMatrixElement(canny_cache,0,0,&element); |
| max=element.intensity; |
| min=element.intensity; |
| edge_view=AcquireAuthenticCacheView(edge_image,exception); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(edge_image,edge_image,edge_image->rows,1) |
| #endif |
| for (y=0; y < (ssize_t) edge_image->rows; y++) |
| { |
| register Quantum |
| *magick_restrict q; |
| |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| q=GetCacheViewAuthenticPixels(edge_view,0,y,edge_image->columns,1, |
| exception); |
| if (q == (Quantum *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| for (x=0; x < (ssize_t) edge_image->columns; x++) |
| { |
| CannyInfo |
| alpha_pixel, |
| beta_pixel, |
| pixel; |
| |
| (void) GetMatrixElement(canny_cache,x,y,&pixel); |
| switch (pixel.orientation) |
| { |
| case 0: |
| default: |
| { |
| /* |
| 0 degrees, north and south. |
| */ |
| (void) GetMatrixElement(canny_cache,x,y-1,&alpha_pixel); |
| (void) GetMatrixElement(canny_cache,x,y+1,&beta_pixel); |
| break; |
| } |
| case 1: |
| { |
| /* |
| 45 degrees, northwest and southeast. |
| */ |
| (void) GetMatrixElement(canny_cache,x-1,y-1,&alpha_pixel); |
| (void) GetMatrixElement(canny_cache,x+1,y+1,&beta_pixel); |
| break; |
| } |
| case 2: |
| { |
| /* |
| 90 degrees, east and west. |
| */ |
| (void) GetMatrixElement(canny_cache,x-1,y,&alpha_pixel); |
| (void) GetMatrixElement(canny_cache,x+1,y,&beta_pixel); |
| break; |
| } |
| case 3: |
| { |
| /* |
| 135 degrees, northeast and southwest. |
| */ |
| (void) GetMatrixElement(canny_cache,x+1,y-1,&beta_pixel); |
| (void) GetMatrixElement(canny_cache,x-1,y+1,&alpha_pixel); |
| break; |
| } |
| } |
| pixel.intensity=pixel.magnitude; |
| if ((pixel.magnitude < alpha_pixel.magnitude) || |
| (pixel.magnitude < beta_pixel.magnitude)) |
| pixel.intensity=0; |
| (void) SetMatrixElement(canny_cache,x,y,&pixel); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp critical (MagickCore_CannyEdgeImage) |
| #endif |
| { |
| if (pixel.intensity < min) |
| min=pixel.intensity; |
| if (pixel.intensity > max) |
| max=pixel.intensity; |
| } |
| *q=0; |
| q+=GetPixelChannels(edge_image); |
| } |
| if (SyncCacheViewAuthenticPixels(edge_view,exception) == MagickFalse) |
| status=MagickFalse; |
| } |
| edge_view=DestroyCacheView(edge_view); |
| /* |
| Estimate hysteresis threshold. |
| */ |
| lower_threshold=lower_percent*(max-min)+min; |
| upper_threshold=upper_percent*(max-min)+min; |
| /* |
| Hysteresis threshold. |
| */ |
| edge_view=AcquireAuthenticCacheView(edge_image,exception); |
| for (y=0; y < (ssize_t) edge_image->rows; y++) |
| { |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| for (x=0; x < (ssize_t) edge_image->columns; x++) |
| { |
| CannyInfo |
| pixel; |
| |
| register const Quantum |
| *magick_restrict p; |
| |
| /* |
| Edge if pixel gradient higher than upper threshold. |
| */ |
| p=GetCacheViewVirtualPixels(edge_view,x,y,1,1,exception); |
| if (p == (const Quantum *) NULL) |
| continue; |
| status=GetMatrixElement(canny_cache,x,y,&pixel); |
| if (status == MagickFalse) |
| continue; |
| if ((GetPixelIntensity(edge_image,p) == 0.0) && |
| (pixel.intensity >= upper_threshold)) |
| status=TraceEdges(edge_image,edge_view,canny_cache,x,y,lower_threshold, |
| exception); |
| } |
| if (image->progress_monitor != (MagickProgressMonitor) NULL) |
| { |
| MagickBooleanType |
| proceed; |
| |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp critical (MagickCore_CannyEdgeImage) |
| #endif |
| proceed=SetImageProgress(image,CannyEdgeImageTag,progress++, |
| image->rows); |
| if (proceed == MagickFalse) |
| status=MagickFalse; |
| } |
| } |
| edge_view=DestroyCacheView(edge_view); |
| /* |
| Free resources. |
| */ |
| canny_cache=DestroyMatrixInfo(canny_cache); |
| return(edge_image); |
| } |
| |
| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % G e t I m a g e F e a t u r e s % |
| % % |
| % % |
| % % |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % |
| % GetImageFeatures() 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=GetImageFeatures(image,1,exception); |
| % contrast=channel_features[RedPixelChannel].contrast[0]; |
| % |
| % Use MagickRelinquishMemory() to free the features buffer. |
| % |
| % The format of the GetImageFeatures method is: |
| % |
| % ChannelFeatures *GetImageFeatures(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 double MagickLog10(const double x) |
| { |
| #define Log10Epsilon (1.0e-11) |
| |
| if (fabs(x) < Log10Epsilon) |
| return(log10(Log10Epsilon)); |
| return(log10(fabs(x))); |
| } |
| |
| MagickExport ChannelFeatures *GetImageFeatures(const Image *image, |
| const size_t distance,ExceptionInfo *exception) |
| { |
| typedef struct _ChannelStatistics |
| { |
| PixelInfo |
| 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; |
| |
| PixelPacket |
| gray, |
| *grays; |
| |
| MagickBooleanType |
| status; |
| |
| register ssize_t |
| i, |
| r; |
| |
| size_t |
| length; |
| |
| unsigned int |
| number_grays; |
| |
| assert(image != (Image *) NULL); |
| assert(image->signature == MagickCoreSignature); |
| if (image->debug != MagickFalse) |
| (void) LogMagickEvent(TraceEvent,GetMagickModule(),"%s",image->filename); |
| if ((image->columns < (distance+1)) || (image->rows < (distance+1))) |
| return((ChannelFeatures *) NULL); |
| length=MaxPixelChannels+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=(PixelPacket *) AcquireQuantumMemory(MaxMap+1UL,sizeof(*grays)); |
| if (grays == (PixelPacket *) 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].alpha=(~0U); |
| grays[i].black=(~0U); |
| } |
| status=MagickTrue; |
| image_view=AcquireVirtualCacheView(image,exception); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(image,image,image->rows,1) |
| #endif |
| for (r=0; r < (ssize_t) image->rows; r++) |
| { |
| register const Quantum |
| *magick_restrict p; |
| |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(image_view,0,r,image->columns,1,exception); |
| if (p == (const Quantum *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| for (x=0; x < (ssize_t) image->columns; x++) |
| { |
| grays[ScaleQuantumToMap(GetPixelRed(image,p))].red= |
| ScaleQuantumToMap(GetPixelRed(image,p)); |
| grays[ScaleQuantumToMap(GetPixelGreen(image,p))].green= |
| ScaleQuantumToMap(GetPixelGreen(image,p)); |
| grays[ScaleQuantumToMap(GetPixelBlue(image,p))].blue= |
| ScaleQuantumToMap(GetPixelBlue(image,p)); |
| if (image->colorspace == CMYKColorspace) |
| grays[ScaleQuantumToMap(GetPixelBlack(image,p))].black= |
| ScaleQuantumToMap(GetPixelBlack(image,p)); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| grays[ScaleQuantumToMap(GetPixelAlpha(image,p))].alpha= |
| ScaleQuantumToMap(GetPixelAlpha(image,p)); |
| p+=GetPixelChannels(image); |
| } |
| } |
| image_view=DestroyCacheView(image_view); |
| if (status == MagickFalse) |
| { |
| grays=(PixelPacket *) 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->colorspace == CMYKColorspace) |
| if (grays[i].black != ~0U) |
| grays[gray.black++].black=grays[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| if (grays[i].alpha != ~0U) |
| grays[gray.alpha++].alpha=grays[i].alpha; |
| } |
| /* |
| 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->colorspace == CMYKColorspace) |
| if (gray.black > number_grays) |
| number_grays=gray.black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| if (gray.alpha > number_grays) |
| number_grays=gray.alpha; |
| 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=(PixelPacket *) 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=(PixelPacket *) 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=AcquireVirtualCacheView(image,exception); |
| for (r=0; r < (ssize_t) image->rows; r++) |
| { |
| register const Quantum |
| *magick_restrict p; |
| |
| register ssize_t |
| x; |
| |
| ssize_t |
| offset, |
| u, |
| v; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(image_view,-(ssize_t) distance,r,image->columns+ |
| 2*distance,distance+2,exception); |
| if (p == (const Quantum *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| p+=distance*GetPixelChannels(image);; |
| 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(GetPixelRed(image,p))) |
| u++; |
| while (grays[v].red != ScaleQuantumToMap(GetPixelRed(image,p+offset*GetPixelChannels(image)))) |
| v++; |
| cooccurrence[u][v].direction[i].red++; |
| cooccurrence[v][u].direction[i].red++; |
| u=0; |
| v=0; |
| while (grays[u].green != ScaleQuantumToMap(GetPixelGreen(image,p))) |
| u++; |
| while (grays[v].green != ScaleQuantumToMap(GetPixelGreen(image,p+offset*GetPixelChannels(image)))) |
| v++; |
| cooccurrence[u][v].direction[i].green++; |
| cooccurrence[v][u].direction[i].green++; |
| u=0; |
| v=0; |
| while (grays[u].blue != ScaleQuantumToMap(GetPixelBlue(image,p))) |
| u++; |
| while (grays[v].blue != ScaleQuantumToMap(GetPixelBlue(image,p+offset*GetPixelChannels(image)))) |
| v++; |
| cooccurrence[u][v].direction[i].blue++; |
| cooccurrence[v][u].direction[i].blue++; |
| if (image->colorspace == CMYKColorspace) |
| { |
| u=0; |
| v=0; |
| while (grays[u].black != ScaleQuantumToMap(GetPixelBlack(image,p))) |
| u++; |
| while (grays[v].black != ScaleQuantumToMap(GetPixelBlack(image,p+offset*GetPixelChannels(image)))) |
| v++; |
| cooccurrence[u][v].direction[i].black++; |
| cooccurrence[v][u].direction[i].black++; |
| } |
| if (image->alpha_trait != UndefinedPixelTrait) |
| { |
| u=0; |
| v=0; |
| while (grays[u].alpha != ScaleQuantumToMap(GetPixelAlpha(image,p))) |
| u++; |
| while (grays[v].alpha != ScaleQuantumToMap(GetPixelAlpha(image,p+offset*GetPixelChannels(image)))) |
| v++; |
| cooccurrence[u][v].direction[i].alpha++; |
| cooccurrence[v][u].direction[i].alpha++; |
| } |
| } |
| p+=GetPixelChannels(image); |
| } |
| } |
| grays=(PixelPacket *) 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. |
| */ |
| for (i=0; i < 4; i++) |
| { |
| double |
| normalize; |
| |
| register ssize_t |
| y; |
| |
| 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; |
| } |
| } |
| normalize=PerceptibleReciprocal(normalize); |
| 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->colorspace == CMYKColorspace) |
| cooccurrence[x][y].direction[i].black*=normalize; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| cooccurrence[x][y].direction[i].alpha*=normalize; |
| } |
| } |
| } |
| /* |
| Compute texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(image,image,number_grays,1) |
| #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[RedPixelChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].red* |
| cooccurrence[x][y].direction[i].red; |
| channel_features[GreenPixelChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].green* |
| cooccurrence[x][y].direction[i].green; |
| channel_features[BluePixelChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].blue* |
| cooccurrence[x][y].direction[i].blue; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].black* |
| cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].angular_second_moment[i]+= |
| cooccurrence[x][y].direction[i].alpha* |
| cooccurrence[x][y].direction[i].alpha; |
| /* |
| 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->colorspace == CMYKColorspace) |
| sum[y].direction[i].black+=cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| sum[y].direction[i].alpha+=cooccurrence[x][y].direction[i].alpha; |
| 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->colorspace == CMYKColorspace) |
| correlation.direction[i].black+=x*y* |
| cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| correlation.direction[i].alpha+=x*y* |
| cooccurrence[x][y].direction[i].alpha; |
| /* |
| Inverse Difference Moment. |
| */ |
| channel_features[RedPixelChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].red/((y-x)*(y-x)+1); |
| channel_features[GreenPixelChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].green/((y-x)*(y-x)+1); |
| channel_features[BluePixelChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].blue/((y-x)*(y-x)+1); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].black/((y-x)*(y-x)+1); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].inverse_difference_moment[i]+= |
| cooccurrence[x][y].direction[i].alpha/((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->colorspace == CMYKColorspace) |
| density_xy[y+x+2].direction[i].black+= |
| cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| density_xy[y+x+2].direction[i].alpha+= |
| cooccurrence[x][y].direction[i].alpha; |
| /* |
| Entropy. |
| */ |
| channel_features[RedPixelChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].red* |
| MagickLog10(cooccurrence[x][y].direction[i].red); |
| channel_features[GreenPixelChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].green* |
| MagickLog10(cooccurrence[x][y].direction[i].green); |
| channel_features[BluePixelChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].blue* |
| MagickLog10(cooccurrence[x][y].direction[i].blue); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].black* |
| MagickLog10(cooccurrence[x][y].direction[i].black); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].entropy[i]-= |
| cooccurrence[x][y].direction[i].alpha* |
| MagickLog10(cooccurrence[x][y].direction[i].alpha); |
| /* |
| 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->alpha_trait != UndefinedPixelTrait) |
| density_x[x].direction[i].alpha+= |
| cooccurrence[x][y].direction[i].alpha; |
| if (image->colorspace == CMYKColorspace) |
| density_x[x].direction[i].black+= |
| cooccurrence[x][y].direction[i].black; |
| 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->colorspace == CMYKColorspace) |
| density_y[y].direction[i].black+= |
| cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| density_y[y].direction[i].alpha+= |
| cooccurrence[x][y].direction[i].alpha; |
| } |
| 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->colorspace == CMYKColorspace) |
| { |
| mean.direction[i].black+=y*sum[y].direction[i].black; |
| sum_squares.direction[i].black+=y*y*sum[y].direction[i].black; |
| } |
| if (image->alpha_trait != UndefinedPixelTrait) |
| { |
| mean.direction[i].alpha+=y*sum[y].direction[i].alpha; |
| sum_squares.direction[i].alpha+=y*y*sum[y].direction[i].alpha; |
| } |
| } |
| /* |
| Correlation: measure of linear-dependencies in the image. |
| */ |
| channel_features[RedPixelChannel].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[GreenPixelChannel].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[BluePixelChannel].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->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].correlation[i]= |
| (correlation.direction[i].black-mean.direction[i].black* |
| mean.direction[i].black)/(sqrt(sum_squares.direction[i].black- |
| (mean.direction[i].black*mean.direction[i].black))*sqrt( |
| sum_squares.direction[i].black-(mean.direction[i].black* |
| mean.direction[i].black))); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].correlation[i]= |
| (correlation.direction[i].alpha-mean.direction[i].alpha* |
| mean.direction[i].alpha)/(sqrt(sum_squares.direction[i].alpha- |
| (mean.direction[i].alpha*mean.direction[i].alpha))*sqrt( |
| sum_squares.direction[i].alpha-(mean.direction[i].alpha* |
| mean.direction[i].alpha))); |
| } |
| /* |
| Compute more texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(image,image,number_grays,1) |
| #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[RedPixelChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].red; |
| channel_features[GreenPixelChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].green; |
| channel_features[BluePixelChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].blue; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].sum_average[i]+= |
| x*density_xy[x].direction[i].alpha; |
| /* |
| Sum entropy. |
| */ |
| channel_features[RedPixelChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].red* |
| MagickLog10(density_xy[x].direction[i].red); |
| channel_features[GreenPixelChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].green* |
| MagickLog10(density_xy[x].direction[i].green); |
| channel_features[BluePixelChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].blue* |
| MagickLog10(density_xy[x].direction[i].blue); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].black* |
| MagickLog10(density_xy[x].direction[i].black); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].sum_entropy[i]-= |
| density_xy[x].direction[i].alpha* |
| MagickLog10(density_xy[x].direction[i].alpha); |
| /* |
| Sum variance. |
| */ |
| channel_features[RedPixelChannel].sum_variance[i]+= |
| (x-channel_features[RedPixelChannel].sum_entropy[i])* |
| (x-channel_features[RedPixelChannel].sum_entropy[i])* |
| density_xy[x].direction[i].red; |
| channel_features[GreenPixelChannel].sum_variance[i]+= |
| (x-channel_features[GreenPixelChannel].sum_entropy[i])* |
| (x-channel_features[GreenPixelChannel].sum_entropy[i])* |
| density_xy[x].direction[i].green; |
| channel_features[BluePixelChannel].sum_variance[i]+= |
| (x-channel_features[BluePixelChannel].sum_entropy[i])* |
| (x-channel_features[BluePixelChannel].sum_entropy[i])* |
| density_xy[x].direction[i].blue; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].sum_variance[i]+= |
| (x-channel_features[BlackPixelChannel].sum_entropy[i])* |
| (x-channel_features[BlackPixelChannel].sum_entropy[i])* |
| density_xy[x].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].sum_variance[i]+= |
| (x-channel_features[AlphaPixelChannel].sum_entropy[i])* |
| (x-channel_features[AlphaPixelChannel].sum_entropy[i])* |
| density_xy[x].direction[i].alpha; |
| } |
| } |
| /* |
| Compute more texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(image,image,number_grays,1) |
| #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->colorspace == CMYKColorspace) |
| variance.direction[i].black+=(y-mean.direction[i].black+1)* |
| (y-mean.direction[i].black+1)*cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| variance.direction[i].alpha+=(y-mean.direction[i].alpha+1)* |
| (y-mean.direction[i].alpha+1)* |
| cooccurrence[x][y].direction[i].alpha; |
| /* |
| 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->colorspace == CMYKColorspace) |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].black+= |
| cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| density_xy[MagickAbsoluteValue(y-x)].direction[i].alpha+= |
| cooccurrence[x][y].direction[i].alpha; |
| /* |
| Information Measures of Correlation. |
| */ |
| entropy_xy.direction[i].red-=cooccurrence[x][y].direction[i].red* |
| MagickLog10(cooccurrence[x][y].direction[i].red); |
| entropy_xy.direction[i].green-=cooccurrence[x][y].direction[i].green* |
| MagickLog10(cooccurrence[x][y].direction[i].green); |
| entropy_xy.direction[i].blue-=cooccurrence[x][y].direction[i].blue* |
| MagickLog10(cooccurrence[x][y].direction[i].blue); |
| if (image->colorspace == CMYKColorspace) |
| entropy_xy.direction[i].black-=cooccurrence[x][y].direction[i].black* |
| MagickLog10(cooccurrence[x][y].direction[i].black); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| entropy_xy.direction[i].alpha-= |
| cooccurrence[x][y].direction[i].alpha*MagickLog10( |
| cooccurrence[x][y].direction[i].alpha); |
| entropy_xy1.direction[i].red-=(cooccurrence[x][y].direction[i].red* |
| MagickLog10(density_x[x].direction[i].red*density_y[y].direction[i].red)); |
| entropy_xy1.direction[i].green-=(cooccurrence[x][y].direction[i].green* |
| MagickLog10(density_x[x].direction[i].green* |
| density_y[y].direction[i].green)); |
| entropy_xy1.direction[i].blue-=(cooccurrence[x][y].direction[i].blue* |
| MagickLog10(density_x[x].direction[i].blue*density_y[y].direction[i].blue)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_xy1.direction[i].black-=( |
| cooccurrence[x][y].direction[i].black*MagickLog10( |
| density_x[x].direction[i].black*density_y[y].direction[i].black)); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| entropy_xy1.direction[i].alpha-=( |
| cooccurrence[x][y].direction[i].alpha*MagickLog10( |
| density_x[x].direction[i].alpha*density_y[y].direction[i].alpha)); |
| entropy_xy2.direction[i].red-=(density_x[x].direction[i].red* |
| density_y[y].direction[i].red*MagickLog10(density_x[x].direction[i].red* |
| density_y[y].direction[i].red)); |
| entropy_xy2.direction[i].green-=(density_x[x].direction[i].green* |
| density_y[y].direction[i].green*MagickLog10(density_x[x].direction[i].green* |
| density_y[y].direction[i].green)); |
| entropy_xy2.direction[i].blue-=(density_x[x].direction[i].blue* |
| density_y[y].direction[i].blue*MagickLog10(density_x[x].direction[i].blue* |
| density_y[y].direction[i].blue)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_xy2.direction[i].black-=(density_x[x].direction[i].black* |
| density_y[y].direction[i].black*MagickLog10( |
| density_x[x].direction[i].black*density_y[y].direction[i].black)); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| entropy_xy2.direction[i].alpha-=(density_x[x].direction[i].alpha* |
| density_y[y].direction[i].alpha*MagickLog10( |
| density_x[x].direction[i].alpha*density_y[y].direction[i].alpha)); |
| } |
| } |
| channel_features[RedPixelChannel].variance_sum_of_squares[i]= |
| variance.direction[i].red; |
| channel_features[GreenPixelChannel].variance_sum_of_squares[i]= |
| variance.direction[i].green; |
| channel_features[BluePixelChannel].variance_sum_of_squares[i]= |
| variance.direction[i].blue; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].variance_sum_of_squares[i]= |
| variance.direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].variance_sum_of_squares[i]= |
| variance.direction[i].alpha; |
| } |
| /* |
| 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(static,4) shared(status) \ |
| magick_threads(image,image,number_grays,1) |
| #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->colorspace == CMYKColorspace) |
| variance.direction[i].black+=density_xy[x].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| variance.direction[i].alpha+=density_xy[x].direction[i].alpha; |
| 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->colorspace == CMYKColorspace) |
| sum_squares.direction[i].black+=density_xy[x].direction[i].black* |
| density_xy[x].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| sum_squares.direction[i].alpha+=density_xy[x].direction[i].alpha* |
| density_xy[x].direction[i].alpha; |
| /* |
| Difference entropy. |
| */ |
| channel_features[RedPixelChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].red* |
| MagickLog10(density_xy[x].direction[i].red); |
| channel_features[GreenPixelChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].green* |
| MagickLog10(density_xy[x].direction[i].green); |
| channel_features[BluePixelChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].blue* |
| MagickLog10(density_xy[x].direction[i].blue); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].black* |
| MagickLog10(density_xy[x].direction[i].black); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].difference_entropy[i]-= |
| density_xy[x].direction[i].alpha* |
| MagickLog10(density_xy[x].direction[i].alpha); |
| /* |
| Information Measures of Correlation. |
| */ |
| entropy_x.direction[i].red-=(density_x[x].direction[i].red* |
| MagickLog10(density_x[x].direction[i].red)); |
| entropy_x.direction[i].green-=(density_x[x].direction[i].green* |
| MagickLog10(density_x[x].direction[i].green)); |
| entropy_x.direction[i].blue-=(density_x[x].direction[i].blue* |
| MagickLog10(density_x[x].direction[i].blue)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_x.direction[i].black-=(density_x[x].direction[i].black* |
| MagickLog10(density_x[x].direction[i].black)); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| entropy_x.direction[i].alpha-=(density_x[x].direction[i].alpha* |
| MagickLog10(density_x[x].direction[i].alpha)); |
| entropy_y.direction[i].red-=(density_y[x].direction[i].red* |
| MagickLog10(density_y[x].direction[i].red)); |
| entropy_y.direction[i].green-=(density_y[x].direction[i].green* |
| MagickLog10(density_y[x].direction[i].green)); |
| entropy_y.direction[i].blue-=(density_y[x].direction[i].blue* |
| MagickLog10(density_y[x].direction[i].blue)); |
| if (image->colorspace == CMYKColorspace) |
| entropy_y.direction[i].black-=(density_y[x].direction[i].black* |
| MagickLog10(density_y[x].direction[i].black)); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| entropy_y.direction[i].alpha-=(density_y[x].direction[i].alpha* |
| MagickLog10(density_y[x].direction[i].alpha)); |
| } |
| /* |
| Difference variance. |
| */ |
| channel_features[RedPixelChannel].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[GreenPixelChannel].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[BluePixelChannel].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->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].black)- |
| (variance.direction[i].black*variance.direction[i].black))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].difference_variance[i]= |
| (((double) number_grays*number_grays*sum_squares.direction[i].alpha)- |
| (variance.direction[i].alpha*variance.direction[i].alpha))/ |
| ((double) number_grays*number_grays*number_grays*number_grays); |
| /* |
| Information Measures of Correlation. |
| */ |
| channel_features[RedPixelChannel].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[GreenPixelChannel].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[BluePixelChannel].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->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].black-entropy_xy1.direction[i].black)/ |
| (entropy_x.direction[i].black > entropy_y.direction[i].black ? |
| entropy_x.direction[i].black : entropy_y.direction[i].black); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].measure_of_correlation_1[i]= |
| (entropy_xy.direction[i].alpha-entropy_xy1.direction[i].alpha)/ |
| (entropy_x.direction[i].alpha > entropy_y.direction[i].alpha ? |
| entropy_x.direction[i].alpha : entropy_y.direction[i].alpha); |
| channel_features[RedPixelChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(double) (entropy_xy2.direction[i].red- |
| entropy_xy.direction[i].red))))); |
| channel_features[GreenPixelChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(double) (entropy_xy2.direction[i].green- |
| entropy_xy.direction[i].green))))); |
| channel_features[BluePixelChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(double) (entropy_xy2.direction[i].blue- |
| entropy_xy.direction[i].blue))))); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(double) (entropy_xy2.direction[i].black- |
| entropy_xy.direction[i].black))))); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].measure_of_correlation_2[i]= |
| (sqrt(fabs(1.0-exp(-2.0*(double) (entropy_xy2.direction[i].alpha- |
| entropy_xy.direction[i].alpha))))); |
| } |
| /* |
| Compute more texture features. |
| */ |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status) \ |
| magick_threads(image,image,number_grays,1) |
| #endif |
| for (i=0; i < 4; i++) |
| { |
| ssize_t |
| z; |
| |
| 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->colorspace == CMYKColorspace) |
| pixel.direction[i].black+=cooccurrence[x][y].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| pixel.direction[i].alpha+= |
| cooccurrence[x][y].direction[i].alpha; |
| } |
| /* |
| 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->colorspace == CMYKColorspace) |
| Q[z][y].direction[i].black+=cooccurrence[z][x].direction[i].black* |
| cooccurrence[y][x].direction[i].black/ |
| density_x[z].direction[i].black/density_y[x].direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| Q[z][y].direction[i].alpha+= |
| cooccurrence[z][x].direction[i].alpha* |
| cooccurrence[y][x].direction[i].alpha/ |
| density_x[z].direction[i].alpha/ |
| density_y[x].direction[i].alpha; |
| } |
| } |
| channel_features[RedPixelChannel].contrast[i]+=z*z* |
| pixel.direction[i].red; |
| channel_features[GreenPixelChannel].contrast[i]+=z*z* |
| pixel.direction[i].green; |
| channel_features[BluePixelChannel].contrast[i]+=z*z* |
| pixel.direction[i].blue; |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].contrast[i]+=z*z* |
| pixel.direction[i].black; |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].contrast[i]+=z*z* |
| pixel.direction[i].alpha; |
| } |
| /* |
| Maximum Correlation Coefficient. |
| Future: return second largest eigenvalue of Q. |
| */ |
| channel_features[RedPixelChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| channel_features[GreenPixelChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| channel_features[BluePixelChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| if (image->colorspace == CMYKColorspace) |
| channel_features[BlackPixelChannel].maximum_correlation_coefficient[i]= |
| sqrt((double) -1.0); |
| if (image->alpha_trait != UndefinedPixelTrait) |
| channel_features[AlphaPixelChannel].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); |
| } |
| |
| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % H o u g h L i n e I m a g e % |
| % % |
| % % |
| % % |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % |
| % Use HoughLineImage() in conjunction with any binary edge extracted image (we |
| % recommand Canny) to identify lines in the image. The algorithm accumulates |
| % counts for every white pixel for every possible orientation (for angles from |
| % 0 to 179 in 1 degree increments) and distance from the center of the image to |
| % the corner (in 1 px increments) and stores the counts in an accumulator matrix |
| % of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next |
| % it searches this space for peaks in counts and converts the locations of the |
| % peaks to slope and intercept in the normal x,y input image space. Use the |
| % slope/intercepts to find the endpoints clipped to the bounds of the image. The |
| % lines are then drawn. The counts are a measure of the length of the lines |
| % |
| % The format of the HoughLineImage method is: |
| % |
| % Image *HoughLineImage(const Image *image,const size_t width, |
| % const size_t height,const size_t threshold,ExceptionInfo *exception) |
| % |
| % A description of each parameter follows: |
| % |
| % o image: the image. |
| % |
| % o width, height: find line pairs as local maxima in this neighborhood. |
| % |
| % o threshold: the line count threshold. |
| % |
| % o exception: return any errors or warnings in this structure. |
| % |
| */ |
| |
| static inline double MagickRound(double x) |
| { |
| /* |
| Round the fraction to nearest integer. |
| */ |
| if ((x-floor(x)) < (ceil(x)-x)) |
| return(floor(x)); |
| return(ceil(x)); |
| } |
| |
| static Image *RenderHoughLines(const ImageInfo *image_info,const size_t columns, |
| const size_t rows,ExceptionInfo *exception) |
| { |
| #define BoundingBox "viewbox" |
| |
| DrawInfo |
| *draw_info; |
| |
| Image |
| *image; |
| |
| MagickBooleanType |
| status; |
| |
| /* |
| Open image. |
| */ |
| image=AcquireImage(image_info,exception); |
| status=OpenBlob(image_info,image,ReadBinaryBlobMode,exception); |
| if (status == MagickFalse) |
| { |
| image=DestroyImageList(image); |
| return((Image *) NULL); |
| } |
| image->columns=columns; |
| image->rows=rows; |
| draw_info=CloneDrawInfo(image_info,(DrawInfo *) NULL); |
| draw_info->affine.sx=image->resolution.x == 0.0 ? 1.0 : image->resolution.x/ |
| DefaultResolution; |
| draw_info->affine.sy=image->resolution.y == 0.0 ? 1.0 : image->resolution.y/ |
| DefaultResolution; |
| image->columns=(size_t) (draw_info->affine.sx*image->columns); |
| image->rows=(size_t) (draw_info->affine.sy*image->rows); |
| status=SetImageExtent(image,image->columns,image->rows,exception); |
| if (status == MagickFalse) |
| return(DestroyImageList(image)); |
| if (SetImageBackgroundColor(image,exception) == MagickFalse) |
| { |
| image=DestroyImageList(image); |
| return((Image *) NULL); |
| } |
| /* |
| Render drawing. |
| */ |
| if (GetBlobStreamData(image) == (unsigned char *) NULL) |
| draw_info->primitive=FileToString(image->filename,~0UL,exception); |
| else |
| { |
| draw_info->primitive=(char *) AcquireMagickMemory((size_t) |
| GetBlobSize(image)+1); |
| if (draw_info->primitive != (char *) NULL) |
| { |
| (void) CopyMagickMemory(draw_info->primitive,GetBlobStreamData(image), |
| (size_t) GetBlobSize(image)); |
| draw_info->primitive[GetBlobSize(image)]='\0'; |
| } |
| } |
| (void) DrawImage(image,draw_info,exception); |
| draw_info=DestroyDrawInfo(draw_info); |
| (void) CloseBlob(image); |
| return(GetFirstImageInList(image)); |
| } |
| |
| MagickExport Image *HoughLineImage(const Image *image,const size_t width, |
| const size_t height,const size_t threshold,ExceptionInfo *exception) |
| { |
| #define HoughLineImageTag "HoughLine/Image" |
| |
| CacheView |
| *image_view; |
| |
| char |
| message[MagickPathExtent], |
| path[MagickPathExtent]; |
| |
| const char |
| *artifact; |
| |
| double |
| hough_height; |
| |
| Image |
| *lines_image = NULL; |
| |
| ImageInfo |
| *image_info; |
| |
| int |
| file; |
| |
| MagickBooleanType |
| status; |
| |
| MagickOffsetType |
| progress; |
| |
| MatrixInfo |
| *accumulator; |
| |
| PointInfo |
| center; |
| |
| register ssize_t |
| y; |
| |
| size_t |
| accumulator_height, |
| accumulator_width, |
| line_count; |
| |
| /* |
| Create the accumulator. |
| */ |
| assert(image != (const Image *) NULL); |
| assert(image->signature == MagickCoreSignature); |
| if (image->debug != MagickFalse) |
| (void) LogMagickEvent(TraceEvent,GetMagickModule(),"%s",image->filename); |
| assert(exception != (ExceptionInfo *) NULL); |
| assert(exception->signature == MagickCoreSignature); |
| accumulator_width=180; |
| hough_height=((sqrt(2.0)*(double) (image->rows > image->columns ? |
| image->rows : image->columns))/2.0); |
| accumulator_height=(size_t) (2.0*hough_height); |
| accumulator=AcquireMatrixInfo(accumulator_width,accumulator_height, |
| sizeof(double),exception); |
| if (accumulator == (MatrixInfo *) NULL) |
| ThrowImageException(ResourceLimitError,"MemoryAllocationFailed"); |
| if (NullMatrix(accumulator) == MagickFalse) |
| { |
| accumulator=DestroyMatrixInfo(accumulator); |
| ThrowImageException(ResourceLimitError,"MemoryAllocationFailed"); |
| } |
| /* |
| Populate the accumulator. |
| */ |
| status=MagickTrue; |
| progress=0; |
| center.x=(double) image->columns/2.0; |
| center.y=(double) image->rows/2.0; |
| image_view=AcquireVirtualCacheView(image,exception); |
| for (y=0; y < (ssize_t) image->rows; y++) |
| { |
| register const Quantum |
| *magick_restrict p; |
| |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(image_view,0,y,image->columns,1,exception); |
| if (p == (Quantum *) NULL) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| for (x=0; x < (ssize_t) image->columns; x++) |
| { |
| if (GetPixelIntensity(image,p) > (QuantumRange/2.0)) |
| { |
| register ssize_t |
| i; |
| |
| for (i=0; i < 180; i++) |
| { |
| double |
| count, |
| radius; |
| |
| radius=(((double) x-center.x)*cos(DegreesToRadians((double) i)))+ |
| (((double) y-center.y)*sin(DegreesToRadians((double) i))); |
| (void) GetMatrixElement(accumulator,i,(ssize_t) |
| MagickRound(radius+hough_height),&count); |
| count++; |
| (void) SetMatrixElement(accumulator,i,(ssize_t) |
| MagickRound(radius+hough_height),&count); |
| } |
| } |
| p+=GetPixelChannels(image); |
| } |
| if (image->progress_monitor != (MagickProgressMonitor) NULL) |
| { |
| MagickBooleanType |
| proceed; |
| |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp critical (MagickCore_CannyEdgeImage) |
| #endif |
| proceed=SetImageProgress(image,CannyEdgeImageTag,progress++, |
| image->rows); |
| if (proceed == MagickFalse) |
| status=MagickFalse; |
| } |
| } |
| image_view=DestroyCacheView(image_view); |
| if (status == MagickFalse) |
| { |
| accumulator=DestroyMatrixInfo(accumulator); |
| return((Image *) NULL); |
| } |
| /* |
| Generate line segments from accumulator. |
| */ |
| file=AcquireUniqueFileResource(path); |
| if (file == -1) |
| { |
| accumulator=DestroyMatrixInfo(accumulator); |
| return((Image *) NULL); |
| } |
| (void) FormatLocaleString(message,MagickPathExtent, |
| "# Hough line transform: %.20gx%.20g%+.20g\n",(double) width, |
| (double) height,(double) threshold); |
| if (write(file,message,strlen(message)) != (ssize_t) strlen(message)) |
| status=MagickFalse; |
| (void) FormatLocaleString(message,MagickPathExtent, |
| "viewbox 0 0 %.20g %.20g\n",(double) image->columns,(double) image->rows); |
| if (write(file,message,strlen(message)) != (ssize_t) strlen(message)) |
| status=MagickFalse; |
| line_count=image->columns > image->rows ? image->columns/4 : image->rows/4; |
| if (threshold != 0) |
| line_count=threshold; |
| for (y=0; y < (ssize_t) accumulator_height; y++) |
| { |
| register ssize_t |
| x; |
| |
| for (x=0; x < (ssize_t) accumulator_width; x++) |
| { |
| double |
| count; |
| |
| (void) GetMatrixElement(accumulator,x,y,&count); |
| if (count >= (double) line_count) |
| { |
| double |
| maxima; |
| |
| SegmentInfo |
| line; |
| |
| ssize_t |
| v; |
| |
| /* |
| Is point a local maxima? |
| */ |
| maxima=count; |
| for (v=(-((ssize_t) height/2)); v <= (((ssize_t) height/2)); v++) |
| { |
| ssize_t |
| u; |
| |
| for (u=(-((ssize_t) width/2)); u <= (((ssize_t) width/2)); u++) |
| { |
| if ((u != 0) || (v !=0)) |
| { |
| (void) GetMatrixElement(accumulator,x+u,y+v,&count); |
| if (count > maxima) |
| { |
| maxima=count; |
| break; |
| } |
| } |
| } |
| if (u < (ssize_t) (width/2)) |
| break; |
| } |
| (void) GetMatrixElement(accumulator,x,y,&count); |
| if (maxima > count) |
| continue; |
| if ((x >= 45) && (x <= 135)) |
| { |
| /* |
| y = (r-x cos(t))/sin(t) |
| */ |
| line.x1=0.0; |
| line.y1=((double) (y-(accumulator_height/2.0))-((line.x1- |
| (image->columns/2.0))*cos(DegreesToRadians((double) x))))/ |
| sin(DegreesToRadians((double) x))+(image->rows/2.0); |
| line.x2=(double) image->columns; |
| line.y2=((double) (y-(accumulator_height/2.0))-((line.x2- |
| (image->columns/2.0))*cos(DegreesToRadians((double) x))))/ |
| sin(DegreesToRadians((double) x))+(image->rows/2.0); |
| } |
| else |
| { |
| /* |
| x = (r-y cos(t))/sin(t) |
| */ |
| line.y1=0.0; |
| line.x1=((double) (y-(accumulator_height/2.0))-((line.y1- |
| (image->rows/2.0))*sin(DegreesToRadians((double) x))))/ |
| cos(DegreesToRadians((double) x))+(image->columns/2.0); |
| line.y2=(double) image->rows; |
| line.x2=((double) (y-(accumulator_height/2.0))-((line.y2- |
| (image->rows/2.0))*sin(DegreesToRadians((double) x))))/ |
| cos(DegreesToRadians((double) x))+(image->columns/2.0); |
| } |
| (void) FormatLocaleString(message,MagickPathExtent, |
| "line %g,%g %g,%g # %g\n",line.x1,line.y1,line.x2,line.y2,maxima); |
| if (write(file,message,strlen(message)) != (ssize_t) strlen(message)) |
| status=MagickFalse; |
| } |
| } |
| } |
| (void) close(file); |
| /* |
| Render lines to image canvas. |
| */ |
| image_info=AcquireImageInfo(); |
| image_info->background_color=image->background_color; |
| (void) FormatLocaleString(image_info->filename,MagickPathExtent,"%s",path); |
| artifact=GetImageArtifact(image,"background"); |
| if (artifact != (const char *) NULL) |
| (void) SetImageOption(image_info,"background",artifact); |
| artifact=GetImageArtifact(image,"fill"); |
| if (artifact != (const char *) NULL) |
| (void) SetImageOption(image_info,"fill",artifact); |
| artifact=GetImageArtifact(image,"stroke"); |
| if (artifact != (const char *) NULL) |
| (void) SetImageOption(image_info,"stroke",artifact); |
| artifact=GetImageArtifact(image,"strokewidth"); |
| if (artifact != (const char *) NULL) |
| (void) SetImageOption(image_info,"strokewidth",artifact); |
| lines_image=RenderHoughLines(image_info,image->columns,image->rows,exception); |
| artifact=GetImageArtifact(image,"hough-lines:accumulator"); |
| if ((lines_image != (Image *) NULL) && |
| (IsStringTrue(artifact) != MagickFalse)) |
| { |
| Image |
| *accumulator_image; |
| |
| accumulator_image=MatrixToImage(accumulator,exception); |
| if (accumulator_image != (Image *) NULL) |
| AppendImageToList(&lines_image,accumulator_image); |
| } |
| /* |
| Free resources. |
| */ |
| accumulator=DestroyMatrixInfo(accumulator); |
| image_info=DestroyImageInfo(image_info); |
| (void) RelinquishUniqueFileResource(path); |
| return(GetFirstImageInList(lines_image)); |
| } |
| |
| /* |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % % |
| % % |
| % % |
| % M e a n S h i f t I m a g e % |
| % % |
| % % |
| % % |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| % |
| % MeanShiftImage() delineate arbitrarily shaped clusters in the image. For |
| % each pixel, it visits all the pixels in the neighborhood specified by |
| % the window centered at the pixel and excludes those that are outside the |
| % radius=(window-1)/2 surrounding the pixel. From those pixels, it finds those |
| % that are within the specified color distance from the current mean, and |
| % computes a new x,y centroid from those coordinates and a new mean. This new |
| % x,y centroid is used as the center for a new window. This process iterates |
| % until it converges and the final mean is replaces the (original window |
| % center) pixel value. It repeats this process for the next pixel, etc., |
| % until it processes all pixels in the image. Results are typically better with |
| % colorspaces other than sRGB. We recommend YIQ, YUV or YCbCr. |
| % |
| % The format of the MeanShiftImage method is: |
| % |
| % Image *MeanShiftImage(const Image *image,const size_t width, |
| % const size_t height,const double color_distance, |
| % ExceptionInfo *exception) |
| % |
| % A description of each parameter follows: |
| % |
| % o image: the image. |
| % |
| % o width, height: find pixels in this neighborhood. |
| % |
| % o color_distance: the color distance. |
| % |
| % o exception: return any errors or warnings in this structure. |
| % |
| */ |
| MagickExport Image *MeanShiftImage(const Image *image,const size_t width, |
| const size_t height,const double color_distance,ExceptionInfo *exception) |
| { |
| #define MaxMeanShiftIterations 100 |
| #define MeanShiftImageTag "MeanShift/Image" |
| |
| CacheView |
| *image_view, |
| *mean_view, |
| *pixel_view; |
| |
| Image |
| *mean_image; |
| |
| MagickBooleanType |
| status; |
| |
| MagickOffsetType |
| progress; |
| |
| ssize_t |
| y; |
| |
| assert(image != (const Image *) NULL); |
| assert(image->signature == MagickCoreSignature); |
| if (image->debug != MagickFalse) |
| (void) LogMagickEvent(TraceEvent,GetMagickModule(),"%s",image->filename); |
| assert(exception != (ExceptionInfo *) NULL); |
| assert(exception->signature == MagickCoreSignature); |
| mean_image=CloneImage(image,image->columns,image->rows,MagickTrue,exception); |
| if (mean_image == (Image *) NULL) |
| return((Image *) NULL); |
| if (SetImageStorageClass(mean_image,DirectClass,exception) == MagickFalse) |
| { |
| mean_image=DestroyImage(mean_image); |
| return((Image *) NULL); |
| } |
| status=MagickTrue; |
| progress=0; |
| image_view=AcquireVirtualCacheView(image,exception); |
| pixel_view=AcquireVirtualCacheView(image,exception); |
| mean_view=AcquireAuthenticCacheView(mean_image,exception); |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp parallel for schedule(static,4) shared(status,progress) \ |
| magick_threads(mean_image,mean_image,mean_image->rows,1) |
| #endif |
| for (y=0; y < (ssize_t) mean_image->rows; y++) |
| { |
| register const Quantum |
| *magick_restrict p; |
| |
| register Quantum |
| *magick_restrict q; |
| |
| register ssize_t |
| x; |
| |
| if (status == MagickFalse) |
| continue; |
| p=GetCacheViewVirtualPixels(image_view,0,y,image->columns,1,exception); |
| q=GetCacheViewAuthenticPixels(mean_view,0,y,mean_image->columns,1, |
| exception); |
| if ((p == (const Quantum *) NULL) || (q == (Quantum *) NULL)) |
| { |
| status=MagickFalse; |
| continue; |
| } |
| for (x=0; x < (ssize_t) mean_image->columns; x++) |
| { |
| PixelInfo |
| mean_pixel, |
| previous_pixel; |
| |
| PointInfo |
| mean_location, |
| previous_location; |
| |
| register ssize_t |
| i; |
| |
| GetPixelInfo(image,&mean_pixel); |
| GetPixelInfoPixel(image,p,&mean_pixel); |
| mean_location.x=(double) x; |
| mean_location.y=(double) y; |
| for (i=0; i < MaxMeanShiftIterations; i++) |
| { |
| double |
| distance, |
| gamma; |
| |
| PixelInfo |
| sum_pixel; |
| |
| PointInfo |
| sum_location; |
| |
| ssize_t |
| count, |
| v; |
| |
| sum_location.x=0.0; |
| sum_location.y=0.0; |
| GetPixelInfo(image,&sum_pixel); |
| previous_location=mean_location; |
| previous_pixel=mean_pixel; |
| count=0; |
| for (v=(-((ssize_t) height/2)); v <= (((ssize_t) height/2)); v++) |
| { |
| ssize_t |
| u; |
| |
| for (u=(-((ssize_t) width/2)); u <= (((ssize_t) width/2)); u++) |
| { |
| if ((v*v+u*u) <= (ssize_t) ((width/2)*(height/2))) |
| { |
| PixelInfo |
| pixel; |
| |
| status=GetOneCacheViewVirtualPixelInfo(pixel_view,(ssize_t) |
| MagickRound(mean_location.x+u),(ssize_t) MagickRound( |
| mean_location.y+v),&pixel,exception); |
| distance=(mean_pixel.red-pixel.red)*(mean_pixel.red-pixel.red)+ |
| (mean_pixel.green-pixel.green)*(mean_pixel.green-pixel.green)+ |
| (mean_pixel.blue-pixel.blue)*(mean_pixel.blue-pixel.blue); |
| if (distance <= (color_distance*color_distance)) |
| { |
| sum_location.x+=mean_location.x+u; |
| sum_location.y+=mean_location.y+v; |
| sum_pixel.red+=pixel.red; |
| sum_pixel.green+=pixel.green; |
| sum_pixel.blue+=pixel.blue; |
| sum_pixel.alpha+=pixel.alpha; |
| count++; |
| } |
| } |
| } |
| } |
| gamma=1.0/count; |
| mean_location.x=gamma*sum_location.x; |
| mean_location.y=gamma*sum_location.y; |
| mean_pixel.red=gamma*sum_pixel.red; |
| mean_pixel.green=gamma*sum_pixel.green; |
| mean_pixel.blue=gamma*sum_pixel.blue; |
| mean_pixel.alpha=gamma*sum_pixel.alpha; |
| distance=(mean_location.x-previous_location.x)* |
| (mean_location.x-previous_location.x)+ |
| (mean_location.y-previous_location.y)* |
| (mean_location.y-previous_location.y)+ |
| 255.0*QuantumScale*(mean_pixel.red-previous_pixel.red)* |
| 255.0*QuantumScale*(mean_pixel.red-previous_pixel.red)+ |
| 255.0*QuantumScale*(mean_pixel.green-previous_pixel.green)* |
| 255.0*QuantumScale*(mean_pixel.green-previous_pixel.green)+ |
| 255.0*QuantumScale*(mean_pixel.blue-previous_pixel.blue)* |
| 255.0*QuantumScale*(mean_pixel.blue-previous_pixel.blue); |
| if (distance <= 3.0) |
| break; |
| } |
| SetPixelRed(mean_image,ClampToQuantum(mean_pixel.red),q); |
| SetPixelGreen(mean_image,ClampToQuantum(mean_pixel.green),q); |
| SetPixelBlue(mean_image,ClampToQuantum(mean_pixel.blue),q); |
| SetPixelAlpha(mean_image,ClampToQuantum(mean_pixel.alpha),q); |
| p+=GetPixelChannels(image); |
| q+=GetPixelChannels(mean_image); |
| } |
| if (SyncCacheViewAuthenticPixels(mean_view,exception) == MagickFalse) |
| status=MagickFalse; |
| if (image->progress_monitor != (MagickProgressMonitor) NULL) |
| { |
| MagickBooleanType |
| proceed; |
| |
| #if defined(MAGICKCORE_OPENMP_SUPPORT) |
| #pragma omp critical (MagickCore_MeanShiftImage) |
| #endif |
| proceed=SetImageProgress(image,MeanShiftImageTag,progress++, |
| image->rows); |
| if (proceed == MagickFalse) |
| status=MagickFalse; |
| } |
| } |
| mean_view=DestroyCacheView(mean_view); |
| pixel_view=DestroyCacheView(pixel_view); |
| image_view=DestroyCacheView(image_view); |
| return(mean_image); |
| } |