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documentation update
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@ereator
ereator committed Sep 14, 2016
1 parent fb8b964 commit 633c3cf
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3 changes: 2 additions & 1 deletion doc/Doxyfile
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Expand Up @@ -2046,7 +2046,8 @@ INCLUDE_FILE_PATTERNS =
# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.

PREDEFINED = DllExport \
USE_SHERWOOD
USE_SHERWOOD \
USE_OPENGL

# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
# tag can be used to specify a list of macro names that should be expanded. The
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320 changes: 160 additions & 160 deletions include/VIS.h
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Expand Up @@ -54,137 +54,137 @@ using namespace DirectGraphicalModels::vis;
Mat histogramImg;
typedef struct {
CGraph * pGraph;
CMarkerHistogram * pMarker;
int imgWidth;
CGraph * pGraph;
CMarkerHistogram * pMarker;
int imgWidth;
} USER_DATA;
int main(int argv, char *argc[])
{
const CvSize imgSize = cvSize(400, 400);
const int width = imgSize.width;
const int height = imgSize.height;
const unsigned int nStates = 6; // {road, traffic island, grass, agriculture, tree, car}
const unsigned int nFeatures = 3;
if (argv != 4) {
print_help();
return 0;
}
// Reading parameters and images
Mat img = imread(argc[1], 1); resize(img, img, imgSize, 0, 0, INTER_LANCZOS4); // image
Mat fv = imread(argc[2], 1); resize(fv, fv, imgSize, 0, 0, INTER_LANCZOS4); // feature vector
Mat gt = imread(argc[3], 0); resize(gt, gt, imgSize, 0, 0, INTER_NEAREST); // groundtruth
CTrainNode * nodeTrainer = new CTrainNodeNaiveBayes(nStates, nFeatures);
CTrainEdge * edgeTrainer = new CTrainEdgePottsCS(nStates, nFeatures);
float params[] = {400, 0.001f};
size_t params_len = 2;
CGraph * graph = new CGraph(nStates);
CInfer * decoder = new CInferLBP(graph);
// Define custom colors in RGB format for our classes (for visualization)
vec_nColor_t palette;
palette.push_back(std::make_pair(CV_RGB(64, 64, 64), "road"));
palette.push_back(std::make_pair(CV_RGB(0, 255, 255), "tr. island"));
palette.push_back(std::make_pair(CV_RGB(0, 255, 0), "grass"));
palette.push_back(std::make_pair(CV_RGB(200, 135, 70), "agricult."));
palette.push_back(std::make_pair(CV_RGB(64, 128, 0), "tree"));
palette.push_back(std::make_pair(CV_RGB(255, 0, 0), "car"));
// Define feature names for visualization
vec_string_t featureNames = {"NDVI", "Var. Int.", "Saturation"};
CMarkerHistogram * marker = new CMarkerHistogram(nodeTrainer, palette, featureNames);
CCMat * confMat = new CCMat(nStates);
// ==================== STAGE 1: Building the graph ====================
printf("Building the Graph... ");
int64 ticks = getTickCount();
for (int y = 0; y < height; y++)
for (int x = 0; x < width; x++) {
size_t idx = graph->addNode();
if (x > 0) graph->addArc(idx, idx - 1);
if (y > 0) graph->addArc(idx, idx - width);
} // x
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ========================= STAGE 2: Training =========================
printf("Training... ");
ticks = getTickCount();
nodeTrainer->addFeatureVec(fv, gt); // Only Node Training
nodeTrainer->train(); // Contrast-Sensitive Edge Model requires no training
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ==================== STAGE 3: Filling the Graph =====================
printf("Filling the Graph... ");
ticks = getTickCount();
Mat featureVector1(nFeatures, 1, CV_8UC1);
Mat featureVector2(nFeatures, 1, CV_8UC1);
Mat nodePot, edgePot;
for (int y = 0, idx = 0; y < height; y++) {
byte *pFv1 = fv.ptr<byte>(y);
byte *pFv2 = (y > 0) ? fv.ptr<byte>(y - 1) : NULL;
for (int x = 0; x < width; x++, idx++) {
for (word f = 0; f < nFeatures; f++) featureVector1.at<byte>(f, 0) = pFv1[nFeatures * x + f]; // featureVector1 = fv[x][y]
nodePot = nodeTrainer->getNodePotentials(featureVector1); // node potential
graph->setNode(idx, nodePot);
if (x > 0) {
for (word f = 0; f < nFeatures; f++) featureVector2.at<byte>(f, 0) = pFv1[nFeatures * (x - 1) + f]; // featureVector2 = fv[x-1][y]
edgePot = edgeTrainer->getEdgePotentials(featureVector1, featureVector2, params, params_len); // edge potential
graph->setArc(idx, idx - 1, edgePot);
} // if x
if (y > 0) {
for (word f = 0; f < nFeatures; f++) featureVector2.at<byte>(f, 0) = pFv2[nFeatures * x + f]; // featureVector2 = fv[x][y-1]
edgePot = edgeTrainer->getEdgePotentials(featureVector1, featureVector2, params, params_len); // edge potential
graph->setArc(idx, idx - width, edgePot);
} // if y
} // x
} // y
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ========================= STAGE 4: Decoding =========================
printf("Decoding... ");
ticks = getTickCount();
std::vector<byte> optimalDecoding = decoder->decode(10);
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ====================== Evaluation =======================
Mat solution(imgSize, CV_8UC1, optimalDecoding.data());
confMat->estimate(gt, solution); // compare solution with the groundtruth
char str[255];
sprintf(str, "Accuracy = %.2f%%", confMat->getAccuracy());
printf("%s\n", str);
// ====================== Visualization =======================
marker->markClasses(img, solution);
rectangle(img, Point(width - 160, height- 18), Point(width, height), CV_RGB(0,0,0), -1);
putText(img, str, Point(width - 155, height - 5), FONT_HERSHEY_SIMPLEX, 0.45, CV_RGB(225, 240, 255), 1, CV_AA);
imshow("Solution", img);
// Feature distribution histograms
histogramImg = marker->drawHistogram();
imshow("Histogram", histogramImg);
// Confusion matrix
Mat cMat = confMat->getConfusionMatrix();
Mat cMatImg = marker->drawConfusionMatrix(cMat, MARK_BW);
imshow("Confusion Matrix", cMatImg);
// Setting up handlers
USER_DATA userData;
userData.pGraph = graph;
userData.pMarker = marker;
userData.imgWidth = width;
cvSetMouseCallback("Solution", solutiontWindowMouseHandler, &userData);
cvSetMouseCallback("Histogram", histogramWindowMouseHandler, &userData);
cvWaitKey();
return 0;
const CvSize imgSize = cvSize(400, 400);
const int width = imgSize.width;
const int height = imgSize.height;
const unsigned int nStates = 6; // { road, traffic island, grass, agriculture, tree, car }
const unsigned int nFeatures = 3;
if (argv != 4) {
print_help();
return 0;
}
// Reading parameters and images
Mat img = imread(argc[1], 1); resize(img, img, imgSize, 0, 0, INTER_LANCZOS4); // image
Mat fv = imread(argc[2], 1); resize(fv, fv, imgSize, 0, 0, INTER_LANCZOS4); // feature vector
Mat gt = imread(argc[3], 0); resize(gt, gt, imgSize, 0, 0, INTER_NEAREST); // groundtruth
CTrainNode * nodeTrainer = new CTrainNodeNaiveBayes(nStates, nFeatures);
CTrainEdge * edgeTrainer = new CTrainEdgePottsCS(nStates, nFeatures);
float params[] = { 400, 0.001f };
size_t params_len = 2;
CGraph * graph = new CGraph(nStates);
CInfer * decoder = new CInferLBP(graph);
// Define custom colors in RGB format for our classes (for visualization)
vec_nColor_t palette;
palette.push_back(std::make_pair(CV_RGB(64, 64, 64), "road"));
palette.push_back(std::make_pair(CV_RGB(0, 255, 255), "tr. island"));
palette.push_back(std::make_pair(CV_RGB(0, 255, 0), "grass"));
palette.push_back(std::make_pair(CV_RGB(200, 135, 70), "agricult."));
palette.push_back(std::make_pair(CV_RGB(64, 128, 0), "tree"));
palette.push_back(std::make_pair(CV_RGB(255, 0, 0), "car"));
// Define feature names for visualization
vec_string_t featureNames = {"NDVI", "Var. Int.", "Saturation"};
CMarkerHistogram * marker = new CMarkerHistogram(nodeTrainer, palette, featureNames);
CCMat * confMat = new CCMat(nStates);
// ==================== STAGE 1: Building the graph ====================
printf("Building the Graph... ");
int64 ticks = getTickCount();
for (int y = 0; y < height; y++)
for (int x = 0; x < width; x++) {
size_t idx = graph->addNode();
if (x > 0) graph->addArc(idx, idx - 1);
if (y > 0) graph->addArc(idx, idx - width);
} // x
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ========================= STAGE 2: Training =========================
printf("Training... ");
ticks = getTickCount();
nodeTrainer->addFeatureVec(fv, gt); // Only Node Training
nodeTrainer->train(); // Contrast-Sensitive Edge Model requires no training
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ==================== STAGE 3: Filling the Graph =====================
printf("Filling the Graph... ");
ticks = getTickCount();
Mat featureVector1(nFeatures, 1, CV_8UC1);
Mat featureVector2(nFeatures, 1, CV_8UC1);
Mat nodePot, edgePot;
for (int y = 0, idx = 0; y < height; y++) {
byte *pFv1 = fv.ptr<byte>(y);
byte *pFv2 = (y > 0) ? fv.ptr<byte>(y - 1) : NULL;
for (int x = 0; x < width; x++, idx++) {
for (word f = 0; f < nFeatures; f++) featureVector1.at<byte>(f, 0) = pFv1[nFeatures * x + f]; // featureVector1 = fv[x][y]
nodePot = nodeTrainer->getNodePotentials(featureVector1); // node potential
graph->setNode(idx, nodePot);
if (x > 0) {
for (word f = 0; f < nFeatures; f++) featureVector2.at<byte>(f, 0) = pFv1[nFeatures * (x - 1) + f]; // featureVector2 = fv[x-1][y]
edgePot = edgeTrainer->getEdgePotentials(featureVector1, featureVector2, params, params_len); // edge potential
graph->setArc(idx, idx - 1, edgePot);
} // if x
if (y > 0) {
for (word f = 0; f < nFeatures; f++) featureVector2.at<byte>(f, 0) = pFv2[nFeatures * x + f]; // featureVector2 = fv[x][y-1]
edgePot = edgeTrainer->getEdgePotentials(featureVector1, featureVector2, params, params_len); // edge potential
graph->setArc(idx, idx - width, edgePot);
} // if y
} // x
} // y
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ========================= STAGE 4: Decoding =========================
printf("Decoding... ");
ticks = getTickCount();
std::vector<byte> optimalDecoding = decoder->decode(10);
ticks = getTickCount() - ticks;
printf("Done! (%fms)\n", ticks * 1000 / getTickFrequency());
// ====================== Evaluation =======================
Mat solution(imgSize, CV_8UC1, optimalDecoding.data());
confMat->estimate(gt, solution); // compare solution with the groundtruth
char str[255];
sprintf(str, "Accuracy = %.2f%%", confMat->getAccuracy());
printf("%s\n", str);
// ====================== Visualization =======================
marker->markClasses(img, solution);
rectangle(img, Point(width - 160, height- 18), Point(width, height), CV_RGB(0,0,0), -1);
putText(img, str, Point(width - 155, height - 5), FONT_HERSHEY_SIMPLEX, 0.45, CV_RGB(225, 240, 255), 1, CV_AA);
imshow("Solution", img);
// Feature distribution histograms
histogramImg = marker->drawHistogram();
imshow("Histogram", histogramImg);
// Confusion matrix
Mat cMat = confMat->getConfusionMatrix();
Mat cMatImg = marker->drawConfusionMatrix(cMat, MARK_BW);
imshow("Confusion Matrix", cMatImg);
// Setting up handlers
USER_DATA userData;
userData.pGraph = graph;
userData.pMarker = marker;
userData.imgWidth = width;
cvSetMouseCallback("Solution", solutiontWindowMouseHandler, &userData);
cvSetMouseCallback("Histogram", histogramWindowMouseHandler, &userData);
cvWaitKey();
return 0;
}
@endcode
Expand All @@ -196,28 +196,28 @@ corresponding to one of these four edge potentials. The visualization of the nod
@code
void solutiontWindowMouseHandler(int Event, int x, int y, int flags, void *param)
{
USER_DATA * pUserData = static_cast<USER_DATA *>(param);
if (Event == CV_EVENT_LBUTTONDOWN) {
Mat pot, potImg;
size_t node_id = pUserData->imgWidth * y + x;
// Node potential
pUserData->pGraph->getNode(node_id, pot);
potImg = pUserData->pMarker->drawPotentials(pot, MARK_BW);
imshow("Node Potential", potImg);
// Edge potential
vec_size_t child_nodes;
pUserData->pGraph->getChildNodes(node_id, child_nodes);
if (child_nodes.size() > 0) {
pUserData->pGraph->getEdge(node_id, child_nodes.at(0), pot);
potImg = pUserData->pMarker->drawPotentials(pot, MARK_BW);
imshow("Edge Potential", potImg);
}
pot.release();
potImg.release();
}
USER_DATA * pUserData = static_cast<USER_DATA *>(param);
if (Event == CV_EVENT_LBUTTONDOWN) {
Mat pot, potImg;
size_t node_id = pUserData->imgWidth * y + x;
// Node potential
pUserData->pGraph->getNode(node_id, pot);
potImg = pUserData->pMarker->drawPotentials(pot, MARK_BW);
imshow("Node Potential", potImg);
// Edge potential
vec_size_t child_nodes;
pUserData->pGraph->getChildNodes(node_id, child_nodes);
if (child_nodes.size() > 0) {
pUserData->pGraph->getEdge(node_id, child_nodes.at(0), pot);
potImg = pUserData->pMarker->drawPotentials(pot, MARK_BW);
imshow("Edge Potential", potImg);
}
pot.release();
potImg.release();
}
}
@endcode
Expand All @@ -228,17 +228,17 @@ color box near to the class name. These feature distributions allow for analyzin
@code
void histogramWindowMouseHandler(int Event, int x, int y, int flags, void *param)
{
USER_DATA * pUserData = static_cast<USER_DATA *>(param);
if (Event == CV_EVENT_LBUTTONDOWN) {
CvScalar color;
color.val[0] = histogramImg.at<byte>(y, 3 * x + 0); // Blue
color.val[1] = histogramImg.at<byte>(y, 3 * x + 1); // Green
color.val[2] = histogramImg.at<byte>(y, 3 * x + 2); // Red
histogramImg.release();
histogramImg = pUserData->pMarker->drawHistogram(color);
imshow("Histogram", histogramImg);
}
USER_DATA * pUserData = static_cast<USER_DATA *>(param);
if (Event == CV_EVENT_LBUTTONDOWN) {
CvScalar color;
color.val[0] = histogramImg.at<byte>(y, 3 * x + 0); // Blue
color.val[1] = histogramImg.at<byte>(y, 3 * x + 1); // Green
color.val[2] = histogramImg.at<byte>(y, 3 * x + 2); // Red
histogramImg.release();
histogramImg = pUserData->pMarker->drawHistogram(color);
imshow("Histogram", histogramImg);
}
}
@endcode
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