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Diffstat (limited to 'ml/dlib/examples/dnn_semantic_segmentation_ex.cpp')
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diff --git a/ml/dlib/examples/dnn_semantic_segmentation_ex.cpp b/ml/dlib/examples/dnn_semantic_segmentation_ex.cpp deleted file mode 100644 index fa49c5a9e..000000000 --- a/ml/dlib/examples/dnn_semantic_segmentation_ex.cpp +++ /dev/null @@ -1,172 +0,0 @@ -// 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/semantic_segmentation_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 'semantic_segmentation_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/semantic_segmentation_voc2012net.dnn" << endl; - return 1; - } - - // Read the file containing the trained network from the working directory. - anet_type net; - deserialize("semantic_segmentation_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; -} - |