merged

dlib/gui_widgets/widgets.cpp

@@ -1465,6 +1465,14 @@ namespace dlib
 
 // ----------------------------------------------------------------------------------------
 
+    unsigned long tabbed_display::
+    selected_tab (
+    ) const
+    {
+        auto_mutex M(m);
+        return selected_tab_;
+    }
+
     unsigned long tabbed_display::
     number_of_tabs (
     ) const

dlib/gui_widgets/widgets.h

@@ -1190,6 +1190,9 @@ namespace dlib
             unsigned long num
         );
 
+        unsigned long selected_tab (
+        ) const;
+
         unsigned long number_of_tabs (
         ) const;
 

dlib/image_transforms/interpolation.h

@@ -1856,12 +1856,14 @@ namespace dlib
 
     template <
         typename image_type1,
-        typename image_type2
+        typename image_type2,
+        typename interpolation_type
         >
     void extract_image_chips (
         const image_type1& img,
         const std::vector<chip_details>& chip_locations,
-        dlib::array<image_type2>& chips
+        dlib::array<image_type2>& chips,
+        const interpolation_type& interp
     )
     {
         // make sure requires clause is not broken
@@ -1957,9 +1959,9 @@ namespace dlib
 
                 // now extract the actual chip
                 if (level == -1)
-                    transform_image(sub_image(img,bounding_box),chips[i],interpolate_bilinear(),trns);
+                    transform_image(sub_image(img,bounding_box),chips[i],interp,trns);
                 else
-                    transform_image(levels[level],chips[i],interpolate_bilinear(),trns);
+                    transform_image(levels[level],chips[i],interp,trns);
             }
         }
     }
@@ -1970,10 +1972,27 @@ namespace dlib
         typename image_type1,
         typename image_type2
         >
+    void extract_image_chips(
+        const image_type1& img,
+        const std::vector<chip_details>& chip_locations,
+        dlib::array<image_type2>& chips
+    )
+    {
+        extract_image_chips(img, chip_locations, chips, interpolate_bilinear());
+    }
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename image_type1,
+        typename image_type2,
+        typename interpolation_type
+        >
     void extract_image_chip (
         const image_type1& img,
         const chip_details& location,
-        image_type2& chip
+        image_type2& chip,
+        const interpolation_type& interp
     )
     {
         // If the chip doesn't have any rotation or scaling then use the basic version of
@@ -1988,11 +2007,26 @@ namespace dlib
         {
             std::vector<chip_details> chip_locations(1,location);
             dlib::array<image_type2> chips;
-            extract_image_chips(img, chip_locations, chips);
+            extract_image_chips(img, chip_locations, chips, interp);
             swap(chips[0], chip);
         }
     }
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename image_type1,
+        typename image_type2
+        >
+    void extract_image_chip (
+        const image_type1& img,
+        const chip_details& location,
+        image_type2& chip
+    )
+    {
+        extract_image_chip(img, location, chip, interpolate_bilinear());
+    }
+
 // ----------------------------------------------------------------------------------------
 
     inline chip_details get_face_chip_details (

dlib/image_transforms/interpolation_abstract.h

@@ -1163,12 +1163,14 @@ namespace dlib
 
     template <
         typename image_type1,
-        typename image_type2
+        typename image_type2,
+        typename interpolation_type
         >
     void extract_image_chips (
         const image_type1& img,
         const std::vector<chip_details>& chip_locations,
-        dlib::array<image_type2>& chips
+        dlib::array<image_type2>& chips,
+        const interpolation_type& interp
     );
     /*!
         requires
@@ -1185,6 +1187,7 @@ namespace dlib
               rectangular sub-windows (i.e. chips) within an image and extracts those
               sub-windows, storing each into its own image.  It also scales and rotates the
               image chips according to the instructions inside each chip_details object.
+              It uses the interpolation method supplied as a parameter.
             - #chips == the extracted image chips
             - #chips.size() == chip_locations.size()
             - for all valid i:
@@ -1198,16 +1201,33 @@ namespace dlib
             - Any pixels in an image chip that go outside img are set to 0 (i.e. black).
     !*/
 
+    template <
+        typename image_type1,
+        typename image_type2
+        >
+    void extract_image_chips (
+        const image_type1& img,
+        const std::vector<chip_details>& chip_locations,
+        dlib::array<image_type2>& chips
+    );
+    /*!
+        ensures
+            - This function is a simple convenience / compatibility wrapper that calls the
+              above-defined extract_image_chips function using bilinear interpolation.
+    !*/
+
 // ----------------------------------------------------------------------------------------
 
     template <
         typename image_type1,
-        typename image_type2
+        typename image_type2,
+        typename interpolation_type
         >
     void extract_image_chip (
         const image_type1& img,
         const chip_details& chip_location,
-        image_type2& chip
+        image_type2& chip,
+        const interpolation_type& interp
     );
     /*!
         ensures
@@ -1215,6 +1235,21 @@ namespace dlib
               and stores the single output chip into #chip.
     !*/
 
+    template <
+        typename image_type1,
+        typename image_type2
+        >
+    void extract_image_chip (
+        const image_type1& img,
+        const chip_details& chip_location,
+        image_type2& chip
+    );
+    /*!
+        ensures
+            - This function is a simple convenience / compatibility wrapper that calls the
+              above-defined extract_image_chip function using bilinear interpolation.
+    !*/
+
 // ----------------------------------------------------------------------------------------
 
     template <

examples/CMakeLists.txt

@@ -124,12 +124,22 @@ if (NOT USING_OLD_VISUAL_STUDIO_COMPILER)
    add_gui_example(dnn_mmod_find_cars_ex)
    add_gui_example(dnn_mmod_find_cars2_ex)
    add_example(dnn_mmod_train_find_cars_ex)
+   add_gui_example(dnn_semantic_segmentation_ex)
    if (NOT MSVC)
       # Don't try to compile these programs using Visual Studio since it causes the
       # compiler to run out of RAM and to crash.  Maybe someday Visual Studio
       # won't be broken :(
+      # (NB: While the 32-bit VC++ compiler launched by the Visual Studio IDE will
+      #  run out of memory, running a 64-bit MSBuild.exe on the Command Prompt
+      #  seems to work fine. So you can try something like this:
+      #  "C:\Program Files (x86)\MSBuild\14.0\Bin\amd64\MSBuild.exe" C:\path\to\examples.sln /p:Configuration=Release /p:Platform=x64 /t:dnn_imagenet_train_ex
+      #  It does take quite a while to build these examples, though!
+      #  Note that you may additionally need to set Debug Information Format to
+      #  C7 compatible (/Z7), in case you get compiler error "cannot update
+      #  program database".)
       add_example(dnn_imagenet_train_ex)
       add_example(dnn_metric_learning_on_images_ex)
+      add_example(dnn_semantic_segmentation_train_ex)
    endif()
 endif()
 

examples/dnn_semantic_segmentation_ex.cpp

+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    This example shows how to do semantic segmentation on an image using net pretrained
+    on the PASCAL VOC2012 dataset.  For an introduction to what segmentation is, see the
+    accompanying header file dnn_semantic_segmentation_ex.h.
+
+    Instructions how to run the example:
+    1. Download the PASCAL VOC2012 data, and untar it somewhere.
+       http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+    2. Build the dnn_semantic_segmentation_train_ex example program.
+    3. Run:
+       ./dnn_semantic_segmentation_train_ex /path/to/VOC2012
+    4. Wait while the network is being trained.
+    5. Build the dnn_semantic_segmentation_ex example program.
+    6. Run:
+       ./dnn_semantic_segmentation_ex /path/to/VOC2012-or-other-images
+
+    An alternative to steps 2-4 above is to download a pre-trained network
+    from here: http://dlib.net/files/voc2012net.dnn
+
+    It would be a good idea to become familiar with dlib's DNN tooling before reading this
+    example.  So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
+    before reading this example program.
+*/
+
+#include "dnn_semantic_segmentation_ex.h"
+
+#include <iostream>
+#include <dlib/data_io.h>
+#include <dlib/gui_widgets.h>
+
+using namespace std;
+using namespace dlib;
+ 
+// ----------------------------------------------------------------------------------------
+
+// The PASCAL VOC2012 dataset contains 20 ground-truth classes + background.  Each class
+// is represented using an RGB color value.  We associate each class also to an index in the
+// range [0, 20], used internally by the network.  To generate nice RGB representations of
+// inference results, we need to be able to convert the index values to the corresponding
+// RGB values.
+
+// Given an index in the range [0, 20], find the corresponding PASCAL VOC2012 class
+// (e.g., 'dog').
+const Voc2012class& find_voc2012_class(const uint16_t& index_label)
+{
+    return find_voc2012_class(
+        [&index_label](const Voc2012class& voc2012class)
+        {
+            return index_label == voc2012class.index;
+        }
+    );
+}
+
+// Convert an index in the range [0, 20] to a corresponding RGB class label.
+inline rgb_pixel index_label_to_rgb_label(uint16_t index_label)
+{
+    return find_voc2012_class(index_label).rgb_label;
+}
+
+// Convert an image containing indexes in the range [0, 20] to a corresponding
+// image containing RGB class labels.
+void index_label_image_to_rgb_label_image(
+    const matrix<uint16_t>& index_label_image,
+    matrix<rgb_pixel>& rgb_label_image
+)
+{
+    const long nr = index_label_image.nr();
+    const long nc = index_label_image.nc();
+
+    rgb_label_image.set_size(nr, nc);
+
+    for (long r = 0; r < nr; ++r)
+    {
+        for (long c = 0; c < nc; ++c)
+        {
+            rgb_label_image(r, c) = index_label_to_rgb_label(index_label_image(r, c));
+        }
+    }
+}
+
+// Find the most prominent class label from amongst the per-pixel predictions.
+std::string get_most_prominent_non_background_classlabel(const matrix<uint16_t>& index_label_image)
+{
+    const long nr = index_label_image.nr();
+    const long nc = index_label_image.nc();
+
+    std::vector<unsigned int> counters(class_count);
+
+    for (long r = 0; r < nr; ++r)
+    {
+        for (long c = 0; c < nc; ++c)
+        {
+            const uint16_t label = index_label_image(r, c);
+            ++counters[label];
+        }
+    }
+
+    const auto max_element = std::max_element(counters.begin() + 1, counters.end());
+    const uint16_t most_prominent_index_label = max_element - counters.begin();
+
+    return find_voc2012_class(most_prominent_index_label).classlabel;
+}
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    if (argc != 2)
+    {
+        cout << "You call this program like this: " << endl;
+        cout << "./dnn_semantic_segmentation_train_ex /path/to/images" << endl;
+        cout << endl;
+        cout << "You will also need a trained 'voc2012net.dnn' file." << endl;
+        cout << "You can either train it yourself (see example program" << endl;
+        cout << "dnn_semantic_segmentation_train_ex), or download a" << endl;
+        cout << "copy from here: http://dlib.net/files/voc2012net.dnn" << endl;
+        return 1;
+    }
+
+    // Read the file containing the trained network from the working directory.
+    anet_type net;
+    deserialize("voc2012net.dnn") >> net;
+
+    // Show inference results in a window.
+    image_window win;
+
+    matrix<rgb_pixel> input_image;
+    matrix<uint16_t> index_label_image;
+    matrix<rgb_pixel> rgb_label_image;
+
+    // Find supported image files.
+    const std::vector<file> files = dlib::get_files_in_directory_tree(argv[1],
+        dlib::match_endings(".jpeg .jpg .png"));
+
+    cout << "Found " << files.size() << " images, processing..." << endl;
+
+    for (const file& file : files)
+    {
+        // Load the input image.
+        load_image(input_image, file.full_name());
+
+        // Create predictions for each pixel. At this point, the type of each prediction
+        // is an index (a value between 0 and 20). Note that the net may return an image
+        // that is not exactly the same size as the input.
+        const matrix<uint16_t> temp = net(input_image);
+
+        // Crop the returned image to be exactly the same size as the input.
+        const chip_details chip_details(
+            centered_rect(temp.nc() / 2, temp.nr() / 2, input_image.nc(), input_image.nr()),
+            chip_dims(input_image.nr(), input_image.nc())
+        );
+        extract_image_chip(temp, chip_details, index_label_image, interpolate_nearest_neighbor());
+
+        // Convert the indexes to RGB values.
+        index_label_image_to_rgb_label_image(index_label_image, rgb_label_image);
+
+        // Show the input image on the left, and the predicted RGB labels on the right.
+        win.set_image(join_rows(input_image, rgb_label_image));
+
+        // Find the most prominent class label from amongst the per-pixel predictions.
+        const std::string classlabel = get_most_prominent_non_background_classlabel(index_label_image);
+
+        cout << file.name() << " : " << classlabel << " - hit enter to process the next image";
+        cin.get();
+    }
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+

examples/dnn_semantic_segmentation_ex.h

+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    Semantic segmentation using the PASCAL VOC2012 dataset.
+
+    In segmentation, the task is to assign each pixel of an input image
+    a label - for example, 'dog'.  Then, the idea is that neighboring
+    pixels having the same label can be connected together to form a
+    larger region, representing a complete (or partially occluded) dog.
+    So technically, segmentation can be viewed as classification of
+    individual pixels (using the relevant context in the input images),
+    however the goal usually is to identify meaningful regions that
+    represent complete entities of interest (such as dogs).
+
+    Instructions how to run the example:
+    1. Download the PASCAL VOC2012 data, and untar it somewhere.
+       http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+    2. Build the dnn_semantic_segmentation_train_ex example program.
+    3. Run:
+       ./dnn_semantic_segmentation_train_ex /path/to/VOC2012
+    4. Wait while the network is being trained.
+    5. Build the dnn_semantic_segmentation_ex example program.
+    6. Run:
+       ./dnn_semantic_segmentation_ex /path/to/VOC2012-or-other-images
+
+    An alternative to steps 2-4 above is to download a pre-trained network
+    from here: http://dlib.net/files/voc2012net.dnn
+
+    It would be a good idea to become familiar with dlib's DNN tooling before reading this
+    example.  So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
+    before reading this example program.
+*/
+
+#ifndef DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
+#define DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
+
+#include <dlib/dnn.h>
+
+// ----------------------------------------------------------------------------------------
+
+inline bool operator == (const dlib::rgb_pixel& a, const dlib::rgb_pixel& b)
+{
+    return a.red == b.red && a.green == b.green && a.blue == b.blue;
+}
+
+// ----------------------------------------------------------------------------------------
+
+// The PASCAL VOC2012 dataset contains 20 ground-truth classes + background.  Each class
+// is represented using an RGB color value.  We associate each class also to an index in the
+// range [0, 20], used internally by the network.
+
+struct Voc2012class {
+    Voc2012class(uint16_t index, const dlib::rgb_pixel& rgb_label, const std::string& classlabel)
+        : index(index), rgb_label(rgb_label), classlabel(classlabel)
+    {}
+
+    // The index of the class. In the PASCAL VOC 2012 dataset, indexes from 0 to 20 are valid.
+    const uint16_t index = 0;
+
+    // The corresponding RGB representation of the class.
+    const dlib::rgb_pixel rgb_label;
+
+    // The label of the class in plain text.
+    const std::string classlabel;
+};
+
+namespace {
+    constexpr int class_count = 21; // background + 20 classes
+
+    const std::vector<Voc2012class> classes = {
+        Voc2012class(0, dlib::rgb_pixel(0, 0, 0), ""), // background
+
+        // The cream-colored `void' label is used in border regions and to mask difficult objects
+        // (see http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/devkit_doc.html)
+        Voc2012class(dlib::loss_multiclass_log_per_pixel_::label_to_ignore,
+            dlib::rgb_pixel(224, 224, 192), "border"),
+
+        Voc2012class(1,  dlib::rgb_pixel(128,   0,   0), "aeroplane"),
+        Voc2012class(2,  dlib::rgb_pixel(  0, 128,   0), "bicycle"),
+        Voc2012class(3,  dlib::rgb_pixel(128, 128,   0), "bird"),
+        Voc2012class(4,  dlib::rgb_pixel(  0,   0, 128), "boat"),
+        Voc2012class(5,  dlib::rgb_pixel(128,   0, 128), "bottle"),
+        Voc2012class(6,  dlib::rgb_pixel(  0, 128, 128), "bus"),
+        Voc2012class(7,  dlib::rgb_pixel(128, 128, 128), "car"),
+        Voc2012class(8,  dlib::rgb_pixel( 64,   0,   0), "cat"),
+        Voc2012class(9,  dlib::rgb_pixel(192,   0,   0), "chair"),
+        Voc2012class(10, dlib::rgb_pixel( 64, 128,   0), "cow"),
+        Voc2012class(11, dlib::rgb_pixel(192, 128,   0), "diningtable"),
+        Voc2012class(12, dlib::rgb_pixel( 64,   0, 128), "dog"),
+        Voc2012class(13, dlib::rgb_pixel(192,   0, 128), "horse"),
+        Voc2012class(14, dlib::rgb_pixel( 64, 128, 128), "motorbike"),
+        Voc2012class(15, dlib::rgb_pixel(192, 128, 128), "person"),
+        Voc2012class(16, dlib::rgb_pixel(  0,  64,   0), "pottedplant"),
+        Voc2012class(17, dlib::rgb_pixel(128,  64,   0), "sheep"),
+        Voc2012class(18, dlib::rgb_pixel(  0, 192,   0), "sofa"),
+        Voc2012class(19, dlib::rgb_pixel(128, 192,   0), "train"),
+        Voc2012class(20, dlib::rgb_pixel(  0,  64, 128), "tvmonitor"),
+    };
+}
+
+template <typename Predicate>
+const Voc2012class& find_voc2012_class(Predicate predicate)
+{
+    const auto i = std::find_if(classes.begin(), classes.end(), predicate);
+
+    if (i != classes.end())
+    {
+        return *i;
+    }
+    else
+    {
+        throw std::runtime_error("Unable to find a matching VOC2012 class");
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+// Introduce the building blocks used to define the segmentation network.
+// The network first does residual downsampling (similar to the dnn_imagenet_(train_)ex 
+// example program), and then residual upsampling. The network could be improved e.g.
+// by introducing skip connections from the input image, and/or the first layers, to the
+// last layer(s).  (See Long et al., Fully Convolutional Networks for Semantic Segmentation,
+// https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf)
+
+template <int N, template <typename> class BN, int stride, typename SUBNET> 
+using block = BN<dlib::con<N,3,3,1,1, dlib::relu<BN<dlib::con<N,3,3,stride,stride,SUBNET>>>>>;
+
+template <int N, template <typename> class BN, int stride, typename SUBNET> 
+using blockt = BN<dlib::cont<N,3,3,1,1,dlib::relu<BN<dlib::cont<N,3,3,stride,stride,SUBNET>>>>>;
+
+template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
+using residual = dlib::add_prev1<block<N,BN,1,dlib::tag1<SUBNET>>>;
+
+template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
+using residual_down = dlib::add_prev2<dlib::avg_pool<2,2,2,2,dlib::skip1<dlib::tag2<block<N,BN,2,dlib::tag1<SUBNET>>>>>>;
+
+template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
+using residual_up = dlib::add_prev2<dlib::cont<N,2,2,2,2,dlib::skip1<dlib::tag2<blockt<N,BN,2,dlib::tag1<SUBNET>>>>>>;
+
+template <int N, typename SUBNET> using res       = dlib::relu<residual<block,N,dlib::bn_con,SUBNET>>;
+template <int N, typename SUBNET> using ares      = dlib::relu<residual<block,N,dlib::affine,SUBNET>>;
+template <int N, typename SUBNET> using res_down  = dlib::relu<residual_down<block,N,dlib::bn_con,SUBNET>>;
+template <int N, typename SUBNET> using ares_down = dlib::relu<residual_down<block,N,dlib::affine,SUBNET>>;
+template <int N, typename SUBNET> using res_up    = dlib::relu<residual_up<block,N,dlib::bn_con,SUBNET>>;
+template <int N, typename SUBNET> using ares_up   = dlib::relu<residual_up<block,N,dlib::affine,SUBNET>>;
+
+// ----------------------------------------------------------------------------------------
+
+template <typename SUBNET> using level1 = res<512,res<512,res_down<512,SUBNET>>>;
+template <typename SUBNET> using level2 = res<256,res<256,res_down<256,SUBNET>>>;
+template <typename SUBNET> using level3 = res<128,res<128,res_down<128,SUBNET>>>;
+template <typename SUBNET> using level4 = res<64,res<64,res<64,SUBNET>>>;
+
+template <typename SUBNET> using alevel1 = ares<512,ares<512,ares_down<512,SUBNET>>>;
+template <typename SUBNET> using alevel2 = ares<256,ares<256,ares_down<256,SUBNET>>>;
+template <typename SUBNET> using alevel3 = ares<128,ares<128,ares_down<128,SUBNET>>>;
+template <typename SUBNET> using alevel4 = ares<64,ares<64,ares<64,SUBNET>>>;
+
+template <typename SUBNET> using level1t = res<512,res<512,res_up<512,SUBNET>>>;
+template <typename SUBNET> using level2t = res<256,res<256,res_up<256,SUBNET>>>;
+template <typename SUBNET> using level3t = res<128,res<128,res_up<128,SUBNET>>>;
+template <typename SUBNET> using level4t = res<64,res<64,res_up<64,SUBNET>>>;
+
+template <typename SUBNET> using alevel1t = ares<512,ares<512,ares_up<512,SUBNET>>>;
+template <typename SUBNET> using alevel2t = ares<256,ares<256,ares_up<256,SUBNET>>>;
+template <typename SUBNET> using alevel3t = ares<128,ares<128,ares_up<128,SUBNET>>>;
+template <typename SUBNET> using alevel4t = ares<64,ares<64,ares_up<64,SUBNET>>>;
+
+// ----------------------------------------------------------------------------------------
+
+// training network type
+using net_type = dlib::loss_multiclass_log_per_pixel<
+                            dlib::cont<class_count,7,7,2,2,
+                            level4t<level3t<level2t<level1t<
+                            level1<level2<level3<level4<
+                            dlib::max_pool<3,3,2,2,dlib::relu<dlib::bn_con<dlib::con<64,7,7,2,2,
+                            dlib::input<dlib::matrix<dlib::rgb_pixel>>
+                            >>>>>>>>>>>>>>;
+
+// testing network type (replaced batch normalization with fixed affine transforms)
+using anet_type = dlib::loss_multiclass_log_per_pixel<
+                            dlib::cont<class_count,7,7,2,2,
+                            alevel4t<alevel3t<alevel2t<alevel1t<
+                            alevel1<alevel2<alevel3<alevel4<
+                            dlib::max_pool<3,3,2,2,dlib::relu<dlib::affine<dlib::con<64,7,7,2,2,
+                            dlib::input<dlib::matrix<dlib::rgb_pixel>>
+                            >>>>>>>>>>>>>>;
+
+// ----------------------------------------------------------------------------------------
+
+#endif // DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
\ No newline at end of file

examples/dnn_semantic_segmentation_train_ex.cpp

+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    This example shows how to train a semantic segmentation net using the PASCAL VOC2012
+    dataset.  For an introduction to what segmentation is, see the accompanying header file
+    dnn_semantic_segmentation_ex.h.
+
+    Instructions how to run the example:
+    1. Download the PASCAL VOC2012 data, and untar it somewhere.
+       http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+    2. Build the dnn_semantic_segmentation_train_ex example program.
+    3. Run:
+       ./dnn_semantic_segmentation_train_ex /path/to/VOC2012
+    4. Wait while the network is being trained.
+    5. Build the dnn_semantic_segmentation_ex example program.
+    6. Run:
+       ./dnn_semantic_segmentation_ex /path/to/VOC2012-or-other-images
+
+    It would be a good idea to become familiar with dlib's DNN tooling before reading this
+    example.  So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
+    before reading this example program.
+*/
+
+#include "dnn_semantic_segmentation_ex.h"
+
+#include <iostream>
+#include <dlib/data_io.h>
+#include <dlib/image_transforms.h>
+#include <dlib/dir_nav.h>
+#include <iterator>
+#include <thread>
+
+using namespace std;
+using namespace dlib;
+
+// A single training sample. A mini-batch comprises many of these.
+struct training_sample
+{
+    matrix<rgb_pixel> input_image;
+    matrix<uint16_t> label_image; // The ground-truth label of each pixel.
+};
+
+// ----------------------------------------------------------------------------------------
+
+rectangle make_random_cropping_rect_resnet(
+    const matrix<rgb_pixel>& img,
+    dlib::rand& rnd
+)
+{
+    // figure out what rectangle we want to crop from the image
+    double mins = 0.466666666, maxs = 0.875;
+    auto scale = mins + rnd.get_random_double()*(maxs-mins);
+    auto size = scale*std::min(img.nr(), img.nc());
+    rectangle rect(size, size);
+    // randomly shift the box around
+    point offset(rnd.get_random_32bit_number()%(img.nc()-rect.width()),
+                 rnd.get_random_32bit_number()%(img.nr()-rect.height()));
+    return move_rect(rect, offset);
+}
+
+// ----------------------------------------------------------------------------------------
+
+void randomly_crop_image (
+    const matrix<rgb_pixel>& input_image,
+    const matrix<uint16_t>& label_image,
+    training_sample& crop,
+    dlib::rand& rnd
+)
+{
+    const auto rect = make_random_cropping_rect_resnet(input_image, rnd);
+
+    const chip_details chip_details(rect, chip_dims(227, 227));
+
+    // Crop the input image.
+    extract_image_chip(input_image, chip_details, crop.input_image, interpolate_bilinear());
+
+    // Crop the labels correspondingly. However, note that here bilinear
+    // interpolation would make absolutely no sense - you wouldn't say that
+    // a bicycle is half-way between an aeroplane and a bird, would you?
+    extract_image_chip(label_image, chip_details, crop.label_image, interpolate_nearest_neighbor());
+
+    // Also randomly flip the input image and the labels.
+    if (rnd.get_random_double() > 0.5)
+    {
+        crop.input_image = fliplr(crop.input_image);
+        crop.label_image = fliplr(crop.label_image);
+    }
+
+    // And then randomly adjust the colors.
+    apply_random_color_offset(crop.input_image, rnd);
+}
+
+// ----------------------------------------------------------------------------------------
+
+// The names of the input image and the associated RGB label image in the PASCAL VOC 2012
+// data set.
+struct image_info
+{
+    string image_filename;
+    string label_filename;
+};
+
+// Read the list of image files belonging to either the "train", "trainval", or "val" set
+// of the PASCAL VOC2012 data.
+std::vector<image_info> get_pascal_voc2012_listing(
+    const std::string& voc2012_folder,
+    const std::string& file = "train" // "train", "trainval", or "val"
+)
+{
+    std::ifstream in(voc2012_folder + "/ImageSets/Segmentation/" + file + ".txt");
+
+    std::vector<image_info> results;
+
+    while (in)
+    {
+        std::string basename;
+        in >> basename;
+
+        if (!basename.empty())
+        {
+            image_info image_info;
+            image_info.image_filename = voc2012_folder + "/JPEGImages/" + basename + ".jpg";
+            image_info.label_filename = voc2012_folder + "/SegmentationClass/" + basename + ".png";
+            results.push_back(image_info);
+        }
+    }
+
+    return results;
+}
+
+// Read the list of image files belong to the "train" set of the PASCAL VOC2012 data.
+std::vector<image_info> get_pascal_voc2012_train_listing(
+    const std::string& voc2012_folder
+)
+{
+    return get_pascal_voc2012_listing(voc2012_folder, "train");
+}
+
+// Read the list of image files belong to the "val" set of the PASCAL VOC2012 data.
+std::vector<image_info> get_pascal_voc2012_val_listing(
+    const std::string& voc2012_folder
+)
+{
+    return get_pascal_voc2012_listing(voc2012_folder, "val");
+}
+
+// ----------------------------------------------------------------------------------------
+
+// The PASCAL VOC2012 dataset contains 20 ground-truth classes + background.  Each class
+// is represented using an RGB color value.  We associate each class also to an index in the
+// range [0, 20], used internally by the network.  To convert the ground-truth data to
+// something that the network can efficiently digest, we need to be able to map the RGB
+// values to the corresponding indexes.
+
+// Given an RGB representation, find the corresponding PASCAL VOC2012 class
+// (e.g., 'dog').
+const Voc2012class& find_voc2012_class(const dlib::rgb_pixel& rgb_label)
+{
+    return find_voc2012_class(
+        [&rgb_label](const Voc2012class& voc2012class)
+        {
+            return rgb_label == voc2012class.rgb_label;
+        }
+    );
+}
+
+// Convert an RGB class label to an index in the range [0, 20].
+inline uint16_t rgb_label_to_index_label(const dlib::rgb_pixel& rgb_label)
+{
+    return find_voc2012_class(rgb_label).index;
+}
+
+// Convert an image containing RGB class labels to a corresponding
+// image containing indexes in the range [0, 20].
+void rgb_label_image_to_index_label_image(
+    const dlib::matrix<dlib::rgb_pixel>& rgb_label_image,
+    dlib::matrix<uint16_t>& index_label_image
+)
+{
+    const long nr = rgb_label_image.nr();
+    const long nc = rgb_label_image.nc();
+
+    index_label_image.set_size(nr, nc);
+
+    for (long r = 0; r < nr; ++r)
+    {
+        for (long c = 0; c < nc; ++c)
+        {
+            index_label_image(r, c) = rgb_label_to_index_label(rgb_label_image(r, c));
+        }
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+// Calculate the per-pixel accuracy on a dataset whose file names are supplied as a parameter.
+double calculate_accuracy(anet_type& anet, const std::vector<image_info>& dataset)
+{
+    int num_right = 0;
+    int num_wrong = 0;
+
+    matrix<rgb_pixel> input_image;
+    matrix<rgb_pixel> rgb_label_image;
+    matrix<uint16_t> index_label_image;
+    matrix<uint16_t> net_output;
+
+    for (const auto& image_info : dataset)
+    {
+        // Load the input image.
+        load_image(input_image, image_info.image_filename);
+
+        // Load the ground-truth (RGB) labels.
+        load_image(rgb_label_image, image_info.label_filename);
+
+        // Create predictions for each pixel. At this point, the type of each prediction
+        // is an index (a value between 0 and 20). Note that the net may return an image
+        // that is not exactly the same size as the input.
+        const matrix<uint16_t> temp = anet(input_image);
+
+        // Convert the indexes to RGB values.
+        rgb_label_image_to_index_label_image(rgb_label_image, index_label_image);
+
+        // Crop the net output to be exactly the same size as the input.
+        const chip_details chip_details(
+            centered_rect(temp.nc() / 2, temp.nr() / 2, input_image.nc(), input_image.nr()),
+            chip_dims(input_image.nr(), input_image.nc())
+        );
+        extract_image_chip(temp, chip_details, net_output, interpolate_nearest_neighbor());
+
+        const long nr = index_label_image.nr();
+        const long nc = index_label_image.nc();
+
+        // Compare the predicted values to the ground-truth values.
+        for (long r = 0; r < nr; ++r)
+        {
+            for (long c = 0; c < nc; ++c)
+            {
+                const uint16_t truth = index_label_image(r, c);
+                if (truth != dlib::loss_multiclass_log_per_pixel_::label_to_ignore)
+                {
+                    const uint16_t prediction = net_output(r, c);
+                    if (prediction == truth)
+                    {
+                        ++num_right;
+                    }
+                    else
+                    {
+                        ++num_wrong;
+                    }
+                }
+            }
+        }
+    }
+
+    // Return the accuracy estimate.
+    return num_right / static_cast<double>(num_right + num_wrong);
+}
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    if (argc != 2)
+    {
+        cout << "To run this program you need a copy of the PASCAL VOC2012 dataset." << endl;
+        cout << endl;
+        cout << "You call this program like this: " << endl;
+        cout << "./dnn_semantic_segmentation_train_ex /path/to/VOC2012" << endl;
+        return 1;
+    }
+
+    cout << "\nSCANNING PASCAL VOC2012 DATASET\n" << endl;
+
+    const auto listing = get_pascal_voc2012_train_listing(argv[1]);
+    cout << "images in dataset: " << listing.size() << endl;
+    if (listing.size() == 0)
+    {
+        cout << "Didn't find the VOC2012 dataset. " << endl;
+        return 1;
+    }
+        
+
+    const double initial_learning_rate = 0.1;
+    const double weight_decay = 0.0001;
+    const double momentum = 0.9;
+
+    net_type net;
+    dnn_trainer<net_type> trainer(net,sgd(weight_decay, momentum));
+    trainer.be_verbose();
+    trainer.set_learning_rate(initial_learning_rate);
+    trainer.set_synchronization_file("pascal_voc2012_trainer_state_file.dat", std::chrono::minutes(10));
+    // This threshold is probably excessively large.
+    trainer.set_iterations_without_progress_threshold(5000);
+    // Since the progress threshold is so large might as well set the batch normalization
+    // stats window to something big too.
+    set_all_bn_running_stats_window_sizes(net, 1000);
+
+    // Output training parameters.
+    cout << endl << trainer << endl;
+
+    std::vector<matrix<rgb_pixel>> samples;
+    std::vector<matrix<uint16_t>> labels;
+
+    // Start a bunch of threads that read images from disk and pull out random crops.  It's
+    // important to be sure to feed the GPU fast enough to keep it busy.  Using multiple
+    // thread for this kind of data preparation helps us do that.  Each thread puts the
+    // crops into the data queue.
+    dlib::pipe<training_sample> data(200);
+    auto f = [&data, &listing](time_t seed)
+    {
+        dlib::rand rnd(time(0)+seed);
+        matrix<rgb_pixel> input_image;
+        matrix<rgb_pixel> rgb_label_image;
+        matrix<uint16_t> index_label_image;
+        training_sample temp;
+        while(data.is_enabled())
+        {
+            // Pick a random input image.
+            const image_info& image_info = listing[rnd.get_random_32bit_number()%listing.size()];
+
+            // Load the input image.
+            load_image(input_image, image_info.image_filename);
+
+            // Load the ground-truth (RGB) labels.
+            load_image(rgb_label_image, image_info.label_filename);
+
+            // Convert the indexes to RGB values.
+            rgb_label_image_to_index_label_image(rgb_label_image, index_label_image);
+
+            // Randomly pick a part of the image.
+            randomly_crop_image(input_image, index_label_image, temp, rnd);
+
+            // Push the result to be used by the trainer.
+            data.enqueue(temp);
+        }
+    };
+    std::thread data_loader1([f](){ f(1); });
+    std::thread data_loader2([f](){ f(2); });
+    std::thread data_loader3([f](){ f(3); });
+    std::thread data_loader4([f](){ f(4); });
+
+    // The main training loop.  Keep making mini-batches and giving them to the trainer.
+    // We will run until the learning rate has dropped by a factor of 1e-4.
+    while(trainer.get_learning_rate() >= 1e-4)
+    {
+        samples.clear();
+        labels.clear();
+
+        // make a 30-image mini-batch
+        training_sample temp;
+        while(samples.size() < 30)
+        {
+            data.dequeue(temp);
+
+            samples.push_back(std::move(temp.input_image));
+            labels.push_back(std::move(temp.label_image));
+        }
+
+        trainer.train_one_step(samples, labels);
+    }
+
+    // Training done, tell threads to stop and make sure to wait for them to finish before
+    // moving on.
+    data.disable();
+    data_loader1.join();
+    data_loader2.join();
+    data_loader3.join();
+    data_loader4.join();
+
+    // also wait for threaded processing to stop in the trainer.
+    trainer.get_net();
+
+    net.clean();
+    cout << "saving network" << endl;
+    serialize("voc2012net.dnn") << net;
+
+
+    // Make a copy of the network to use it for inference.
+    anet_type anet = net;
+
+    cout << "Testing the network..." << endl;
+
+    // Find the accuracy of the newly trained network on both the training and the validation sets.
+    cout << "train accuracy  :  " << calculate_accuracy(anet, get_pascal_voc2012_train_listing(argv[1])) << endl;
+    cout << "val accuracy    :  " << calculate_accuracy(anet, get_pascal_voc2012_val_listing(argv[1])) << endl;
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+