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Extended labels with indices and colors

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This commit is contained in:
Simon Giraudot
2020-04-06 10:25:21 +02:00
parent a51ce12aa5
commit 39ca0f3bf7
15 changed files with 403 additions and 343 deletions
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Classification/examples/Classification/example_classification.cpp
+10 -4
View File
@@ -83,7 +83,7 @@ int main (int argc, char** argv)
std::cerr << "Computing features" << std::endl;
Feature_set features;
features.begin_parallel_additions();
features.begin_parallel_additions(); // No effect in sequential mode
Feature_handle distance_to_plane = features.add<Distance_to_plane> (pts, Pmap(), eigen);
Feature_handle dispersion = features.add<Dispersion> (pts, Pmap(), grid,
@@ -91,7 +91,7 @@ int main (int argc, char** argv)
Feature_handle elevation = features.add<Elevation> (pts, Pmap(), grid,
radius_dtm);
features.end_parallel_additions();
features.end_parallel_additions(); // No effect in sequential mode
//! [Features]
///////////////////////////////////////////////////////////////////
@@ -100,9 +100,15 @@ int main (int argc, char** argv)
//! [Labels]
Label_set labels;
// Init name only
Label_handle ground = labels.add ("ground");
Label_handle vegetation = labels.add ("vegetation");
Label_handle roof = labels.add ("roof");
// Init name and color
Label_handle vegetation = labels.add ("vegetation", CGAL::Color(0,255,0));
// Init name, Color and standard index (here, ASPRS building index)
Label_handle roof = labels.add ("roof", CGAL::Color (255, 0, 0), 6);
//! [Labels]
///////////////////////////////////////////////////////////////////
Classification/examples/Classification/example_ethz_random_forest.cpp
+14 -23
View File
@@ -37,7 +37,7 @@ typedef Classification::Point_set_feature_generator<Kernel, Point_set, Pmap>
int main (int argc, char** argv)
{
std::string filename = "data/b9_training.ply";
if (argc > 1)
filename = argv[1];
@@ -46,7 +46,7 @@ int main (int argc, char** argv)
std::cerr << "Reading input" << std::endl;
in >> pts;
Imap label_map;
bool lm_found = false;
std::tie (label_map, lm_found) = pts.property_map<int> ("label");
@@ -57,17 +57,17 @@ int main (int argc, char** argv)
}
Feature_set features;
std::cerr << "Generating features" << std::endl;
CGAL::Real_timer t;
t.start();
Feature_generator generator (pts, pts.point_map(),
5); // using 5 scales
features.begin_parallel_additions();
generator.generate_point_based_features (features);
features.end_parallel_additions();
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
@@ -78,10 +78,10 @@ int main (int argc, char** argv)
Label_handle roof = labels.add ("roof");
std::vector<int> label_indices(pts.size(), -1);
std::cerr << "Using ETHZ Random Forest Classifier" << std::endl;
Classification::ETHZ::Random_forest_classifier classifier (labels, features);
std::cerr << "Training" << std::endl;
t.reset();
t.start();
@@ -96,7 +96,7 @@ int main (int argc, char** argv)
generator.neighborhood().k_neighbor_query(12),
0.2f, 1, label_indices);
t.stop();
std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl;
std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl;
@@ -123,21 +123,12 @@ int main (int argc, char** argv)
for (std::size_t i = 0; i < label_indices.size(); ++ i)
{
label_map[i] = label_indices[i]; // update label map with computed classification
Label_handle label = labels[label_indices[i]];
if (label == ground)
{
red[i] = 245; green[i] = 180; blue[i] = 0;
}
else if (label == vegetation)
{
red[i] = 0; green[i] = 255; blue[i] = 27;
}
else if (label == roof)
{
red[i] = 255; green[i] = 0; blue[i] = 170;
}
Label_handle label = labels[label_indices[i]];
const CGAL::Color& color = label->color();
red[i] = color.red();
green[i] = color.green();
blue[i] = color.blue();
}
// Save configuration for later use
@@ -150,6 +141,6 @@ int main (int argc, char** argv)
f << pts;
std::cerr << "All done" << std::endl;
return EXIT_SUCCESS;
}
Classification/examples/Classification/example_opencv_random_forest.cpp
+14 -23
View File
@@ -37,7 +37,7 @@ typedef Classification::Point_set_feature_generator<Kernel, Point_set, Pmap>
int main (int argc, char** argv)
{
std::string filename = "data/b9_training.ply";
if (argc > 1)
filename = argv[1];
@@ -46,7 +46,7 @@ int main (int argc, char** argv)
std::cerr << "Reading input" << std::endl;
in >> pts;
Imap label_map;
bool lm_found = false;
std::tie (label_map, lm_found) = pts.property_map<int> ("label");
@@ -57,7 +57,7 @@ int main (int argc, char** argv)
}
Feature_set features;
std::cerr << "Generating features" << std::endl;
CGAL::Real_timer t;
t.start();
@@ -67,7 +67,7 @@ int main (int argc, char** argv)
features.begin_parallel_additions();
generator.generate_point_based_features (features);
features.end_parallel_additions();
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
@@ -78,10 +78,10 @@ int main (int argc, char** argv)
Label_handle roof = labels.add ("roof");
std::vector<int> label_indices(pts.size(), -1);
std::cerr << "Using OpenCV Random Forest Classifier" << std::endl;
Classification::OpenCV::Random_forest_classifier classifier (labels, features);
std::cerr << "Training" << std::endl;
t.reset();
t.start();
@@ -96,12 +96,12 @@ int main (int argc, char** argv)
generator.neighborhood().k_neighbor_query(12),
0.2f, 1, label_indices);
t.stop();
std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl;
std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl;
Classification::Evaluation evaluation (labels, pts.range(label_map), label_indices);
for (Label_handle l : labels)
{
std::cerr << " * " << l->name() << ": "
@@ -123,21 +123,12 @@ int main (int argc, char** argv)
for (std::size_t i = 0; i < label_indices.size(); ++ i)
{
label_map[i] = label_indices[i]; // update label map with computed classification
Label_handle label = labels[label_indices[i]];
if (label == ground)
{
red[i] = 245; green[i] = 180; blue[i] = 0;
}
else if (label == vegetation)
{
red[i] = 0; green[i] = 255; blue[i] = 27;
}
else if (label == roof)
{
red[i] = 255; green[i] = 0; blue[i] = 170;
}
Label_handle label = labels[label_indices[i]];
const CGAL::Color& color = label->color();
red[i] = color.red();
green[i] = color.green();
blue[i] = color.blue();
}
// Write result
@@ -146,6 +137,6 @@ int main (int argc, char** argv)
f << pts;
std::cerr << "All done" << std::endl;
return EXIT_SUCCESS;
}
Classification/examples/Classification/example_tensorflow_neural_network.cpp
+15 -24
View File
@@ -37,7 +37,7 @@ typedef Classification::Point_set_feature_generator<Kernel, Point_set, Pmap>
int main (int argc, char** argv)
{
std::string filename = "data/b9_training.ply";
if (argc > 1)
filename = argv[1];
@@ -46,7 +46,7 @@ int main (int argc, char** argv)
std::cerr << "Reading input" << std::endl;
in >> pts;
Imap label_map;
bool lm_found = false;
std::tie (label_map, lm_found) = pts.property_map<int> ("label");
@@ -57,17 +57,17 @@ int main (int argc, char** argv)
}
Feature_set features;
std::cerr << "Generating features" << std::endl;
CGAL::Real_timer t;
t.start();
Feature_generator generator (pts, pts.point_map(),
5); // using 5 scales
features.begin_parallel_additions();
generator.generate_point_based_features (features);
features.end_parallel_additions();
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
@@ -78,10 +78,10 @@ int main (int argc, char** argv)
Label_handle roof = labels.add ("roof");
std::vector<int> label_indices(pts.size(), -1);
std::cerr << "Using TensorFlow neural network Classifier" << std::endl;
Classification::TensorFlow::Neural_network_classifier<> classifier (labels, features);
std::cerr << "Training" << std::endl;
t.reset();
t.start();
@@ -98,12 +98,12 @@ int main (int argc, char** argv)
generator.neighborhood().k_neighbor_query(12),
0.2f, 1, label_indices);
t.stop();
std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl;
std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl;
Classification::Evaluation evaluation (labels, pts.range(ground_truth), label_indices);
for (Label_handle l : labels)
{
std::cerr << " * " << l->name() << ": "
@@ -125,21 +125,12 @@ int main (int argc, char** argv)
for (std::size_t i = 0; i < label_indices.size(); ++ i)
{
label_map[i] = label_indices[i]; // update label map with computed classification
Label_handle label = labels[label_indices[i]];
if (label == ground)
{
red[i] = 245; green[i] = 180; blue[i] = 0;
}
else if (label == vegetation)
{
red[i] = 0; green[i] = 255; blue[i] = 27;
}
else if (label == roof)
{
red[i] = 255; green[i] = 0; blue[i] = 170;
}
Label_handle label = labels[label_indices[i]];
const CGAL::Color& color = label->color();
red[i] = color.red();
green[i] = color.green();
blue[i] = color.blue();
}
// Write result
@@ -148,6 +139,6 @@ int main (int argc, char** argv)
f << pts;
std::cerr << "All done" << std::endl;
return EXIT_SUCCESS;
}