1 files changed, 0 insertions, 260 deletions
diff --git a/ml/dlib/tools/imglab/src/cluster.cpp b/ml/dlib/tools/imglab/src/cluster.cpp
deleted file mode 100644
index 23b289a7f..000000000
--- a/ml/dlib/tools/imglab/src/cluster.cpp
+++ /dev/null
@@ -1,260 +0,0 @@
-// Copyright (C) 2015  Davis E. King (davis@dlib.net)
-// License: Boost Software License   See LICENSE.txt for the full license.
-
-#include "cluster.h" 
-#include <dlib/console_progress_indicator.h>
-#include <dlib/image_io.h>
-#include <dlib/data_io.h>
-#include <dlib/image_transforms.h>
-#include <dlib/misc_api.h>
-#include <dlib/dir_nav.h>
-#include <dlib/clustering.h>
-#include <dlib/svm.h>
-
-// ----------------------------------------------------------------------------------------
-
-using namespace std;
-using namespace dlib;
-
-//  ----------------------------------------------------------------------------
-
-struct assignment
-{
-    unsigned long c;
-    double dist;
-    unsigned long idx;
-
-    bool operator<(const assignment& item) const
-    { return dist < item.dist; }
-};
-
-std::vector<assignment> angular_cluster (
-    std::vector<matrix<double,0,1> > feats,
-    const unsigned long num_clusters
-)
-{
-    DLIB_CASSERT(feats.size() != 0, "The dataset can't be empty");
-    for (unsigned long i = 0; i < feats.size(); ++i)
-    {
-        DLIB_CASSERT(feats[i].size() == feats[0].size(), "All feature vectors must have the same length.");
-    }
-
-    // find the centroid of feats
-    matrix<double,0,1> m;
-    for (unsigned long i = 0; i < feats.size(); ++i)
-        m += feats[i];
-    m /= feats.size();
-
-    // Now center feats and then project onto the unit sphere.  The reason for projecting
-    // onto the unit sphere is so pick_initial_centers() works in a sensible way.
-    for (unsigned long i = 0; i < feats.size(); ++i)
-    {
-        feats[i] -= m;
-        double len = length(feats[i]);
-        if (len != 0)
-            feats[i] /= len;
-    }
-
-    // now do angular clustering of the points
-    std::vector<matrix<double,0,1> > centers;
-    pick_initial_centers(num_clusters, centers, feats, linear_kernel<matrix<double,0,1> >(), 0.05);
-    find_clusters_using_angular_kmeans(feats, centers);
-
-    // and then report the resulting assignments
-    std::vector<assignment> assignments;
-    for (unsigned long i = 0; i < feats.size(); ++i)
-    {
-        assignment temp;
-        temp.c = nearest_center(centers, feats[i]);
-        temp.dist = length(feats[i] - centers[temp.c]);
-        temp.idx = i;
-        assignments.push_back(temp);
-    }
-    return assignments;
-}
-
-// ----------------------------------------------------------------------------------------
-
-bool compare_first (
-    const std::pair<double,image_dataset_metadata::image>& a,
-    const std::pair<double,image_dataset_metadata::image>& b
-)
-{
-    return a.first < b.first;
-}
-
-// ----------------------------------------------------------------------------------------
-
-double mean_aspect_ratio (
-    const image_dataset_metadata::dataset& data
-)
-{
-    double sum = 0;
-    double cnt = 0;
-    for (unsigned long i = 0; i < data.images.size(); ++i)
-    {
-        for (unsigned long j = 0; j < data.images[i].boxes.size(); ++j)
-        {
-            rectangle rect = data.images[i].boxes[j].rect;
-            if (rect.area() == 0 || data.images[i].boxes[j].ignore)
-                continue;
-            sum += rect.width()/(double)rect.height();
-            ++cnt;
-        }
-    }
-
-    if (cnt != 0)
-        return sum/cnt;
-    else
-        return 0;
-}
-
-// ----------------------------------------------------------------------------------------
-
-bool has_non_ignored_boxes (const image_dataset_metadata::image& img)
-{
-    for (auto&& b : img.boxes)
-    {
-        if (!b.ignore)
-            return true;
-    }
-    return false;
-}
-
-// ----------------------------------------------------------------------------------------
-
-int cluster_dataset(
-    const dlib::command_line_parser& parser
-)
-{
-    // make sure the user entered an argument to this program
-    if (parser.number_of_arguments() != 1)
-    {
-        cerr << "The --cluster option requires you to give one XML file on the command line." << endl;
-        return EXIT_FAILURE;
-    }
-
-    const unsigned long num_clusters = get_option(parser, "cluster", 2);
-    const unsigned long chip_size = get_option(parser, "size", 8000);
-
-    image_dataset_metadata::dataset data;
-
-    image_dataset_metadata::load_image_dataset_metadata(data, parser[0]);
-    set_current_dir(get_parent_directory(file(parser[0])));
-
-    const double aspect_ratio = mean_aspect_ratio(data);
-
-    dlib::array<array2d<rgb_pixel> > images;
-    std::vector<matrix<double,0,1> > feats;
-    console_progress_indicator pbar(data.images.size());
-    // extract all the object chips and HOG features.
-    cout << "Loading image data..." << endl;
-    for (unsigned long i = 0; i < data.images.size(); ++i)
-    {
-        pbar.print_status(i);
-        if (!has_non_ignored_boxes(data.images[i]))
-            continue;
-
-        array2d<rgb_pixel> img, chip;
-        load_image(img, data.images[i].filename);
-
-        for (unsigned long j = 0; j < data.images[i].boxes.size(); ++j)
-        {
-            if (data.images[i].boxes[j].ignore || data.images[i].boxes[j].rect.area() < 10)
-                continue;
-            drectangle rect = data.images[i].boxes[j].rect;
-            rect = set_aspect_ratio(rect, aspect_ratio);
-            extract_image_chip(img, chip_details(rect, chip_size), chip);
-            feats.push_back(extract_fhog_features(chip));
-            images.push_back(chip);
-        }
-    }
-
-    if (feats.size() == 0)
-    {
-        cerr << "No non-ignored object boxes found in the XML dataset.  You can't cluster an empty dataset." << endl;
-        return EXIT_FAILURE;
-    }
-
-    cout << "\nClustering objects..." << endl;
-    std::vector<assignment> assignments = angular_cluster(feats, num_clusters);
-
-
-    // Now output each cluster to disk as an XML file.
-    for (unsigned long c = 0; c < num_clusters; ++c)
-    {
-        // We are going to accumulate all the image metadata for cluster c.  We put it
-        // into idata so we can sort the images such that images with central chips
-        // come before less central chips.  The idea being to get the good chips to
-        // show up first in the listing, making it easy to manually remove bad ones if
-        // that is desired.
-        std::vector<std::pair<double,image_dataset_metadata::image> > idata(data.images.size());
-        unsigned long idx = 0;
-        for (unsigned long i = 0; i < data.images.size(); ++i)
-        {
-            idata[i].first = std::numeric_limits<double>::infinity();
-            idata[i].second.filename = data.images[i].filename;
-            if (!has_non_ignored_boxes(data.images[i]))
-                continue;
-
-            for (unsigned long j = 0; j < data.images[i].boxes.size(); ++j)
-            {
-                idata[i].second.boxes.push_back(data.images[i].boxes[j]);
-
-                if (data.images[i].boxes[j].ignore || data.images[i].boxes[j].rect.area() < 10)
-                    continue;
-
-                // If this box goes into cluster c then update the score for the whole
-                // image based on this boxes' score.  Otherwise, mark the box as
-                // ignored.
-                if (assignments[idx].c == c)
-                    idata[i].first = std::min(idata[i].first, assignments[idx].dist);
-                else
-                    idata[i].second.boxes.back().ignore = true;
-
-                ++idx;
-            }
-        }
-
-        // now save idata to an xml file.
-        std::sort(idata.begin(), idata.end(), compare_first);
-        image_dataset_metadata::dataset cdata;
-        cdata.comment = data.comment + "\n\n This file contains objects which were clustered into group " + 
-                     cast_to_string(c+1) + " of " + cast_to_string(num_clusters) + " groups with a chip size of " + 
-                     cast_to_string(chip_size) + " by imglab.";
-        cdata.name = data.name;
-        for (unsigned long i = 0; i < idata.size(); ++i)
-        {
-            // if this image has non-ignored boxes in it then include it in the output.
-            if (idata[i].first != std::numeric_limits<double>::infinity())
-                cdata.images.push_back(idata[i].second);
-        }
-
-        string outfile = "cluster_"+pad_int_with_zeros(c+1, 3) + ".xml";
-        cout << "Saving " << outfile << endl;
-        save_image_dataset_metadata(cdata, outfile);
-    }
-
-    // Now output each cluster to disk as a big tiled jpeg file.  Sort everything so, just
-    // like in the xml file above, the best objects come first in the tiling.
-    std::sort(assignments.begin(), assignments.end());
-    for (unsigned long c = 0; c < num_clusters; ++c)
-    {
-        dlib::array<array2d<rgb_pixel> > temp;
-        for (unsigned long i = 0; i < assignments.size(); ++i)
-        {
-            if (assignments[i].c == c)
-                temp.push_back(images[assignments[i].idx]);
-        }
-
-        string outfile = "cluster_"+pad_int_with_zeros(c+1, 3) + ".jpg";
-        cout << "Saving " << outfile << endl;
-        save_jpeg(tile_images(temp), outfile);
-    }
-
-
-    return EXIT_SUCCESS;
-}
-
-// ----------------------------------------------------------------------------------------
-