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Diffstat (limited to 'ml/dlib/examples/fhog_object_detector_ex.cpp')
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diff --git a/ml/dlib/examples/fhog_object_detector_ex.cpp b/ml/dlib/examples/fhog_object_detector_ex.cpp deleted file mode 100644 index 152f57d09..000000000 --- a/ml/dlib/examples/fhog_object_detector_ex.cpp +++ /dev/null @@ -1,269 +0,0 @@ -// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt -/* - - This example program shows how you can use dlib to make an object detector - for things like faces, pedestrians, and any other semi-rigid object. In - particular, we go though the steps to train the kind of sliding window - object detector first published by Dalal and Triggs in 2005 in the paper - Histograms of Oriented Gradients for Human Detection. - - Note that this program executes fastest when compiled with at least SSE2 - instructions enabled. So if you are using a PC with an Intel or AMD chip - then you should enable at least SSE2 instructions. If you are using cmake - to compile this program you can enable them by using one of the following - commands when you create the build project: - cmake path_to_dlib_root/examples -DUSE_SSE2_INSTRUCTIONS=ON - cmake path_to_dlib_root/examples -DUSE_SSE4_INSTRUCTIONS=ON - cmake path_to_dlib_root/examples -DUSE_AVX_INSTRUCTIONS=ON - This will set the appropriate compiler options for GCC, clang, Visual - Studio, or the Intel compiler. If you are using another compiler then you - need to consult your compiler's manual to determine how to enable these - instructions. Note that AVX is the fastest but requires a CPU from at least - 2011. SSE4 is the next fastest and is supported by most current machines. - -*/ - - -#include <dlib/svm_threaded.h> -#include <dlib/gui_widgets.h> -#include <dlib/image_processing.h> -#include <dlib/data_io.h> - -#include <iostream> -#include <fstream> - - -using namespace std; -using namespace dlib; - -// ---------------------------------------------------------------------------------------- - -int main(int argc, char** argv) -{ - - try - { - // In this example we are going to train a face detector based on the - // small faces dataset in the examples/faces directory. So the first - // thing we do is load that dataset. This means you need to supply the - // path to this faces folder as a command line argument so we will know - // where it is. - if (argc != 2) - { - cout << "Give the path to the examples/faces directory as the argument to this" << endl; - cout << "program. For example, if you are in the examples folder then execute " << endl; - cout << "this program by running: " << endl; - cout << " ./fhog_object_detector_ex faces" << endl; - cout << endl; - return 0; - } - const std::string faces_directory = argv[1]; - // The faces directory contains a training dataset and a separate - // testing dataset. The training data consists of 4 images, each - // annotated with rectangles that bound each human face. The idea is - // to use this training data to learn to identify human faces in new - // images. - // - // Once you have trained an object detector it is always important to - // test it on data it wasn't trained on. Therefore, we will also load - // a separate testing set of 5 images. Once we have a face detector - // created from the training data we will see how well it works by - // running it on the testing images. - // - // So here we create the variables that will hold our dataset. - // images_train will hold the 4 training images and face_boxes_train - // holds the locations of the faces in the training images. So for - // example, the image images_train[0] has the faces given by the - // rectangles in face_boxes_train[0]. - dlib::array<array2d<unsigned char> > images_train, images_test; - std::vector<std::vector<rectangle> > face_boxes_train, face_boxes_test; - - // Now we load the data. These XML files list the images in each - // dataset and also contain the positions of the face boxes. Obviously - // you can use any kind of input format you like so long as you store - // the data into images_train and face_boxes_train. But for convenience - // dlib comes with tools for creating and loading XML image dataset - // files. Here you see how to load the data. To create the XML files - // you can use the imglab tool which can be found in the tools/imglab - // folder. It is a simple graphical tool for labeling objects in images - // with boxes. To see how to use it read the tools/imglab/README.txt - // file. - load_image_dataset(images_train, face_boxes_train, faces_directory+"/training.xml"); - load_image_dataset(images_test, face_boxes_test, faces_directory+"/testing.xml"); - - // Now we do a little bit of pre-processing. This is optional but for - // this training data it improves the results. The first thing we do is - // increase the size of the images by a factor of two. We do this - // because it will allow us to detect smaller faces than otherwise would - // be practical (since the faces are all now twice as big). Note that, - // in addition to resizing the images, these functions also make the - // appropriate adjustments to the face boxes so that they still fall on - // top of the faces after the images are resized. - upsample_image_dataset<pyramid_down<2> >(images_train, face_boxes_train); - upsample_image_dataset<pyramid_down<2> >(images_test, face_boxes_test); - // Since human faces are generally left-right symmetric we can increase - // our training dataset by adding mirrored versions of each image back - // into images_train. So this next step doubles the size of our - // training dataset. Again, this is obviously optional but is useful in - // many object detection tasks. - add_image_left_right_flips(images_train, face_boxes_train); - cout << "num training images: " << images_train.size() << endl; - cout << "num testing images: " << images_test.size() << endl; - - - // Finally we get to the training code. dlib contains a number of - // object detectors. This typedef tells it that you want to use the one - // based on Felzenszwalb's version of the Histogram of Oriented - // Gradients (commonly called HOG) detector. The 6 means that you want - // it to use an image pyramid that downsamples the image at a ratio of - // 5/6. Recall that HOG detectors work by creating an image pyramid and - // then running the detector over each pyramid level in a sliding window - // fashion. - typedef scan_fhog_pyramid<pyramid_down<6> > image_scanner_type; - image_scanner_type scanner; - // The sliding window detector will be 80 pixels wide and 80 pixels tall. - scanner.set_detection_window_size(80, 80); - structural_object_detection_trainer<image_scanner_type> trainer(scanner); - // Set this to the number of processing cores on your machine. - trainer.set_num_threads(4); - // The trainer is a kind of support vector machine and therefore has the usual SVM - // C parameter. In general, a bigger C encourages it to fit the training data - // better but might lead to overfitting. You must find the best C value - // empirically by checking how well the trained detector works on a test set of - // images you haven't trained on. Don't just leave the value set at 1. Try a few - // different C values and see what works best for your data. - trainer.set_c(1); - // We can tell the trainer to print it's progress to the console if we want. - trainer.be_verbose(); - // The trainer will run until the "risk gap" is less than 0.01. Smaller values - // make the trainer solve the SVM optimization problem more accurately but will - // take longer to train. For most problems a value in the range of 0.1 to 0.01 is - // plenty accurate. Also, when in verbose mode the risk gap is printed on each - // iteration so you can see how close it is to finishing the training. - trainer.set_epsilon(0.01); - - - // Now we run the trainer. For this example, it should take on the order of 10 - // seconds to train. - object_detector<image_scanner_type> detector = trainer.train(images_train, face_boxes_train); - - // Now that we have a face detector we can test it. The first statement tests it - // on the training data. It will print the precision, recall, and then average precision. - cout << "training results: " << test_object_detection_function(detector, images_train, face_boxes_train) << endl; - // However, to get an idea if it really worked without overfitting we need to run - // it on images it wasn't trained on. The next line does this. Happily, we see - // that the object detector works perfectly on the testing images. - cout << "testing results: " << test_object_detection_function(detector, images_test, face_boxes_test) << endl; - - - // If you have read any papers that use HOG you have probably seen the nice looking - // "sticks" visualization of a learned HOG detector. This next line creates a - // window with such a visualization of our detector. It should look somewhat like - // a face. - image_window hogwin(draw_fhog(detector), "Learned fHOG detector"); - - // Now for the really fun part. Let's display the testing images on the screen and - // show the output of the face detector overlaid on each image. You will see that - // it finds all the faces without false alarming on any non-faces. - image_window win; - for (unsigned long i = 0; i < images_test.size(); ++i) - { - // Run the detector and get the face detections. - std::vector<rectangle> dets = detector(images_test[i]); - win.clear_overlay(); - win.set_image(images_test[i]); - win.add_overlay(dets, rgb_pixel(255,0,0)); - cout << "Hit enter to process the next image..." << endl; - cin.get(); - } - - - // Like everything in dlib, you can save your detector to disk using the - // serialize() function. - serialize("face_detector.svm") << detector; - - // Then you can recall it using the deserialize() function. - object_detector<image_scanner_type> detector2; - deserialize("face_detector.svm") >> detector2; - - - - - // Now let's talk about some optional features of this training tool as well as some - // important points you should understand. - // - // The first thing that should be pointed out is that, since this is a sliding - // window classifier, it can't output an arbitrary rectangle as a detection. In - // this example our sliding window is 80 by 80 pixels and is run over an image - // pyramid. This means that it can only output detections that are at least 80 by - // 80 pixels in size (recall that this is why we upsampled the images after loading - // them). It also means that the aspect ratio of the outputs is 1. So if, - // for example, you had a box in your training data that was 200 pixels by 10 - // pixels then it would simply be impossible for the detector to learn to detect - // it. Similarly, if you had a really small box it would be unable to learn to - // detect it. - // - // So the training code performs an input validation check on the training data and - // will throw an exception if it detects any boxes that are impossible to detect - // given your setting of scanning window size and image pyramid resolution. You - // can use a statement like: - // remove_unobtainable_rectangles(trainer, images_train, face_boxes_train) - // to automatically discard these impossible boxes from your training dataset - // before running the trainer. This will avoid getting the "impossible box" - // exception. However, I would recommend you be careful that you are not throwing - // away truth boxes you really care about. The remove_unobtainable_rectangles() - // will return the set of removed rectangles so you can visually inspect them and - // make sure you are OK that they are being removed. - // - // Next, note that any location in the images not marked with a truth box is - // implicitly treated as a negative example. This means that when creating - // training data it is critical that you label all the objects you want to detect. - // So for example, if you are making a face detector then you must mark all the - // faces in each image. However, sometimes there are objects in images you are - // unsure about or simply don't care if the detector identifies or not. For these - // objects you can pass in a set of "ignore boxes" as a third argument to the - // trainer.train() function. The trainer will simply disregard any detections that - // happen to hit these boxes. - // - // Another useful thing you can do is evaluate multiple HOG detectors together. The - // benefit of this is increased testing speed since it avoids recomputing the HOG - // features for each run of the detector. You do this by storing your detectors - // into a std::vector and then invoking evaluate_detectors() like so: - std::vector<object_detector<image_scanner_type> > my_detectors; - my_detectors.push_back(detector); - std::vector<rectangle> dets = evaluate_detectors(my_detectors, images_train[0]); - // - // - // Finally, you can add a nuclear norm regularizer to the SVM trainer. Doing has - // two benefits. First, it can cause the learned HOG detector to be composed of - // separable filters and therefore makes it execute faster when detecting objects. - // It can also help with generalization since it tends to make the learned HOG - // filters smoother. To enable this option you call the following function before - // you create the trainer object: - // scanner.set_nuclear_norm_regularization_strength(1.0); - // The argument determines how important it is to have a small nuclear norm. A - // bigger regularization strength means it is more important. The smaller the - // nuclear norm the smoother and faster the learned HOG filters will be, but if the - // regularization strength value is too large then the SVM will not fit the data - // well. This is analogous to giving a C value that is too small. - // - // You can see how many separable filters are inside your detector like so: - cout << "num filters: "<< num_separable_filters(detector) << endl; - // You can also control how many filters there are by explicitly thresholding the - // singular values of the filters like this: - detector = threshold_filter_singular_values(detector,0.1); - // That removes filter components with singular values less than 0.1. The bigger - // this number the fewer separable filters you will have and the faster the - // detector will run. However, a large enough threshold will hurt detection - // accuracy. - - } - catch (exception& e) - { - cout << "\nexception thrown!" << endl; - cout << e.what() << endl; - } -} - -// ---------------------------------------------------------------------------------------- - |