diff options
Diffstat (limited to 'macros/trainCascadeObjectDetector.sci')
| -rw-r--r-- | macros/trainCascadeObjectDetector.sci | 163 |
1 files changed, 163 insertions, 0 deletions
diff --git a/macros/trainCascadeObjectDetector.sci b/macros/trainCascadeObjectDetector.sci new file mode 100644 index 0000000..eb5369a --- /dev/null +++ b/macros/trainCascadeObjectDetector.sci @@ -0,0 +1,163 @@ +function trainCascadeObjectDetector(outputFolder,positiveInstances,negativeImages,varargin) +// It creates trained cascade XML file +// +// Calling Sequence +// trainCascadeObjectDetector(outputFolder,positiveInstances,"negativeImages") +// +// Parameters +// outputFolder: Folder name to store trained cascade (cascade.xml) and intermediate files +// positiveInstances: Array of structure with image file names (including path) and an M-by-4 matrix of bounding box. +// negativeImages: path to a negative images folder +// numPos: number of positive samples. Default-30 +// numNeg: number of negative samples. Default- 20 +// nsplits: number of splits. Default- 2 +// numStages: number of cascade stages to be trained. Default- 30 +// featureType: Type of features to be used, possible types are HAAR, LBP, HOG. Default-HAAR +// minHitRate: Minimal desired hit rate for each stage of the classifier and value in the range 0 and 1 inclusive. Default- 0.995 +// maxFalseAlarmRate: Maximal desired false alarm rate for each stage of the classifier and value in the range 0 and 1 inclusive. Default- 0.5 +// w: Width of training samples (in pixels). Default-25 +// h: Height of training samples (in pixels). Default-25 +// +// Description +// It produces dataset of positive samples in a file with .vec extension and negative samples are enumerated in a special text file in +// which each line contains an image filename of negative sample image. Negative images must not contain detected objects. +// +// By using these two files trainCascade will create cascade.xml file inside the outputFolder which is used to detect objects in an image. +// +// Examples +// positiveInstances(1)=struct("path",'image1.jpg',"bbox",[1 5 2 3]); +// positiveInstances(2)=struct("path",'image2.jpg',"bbox",[2 7 4 2]); +// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder"); +// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder","numStages",35,"featureType","HOG"); +// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder","minHitRate",0.9,"numPos",8,"numNeg",5); + + [lhs rhs]=argn(0); + if rhs<3 then + error(msprintf(" Not enough input arguments")) + elseif rhs>21 then + error(msprintf(" Too many input arguments to the function")) + elseif modulo(rhs-3,2) + error(msprintf(" wrong number of input arguments,name-value pairs not macthed")) + end + + //validating variables + if ~isdir(outputFolder) then + error(msprintf(" wrong input argument #1, existing directory expected")) + elseif ~isdir(negativeImages) + error(msprintf(" wrong input argument #3,existing directory expected")) + end + + //default values + numPos=30; + numNeg=20; + numStages=30; + nsplits=2; + featureType="HAAR" + minHitRate=0.995 + maxFalseAlarmRate=0.5 + w=25 + h=25 + + for i=1:2:rhs-3 + if strcmpi(varargin(i),"numPos")==0 then + i=i+1; + numPos=varargin(i); + if numPos<0 then + error(msprintf(" numPos value must be positive")) + end + elseif strcmpi(varargin(i),'numNeg')==0 then + i=i+1; + numNeg=varargin(i); + if numNeg<0 then + error(msprintf(" numNeg value must be positive")) + end + + elseif strcmpi(varargin(i),'numStages')==0 then + i=i+1; + numStages=varargin(i); + if numStages<0 then + error(msprintf(" numStages value must be positive")) + end + + elseif strcmpi(varargin(i),'nslits')==0 then + i=i+1; + nsplits=varargin(i); + if nsplits<0 then + error(msprintf(" nsplits value must be positive")) + end + + elseif strcmpi(varargin(i),'featureType')==0 then + i=i+1; + featureType=varargin(i); + if strcmpi(featureType,'haar') & strcmpi(featureType,'lbp') & strcmpi(featureType,'hog') + error(msprintf(" wrong input argument #%d,featureType not matched",i)); + end + + elseif strcmpi(varargin(i),'minHitRate')==0 then + i=i+1; + minHitRate=varargin(i); + if minHitRate<0 | minHitRate>1 then + error(msprintf(" minHitRate value must lie in between 0 and 1")) + end + + elseif strcmpi(varargin(i),'maxFalseAlarmRate')==0 then + i=i+1; + maxFalseAlarmRate=varargin(i); + if maxFalseAlarmRate<0 | minFalseRate>1 then + error(msprintf(" maxFalseAlarmRate value must lie in between 0 and 1")) + end + + elseif strcmpi(varargin(i),'w')==0 then + i=i+1; + w=varargin(i); + if h<0 then + error(msprintf(" w value must be positive")) + end + + elseif strcmpi(varargin(i),'h')==0 then + i=i+1; + h=varargin(i); + if h<0 then + error(msprintf(" h value must be positive")) + end + else + error(msprintf(_(" Wrong value for input argument #%d",i))); + end + end + + [nRows noOfPositiveInstances]=size(positiveInstances); + fields=fieldnames(positiveInstances); + fd = mopen('positive.txt','wt'); + for i=1:noOfPositiveInstances + mfprintf(fd,'%s 1',getfield(fields(1),positiveInstances(i))); + boxVals=getfield(fields(2),positiveInstances(i)); + for j=1:4 + mfprintf(fd,' %d',boxVals(j)); + end + mfprintf(fd,'\n'); + end + mclose(fd); + + disp("Creating positive samples:"); + cmd=sprintf("opencv_createsamples -info positive.txt -num%d -vec positive.vec -w %d -h %d",numPos,w,h); + unix_w(cmd); + if isdir(negativeImages) + if getos()=="Linux" + temp=strcat(["ls ",negativeImages]) + elseif getos()=="Windows" + temp=strcat(["dir ",negativeImages]) + end + s=unix_g(temp); + [noOfFilesInFolder noOfCols]=size(s); + fd = mopen('negative.txt','wt'); + for i=1:noOfFilesInFolder + [path,fname,extension]=fileparts(s(i)) + if ~strcmp(extension,".jpg") | ~strcmp(extension,".jpeg") | ~strcmp(extension,".png") | ~strcmp(extension,".bmp") | ~strcmp(extension,".pgm") | ~strcmp(extension,".JPG") | ~strcmp(extension,".JPEG") | ~strcmp(extension,".PNG") | ~strcmp(extension,".BMP") | ~strcmp(extension,".PGM") + mfprintf(fd,'%s/%s\n',negativeImages,s(i)); + end + end + end + disp("Training Cascade:"); + cmd=sprintf("opencv_traincascade -data %s -vec positive.vec -bg negative.txt -numPos %d -numNeg %d -numStages %d -nsplits %d -featureType %s -minHitRate %d -maxFalseAlarmRate %d -w %d -h %d",outputFolder,numPos,numNeg,numStages,nsplits,featureType,minHitRate,maxFalseAlarmRate,w,h); + unix_w(cmd); +endfunction; |