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fixed hundreds of warnings from MSVC 2010.

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This commit is contained in:
Vadim Pisarevsky
2012-03-16 21:21:04 +00:00
parent 20cceb8fdf
commit 4985c1b632
83 changed files with 317 additions and 306 deletions
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30
samples/cpp/bagofwords_classification.cpp
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@@ -977,7 +977,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
CV_Error(CV_StsError,err_msg.c_str());
}
/* convert iterator to index */
target_idx = std::distance(output_headers.begin(),target_idx_it);
target_idx = (int)std::distance(output_headers.begin(),target_idx_it);
}
/* prepare variables related to calculating recall if using the recall threshold */
@@ -989,7 +989,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
/* in order to calculate the total number of relevant images for normalization of recall
it's necessary to extract the ground truth for the images under consideration */
getClassifierGroundTruth(obj_class, images, ground_truth);
total_relevant = std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<char>(),(char)1));
total_relevant = (int)std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<char>(),(char)1));
}
/* iterate through images */
@@ -1040,7 +1040,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
CV_Error(CV_StsError,err_msg.c_str());
}
/* convert iterator to index */
int class_idx = std::distance(output_headers.begin(),class_idx_it);
int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
//add to confusion matrix row in proportion
output_values[class_idx] += 1.f/static_cast<float>(img_objects.size());
}
@@ -1174,7 +1174,7 @@ void VocData::calcDetectorConfMatRow(const string& obj_class, const ObdDatasetTy
if (ov > maxov)
{
maxov = ov;
max_gt_obj_idx = gt_obj_idx;
max_gt_obj_idx = (int)gt_obj_idx;
}
}
}
@@ -1192,7 +1192,7 @@ void VocData::calcDetectorConfMatRow(const string& obj_class, const ObdDatasetTy
CV_Error(CV_StsError,err_msg.c_str());
}
/* convert iterator to index */
int class_idx = std::distance(output_headers.begin(),class_idx_it);
int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
//add to confusion matrix row in proportion
output_values[class_idx] += 1.0;
} else {
@@ -1540,7 +1540,7 @@ void VocData::readDetectorResultsFile(const string& input_file, vector<string>&
bounding_boxes.push_back(bounding_box_vect);
} else {
/* if the image index has been seen before, add the current object below it in the 2D arrays */
int image_idx = std::distance(image_codes.begin(),image_codes_it);
int image_idx = (int)std::distance(image_codes.begin(),image_codes_it);
scores[image_idx].push_back(score);
bounding_boxes[image_idx].push_back(bounding_box);
}
@@ -1985,7 +1985,7 @@ struct VocabTrainParams
{
VocabTrainParams() : trainObjClass("chair"), vocabSize(1000), memoryUse(200), descProportion(0.3f) {}
VocabTrainParams( const string _trainObjClass, size_t _vocabSize, size_t _memoryUse, float _descProportion ) :
trainObjClass(_trainObjClass), vocabSize(_vocabSize), memoryUse(_memoryUse), descProportion(_descProportion) {}
trainObjClass(_trainObjClass), vocabSize((int)_vocabSize), memoryUse((int)_memoryUse), descProportion(_descProportion) {}
void read( const FileNode& fn )
{
fn["trainObjClass"] >> trainObjClass;
@@ -2154,7 +2154,7 @@ Mat trainVocabulary( const string& filename, VocData& vocData, const VocabTrainP
// Randomly pick an image from the dataset which hasn't yet been seen
// and compute the descriptors from that image.
int randImgIdx = rng( images.size() );
int randImgIdx = rng( (unsigned)images.size() );
Mat colorImage = imread( images[randImgIdx].path );
vector<KeyPoint> imageKeypoints;
fdetector->detect( colorImage, imageKeypoints );
@@ -2296,12 +2296,12 @@ void removeBowImageDescriptorsByCount( vector<ObdImage>& images, vector<Mat> bow
const SVMTrainParamsExt& svmParamsExt, int descsToDelete )
{
RNG& rng = theRNG();
int pos_ex = std::count( objectPresent.begin(), objectPresent.end(), (char)1 );
int neg_ex = std::count( objectPresent.begin(), objectPresent.end(), (char)0 );
int pos_ex = (int)std::count( objectPresent.begin(), objectPresent.end(), (char)1 );
int neg_ex = (int)std::count( objectPresent.begin(), objectPresent.end(), (char)0 );
while( descsToDelete != 0 )
{
int randIdx = rng(images.size());
int randIdx = rng((unsigned)images.size());
// Prefer positive training examples according to svmParamsExt.targetRatio if required
if( objectPresent[randIdx] )
@@ -2415,14 +2415,14 @@ void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsExt, cons
}
// Prepare the input matrices for SVM training.
Mat trainData( images.size(), bowExtractor->getVocabulary().rows, CV_32FC1 );
Mat responses( images.size(), 1, CV_32SC1 );
Mat trainData( (int)images.size(), bowExtractor->getVocabulary().rows, CV_32FC1 );
Mat responses( (int)images.size(), 1, CV_32SC1 );
// Transfer bag of words vectors and responses across to the training data matrices
for( size_t imageIdx = 0; imageIdx < images.size(); imageIdx++ )
{
// Transfer image descriptor (bag of words vector) to training data matrix
Mat submat = trainData.row(imageIdx);
Mat submat = trainData.row((int)imageIdx);
if( bowImageDescriptors[imageIdx].cols != bowExtractor->descriptorSize() )
{
cout << "Error: computed bow image descriptor size " << bowImageDescriptors[imageIdx].cols
@@ -2432,7 +2432,7 @@ void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsExt, cons
bowImageDescriptors[imageIdx].copyTo( submat );
// Set response value
responses.at<int>(imageIdx) = objectPresent[imageIdx] ? 1 : -1;
responses.at<int>((int)imageIdx) = objectPresent[imageIdx] ? 1 : -1;
}
cout << "TRAINING SVM FOR CLASS ..." << objClassName << "..." << endl;