File size: 4,824 Bytes
8be366a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | #include "AttributeDetector.h"
AttributeDetector::AttributeDetector(std::string attribute, float weight, Parts::Location attLoc) {
_attribute = attribute;
_weight = weight;
_attributeLoc = attLoc;
_svm = new OcvSvm();
_threshold = 0.0;
_hogExtractor = new HogFeatureExtractor();
_siftExtractor = new SiftFeatureExtractor();
_hogExtractor->setQuantized(false);
_siftExtractor->setQuantized(true);
_useHOG = true;
_useSIFT = false;
}
AttributeDetector::~AttributeDetector() {
delete _svm;
delete _hogExtractor;
delete _siftExtractor;
}
void AttributeDetector::load() {
std::string directory = Util::env("SVM_MODELS");
_svm->load(directory + "/" + _attribute + ".svm");
std::string file = directory + "/" + _attribute + ".vcb";
ifstream fh(file.c_str());
if(!fh.good()) {
printf("WARNING: Attribute detector file %s doesn't exist.\n", file.c_str());
throw -1;
}
YAML::Parser parser(fh);
YAML::Node node;
parser.GetNextDocument(node);
assert(node.size() > 0);
int i = 0;
if(_useSIFT) {
node[i++] >> *_siftExtractor;
}
if(_useHOG) {
node[i++] >> *_hogExtractor;
}
fh.close();
}
void AttributeDetector::save() {
std::string directory = Util::env("SVM_MODELS");
_svm->save(directory + "/" + _attribute + ".svm");
std::string file = directory + "/" + _attribute + ".vcb";
ofstream fh(file.c_str());
YAML::Emitter emitter;
emitter << YAML::BeginSeq;
if(_useSIFT)
emitter << *_siftExtractor;
if(_useHOG)
emitter << *_hogExtractor;
emitter << YAML::EndSeq;
fh << emitter.c_str();
fh.close();
}
void AttributeDetector::train(const std::vector<std::string>& imageFiles, const std::vector<int>& labels) {
std::vector<cv::Rect> boxes;
for(int i = 0 ; i < imageFiles.size(); i++)
boxes.push_back(cv::Rect(0,0,1000000,1000000));
train(imageFiles, boxes, labels);
}
void AttributeDetector::train(const std::vector<std::string>& imageFiles, const std::vector<cv::Rect>& boxes, const std::vector<int>& labels) {
int maxFile = 1000;
if(maxFile >= imageFiles.size()) maxFile = imageFiles.size() - 1;
if(_useHOG) {
_hogExtractor->setQuantizePoint(maxFile);
_hogExtractor->setLabels(labels);
}
if(_useSIFT) {
_siftExtractor->setQuantizePoint(maxFile);
_siftExtractor->setLabels(labels);
}
for(int i = 0; i < imageFiles.size(); i++) {
std::string file = imageFiles[i];
// Use this function to avoid memory leaks
IplImage* imageP = cvLoadImage(file.c_str());
cv::Mat image = imageP;
cv::Rect box = boxes[i];
box = Util::correctBoundingBox(box, image);
cv::Mat roi = image(box);
cv::Mat gray(roi.size(), CV_8U);
if(roi.channels() != 1)
cv::cvtColor(roi, gray, CV_RGB2GRAY, 1);
else
gray = roi.clone();
if(_useSIFT) _siftExtractor->processImage(gray);
if(_useHOG) _hogExtractor->processImage(gray);
cvReleaseImage(&imageP);
}
FeatureSet fset;
if(_useSIFT) {
fset.push_back(_siftExtractor->getFeatures());
_siftExtractor->clearFeatures();
}
if(_useHOG) {
fset.push_back(_hogExtractor->getFeatures());
_hogExtractor->clearFeatures();
}
std::vector<PidMat> combined = FeatureExtractor::combine(fset);
assert(combined.size() == labels.size());
_svm->train(combined, labels);
}
void AttributeDetector::normalize(std::vector<PidMat>& features) {
BOOST_FOREACH(PidMat& feature, features) {
normalize(feature);
}
}
void AttributeDetector::normalize(PidMat& feature) {
float max = 0;
for(int i = 0; i < feature.rows; i++) {
for(int j = 0; j < feature.cols; j++) {
float temp = feature(i,j);
if(max < temp) max = temp;
}
}
for(int i = 0; i < feature.rows; i++)
for(int j = 0; j < feature.cols; j++)
feature(i,j) /= max;
}
bool AttributeDetector::hasAttribute(cv::Mat& image, cv::Rect box) {
float prediction;
return hasAttribute(image, box, prediction);
}
bool AttributeDetector::hasAttribute(cv::Mat& image, cv::Rect box, float& prediction) {
cv::Mat roi = image(box);
if(roi.channels() != 1)
cv::cvtColor(roi, roi, CV_RGB2GRAY, 1);
PidMat feature;
if(_useSIFT) {
assert(!_siftExtractor->isQuantized() || _siftExtractor->vocabularyBuilt());
feature.push_back(_siftExtractor->extractFeature(roi));
}
if(_useHOG) {
assert(!_hogExtractor->isQuantized() || _hogExtractor->vocabularyBuilt());
feature.push_back(_hogExtractor->extractFeature(roi));
}
prediction = _svm->predict(feature);
if(prediction > _threshold)
return true;
else
return false;
}
float AttributeDetector::getWeight() {
return _weight;
}
Parts::Location AttributeDetector::getDetectLoc()
{
return _attributeLoc;
}
std::string AttributeDetector::getAttribute() {
return _attribute;
}
|