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I want do load a tiff image (GEOTIFF with pixels with float values) like graph in boost C++ (i'm a newbie in C++). My goal is use the bidirectional Dijkstra from source A to target B to get more performance.
Boost:GIL load tiif images:
std::string filename( "raster_clip.tif" );
rgb8_image_t img;
read_image( filename, img, tiff_tag() );
But how convert to Boost graph? I am reading the documentation and looking for examples but I have not yet been able to implement it.
Similar questions and examples that i found:
Shortest path graph algorithm help Boost;
http://www.geeksforgeeks.org/shortest-path-for-directed-acyclic-graphs/
I am currently using the scikit-image library and use skimage.graph.route_through_array function to load graph with array in python. I use GDAL to get an array by load image as suggested by #ustroetz in this example Here:
raster = gdal.Open("raster.tiff")
band = raster.GetRasterBand(1)
array = band.ReadAsArray()
Example of TIFF (was converted to PNG after upload) is:

Ok, so read the PNG:
I've cropped the whitespace border since it wasn't consistent anyways
Reading And Sampling The Image
using Img = boost::gil::rgb8_image_t; // gray8_image_t;
using Px = Img::value_type;
Img img;
//boost::gil::png_read_image("graph.png", img);
boost::gil::png_read_and_convert_image("graph.png", img);
auto vw = view(img);
Next up, make sure we know the dimensions and how to address the center pixels for each cell:
double constexpr cell_w = 30.409;
double constexpr cell_h = 30.375;
auto pixel_sample = [=](boost::array<size_t, 2> xy) -> auto& {
return vw((xy[0]+.5)*cell_w, (xy[1]+.5)*cell_h);
};
auto const w= static_cast<size_t>(img.dimensions()[0] / cell_w);
auto const h= static_cast<size_t>(img.dimensions()[1] / cell_h);
Constructing The Graph
Now let's make the graph. For this task a grid-graph seems in order. It should be w×h and not wrap around at the edges (if it should, change false to true):
using Graph = boost::grid_graph<2>;
Graph graph({{w,h}}, false);
We want to attach weights at each edge. We can either use an old-fashioned external property map that's sized up-front:
std::vector<double> weight_v(num_edges(graph));
auto weights = boost::make_safe_iterator_property_map(weight_v.begin(), weight_v.size(), get(boost::edge_index, graph));
Alternatively, we can use a dynamically allocating and growing property-map:
auto weights = boost::make_vector_property_map<float>(get(boost::edge_index, graph));
As a bonus, here's the equivalent approach using an associative property-map:
std::map<Graph::edge_descriptor, double> weight_m;
auto weights = boost::make_assoc_property_map(weight_m);
Each of these are drop-in compatible and the choice is yours.
Filling The Graph
We simply iterate all edges, setting the cost from the colour difference:
BGL_FORALL_EDGES(e, graph, Graph) {
auto& from = pixel_sample(e.first);
auto& to = pixel_sample(e.second);
// compare RED channels only
auto cost = std::abs(from[0] - to[0]);
put(weights, e, cost);
}
Note Consider normalizing weight to e.g. [0.0, 1.0) using the actual bit-depth of the source image
Let's create a verification TIF so we can actually see where the samples were taken in the image:
{
BGL_FORALL_VERTICES(v, graph, Graph) {
pixel_sample(v) = Px(255, 0, 123); // mark the center pixels so we can verify the sampling
}
boost::gil::tiff_write_view("/tmp/verification.tif", const_view(img));
}
The verification.tif ends up like (note the center pixel for each cell):
Bonus: Visualize The Grid Graph
Let's write it to a Graphviz file:
{
auto calc_color = [&](size_t v) {
std::ostringstream oss;
oss << std::hex << std::noshowbase << std::setfill('0');
auto const& from = pixel_sample(vertex(v, graph));
oss << "#" << std::setw(2) << static_cast<int>(from[0])
<< std::setw(2) << static_cast<int>(from[1])
<< std::setw(2) << static_cast<int>(from[2]);
return oss.str();
};
write_dot_file(graph, weights, calc_color);
}
This calculates the color from the same sample pixel and uses some Graphviz-specific magic to write to a file:
template <typename Graph, typename Weights, typename ColorFunction>
void write_dot_file(Graph const& graph, Weights const& weights, ColorFunction calc_color) {
boost::dynamic_properties dp;
dp.property("node_id", get(boost::vertex_index, graph));
dp.property("fillcolor", boost::make_transform_value_property_map(calc_color, get(boost::vertex_index, graph)));
dp.property("style", boost::make_static_property_map<typename Graph::vertex_descriptor>(std::string("filled")));
std::ofstream ofs("grid.dot");
auto vpw = boost::dynamic_vertex_properties_writer { dp, "node_id" };
auto epw = boost::make_label_writer(weights);
auto gpw = boost::make_graph_attributes_writer(
std::map<std::string, std::string> { },
std::map<std::string, std::string> { {"shape", "rect"} },
std::map<std::string, std::string> { }
);
boost::write_graphviz(ofs, graph, vpw, epw, gpw);
}
Which results in a grid.dot file like this.
Next, let's layout using neato:
neato -T png grid.dot -o grid.png
And the result is:
FULL CODE LISTING
#include <boost/gil/extension/io/png_dynamic_io.hpp>
#include <boost/gil/extension/io/tiff_dynamic_io.hpp>
#include <boost/graph/grid_graph.hpp>
#include <boost/graph/iteration_macros.hpp>
#include <boost/graph/graphviz.hpp>
#include <iostream>
template <typename Graph, typename Weights, typename ColorFunction>
void write_dot_file(Graph const& graph, Weights const& weights, ColorFunction);
int main() try {
using Img = boost::gil::rgb8_image_t; // gray8_image_t;
using Px = Img::value_type;
Img img;
//boost::gil::png_read_image("/home/sehe/graph.png", img);
boost::gil::png_read_and_convert_image("/home/sehe/graph.png", img);
auto vw = view(img);
double constexpr cell_w = 30.409;
double constexpr cell_h = 30.375;
auto pixel_sample = [=](boost::array<size_t, 2> xy) -> auto& {
return vw((xy[0]+.5)*cell_w, (xy[1]+.5)*cell_h);
};
auto const w= static_cast<size_t>(img.dimensions()[0] / cell_w);
auto const h= static_cast<size_t>(img.dimensions()[1] / cell_h);
using Graph = boost::grid_graph<2>;
Graph graph({{w,h}}, false);
#if 0 // dynamic weight map
auto weights = boost::make_vector_property_map<float>(get(boost::edge_index, graph));
std::cout << "Edges: " << (weights.storage_end() - weights.storage_begin()) << "\n";
#elif 1 // fixed vector weight map
std::vector<double> weight_v(num_edges(graph));
auto weights = boost::make_safe_iterator_property_map(weight_v.begin(), weight_v.size(), get(boost::edge_index, graph));
#else // associative weight map
std::map<Graph::edge_descriptor, double> weight_m;
auto weights = boost::make_assoc_property_map(weight_m);
#endif
auto debug_vertex = [] (auto& v) -> auto& { return std::cout << "{" << v[0] << "," << v[1] << "}"; };
auto debug_edge = [&](auto& e) -> auto& { debug_vertex(e.first) << " -> "; return debug_vertex(e.second); };
BGL_FORALL_EDGES(e, graph, Graph) {
//debug_edge(e) << "\n";
auto& from = pixel_sample(e.first);
auto& to = pixel_sample(e.second);
// compare RED channels only
auto cost = std::abs(from[0] - to[0]);
put(weights, e, cost);
}
{
auto calc_color = [&](size_t v) {
std::ostringstream oss;
oss << std::hex << std::noshowbase << std::setfill('0');
auto const& from = pixel_sample(vertex(v, graph));
oss << "#" << std::setw(2) << static_cast<int>(from[0])
<< std::setw(2) << static_cast<int>(from[1])
<< std::setw(2) << static_cast<int>(from[2]);
return oss.str();
};
write_dot_file(graph, weights, calc_color);
}
{
BGL_FORALL_VERTICES(v, graph, Graph) {
pixel_sample(v) = Px(255, 0, 123); // mark the center pixels so we can verify the sampling
}
boost::gil::tiff_write_view("/tmp/verification.tif", const_view(img));
}
} catch(std::exception const& e) {
std::cout << "Exception occured: " << e.what() << "\n";
}
template <typename Graph, typename Weights, typename ColorFunction>
void write_dot_file(Graph const& graph, Weights const& weights, ColorFunction calc_color) {
boost::dynamic_properties dp;
dp.property("node_id", get(boost::vertex_index, graph));
dp.property("fillcolor", boost::make_transform_value_property_map(calc_color, get(boost::vertex_index, graph)));
dp.property("style", boost::make_static_property_map<typename Graph::vertex_descriptor>(std::string("filled")));
std::ofstream ofs("grid.dot");
auto vpw = boost::dynamic_vertex_properties_writer { dp, "node_id" };
auto epw = boost::make_label_writer(weights);
auto gpw = boost::make_graph_attributes_writer(
std::map<std::string, std::string> { },
std::map<std::string, std::string> { {"shape", "rect"} },
std::map<std::string, std::string> { }
);
boost::write_graphviz(ofs, graph, vpw, epw, gpw);
}