// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This example shows how to train a CNN based object detector using dlib's
loss_mmod loss layer. This loss layer implements the Max-Margin Object
Detection loss as described in the paper:
Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046).
This is the same loss used by the popular SVM+HOG object detector in dlib
(see fhog_object_detector_ex.cpp) except here we replace the HOG features
with a CNN and train the entire detector end-to-end. This allows us to make
much more powerful detectors.
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.
Just like in the fhog_object_detector_ex.cpp example, we are going to train
a simple face detector based on the very small training dataset in the
examples/faces folder. As we will see, even with this small dataset the
MMOD method is able to make a working face detector. However, for real
applications you should train with more data for an even better result.
*/
#include <iostream>
#include <dlib/dnn.h>
#include <dlib/data_io.h>
#include <dlib/gui_widgets.h>
using namespace std;
using namespace dlib;
// The first thing we do is define our CNN. The CNN is going to be evaluated
// convolutionally over an entire image pyramid. Think of it like a normal
// sliding window classifier. This means you need to define a CNN that can look
// at some part of an image and decide if it is an object of interest. In this
// example I've defined a CNN with a receptive field of a little over 50x50
// pixels. This is reasonable for face detection since you can clearly tell if
// a 50x50 image contains a face. Other applications may benefit from CNNs with
// different architectures.
//
// In this example our CNN begins with 3 downsampling layers. These layers will
// reduce the size of the image by 8x and output a feature map with
// 32 dimensions. Then we will pass that through 4 more convolutional layers to
// get the final output of the network. The last layer has only 1 channel and
// the values in that last channel are large when the network thinks it has
// found an object at a particular location.
// Let's begin the network definition by creating some network blocks.
// A 5x5 conv layer that does 2x downsampling
template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
// A 3x3 conv layer that doesn't do any downsampling
template <long num_filters, typename SUBNET> using con3 = con<num_filters,3,3,1,1,SUBNET>;
// Now we can define the 8x downsampling block in terms of conv5d blocks. We
// also use relu and batch normalization in the standard way.
template <typename SUBNET> using downsampler = relu<bn_con<con5d<32, relu<bn_con<con5d<32, relu<bn_con<con5d<32,SUBNET>>>>>>>>>;
// The rest of the network will be 3x3 conv layers with batch normalization and
// relu. So we define the 3x3 block we will use here.
template <typename SUBNET> using rcon3 = relu<bn_con<con3<32,SUBNET>>>;
// Finally, we define the entire network. The special input_rgb_image_pyramid
// layer causes the network to operate over a spatial pyramid, making the detector
// scale invariant.
using net_type = loss_mmod<con<1,6,6,1,1,rcon3<rcon3<rcon3<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
// ----------------------------------------------------------------------------------------
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 << " ./dnn_mmod_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].
std::vector<matrix<rgb_pixel>> images_train, images_test;
std::vector<std::vector<mmod_rect>> 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 datasets. 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");
cout << "num training images: " << images_train.size() << endl;
cout << "num testing images: " << images_test.size() << endl;
// The MMOD algorithm has some options you can set to control its behavior. However,
// you can also call the constructor with your training annotations and a "target
// object size" and it will automatically configure itself in a reasonable way for your
// problem. Here we are saying that faces are still recognizably faces when they are
// 40x40 pixels in size. You should generally pick the smallest size where this is
// true. Based on this information the mmod_options constructor will automatically
// pick a good sliding window width and height. It will also automatically set the
// non-max-suppression parameters to something reasonable. For further details see the
// mmod_options documentation.
mmod_options options(face_boxes_train, 40,40);
// The detector will automatically decide to use multiple sliding windows if needed.
// For the face data, only one is needed however.
cout << "num detector windows: "<< options.detector_windows.size() << endl;
for (auto& w : options.detector_windows)
cout << "detector window width by height: " << w.width << " x " << w.height << endl;
cout << "overlap NMS IOU thresh: " << options.overlaps_nms.get_iou_thresh() << endl;
cout << "overlap NMS percent covered thresh: " << options.overlaps_nms.get_percent_covered_thresh() << endl;
// Now we are ready to create our network and trainer.
net_type net(options);
// The MMOD loss requires that the number of filters in the final network layer equal
// options.detector_windows.size(). So we set that here as well.
net.subnet().layer_details().set_num_filters(options.detector_windows.size());
dnn_trainer<net_type> trainer(net);
trainer.set_learning_rate(0.1);
trainer.be_verbose();
trainer.set_synchronization_file("mmod_sync", std::chrono::minutes(5));
trainer.set_iterations_without_progress_threshold(300);
// Now let's train the network. We are going to use mini-batches of 150
// images. The images are random crops from our training set (see
// random_cropper_ex.cpp for a discussion of the random_cropper).
std::vector<matrix<rgb_pixel>> mini_batch_samples;
std::vector<std::vector<mmod_rect>> mini_batch_labels;
random_cropper cropper;
cropper.set_chip_dims(200, 200);
cropper.set_min_object_size(0.2);
dlib::rand rnd;
// Run the trainer until the learning rate gets small. This will probably take several
// hours.
while(trainer.get_learning_rate() >= 1e-4)
{
cropper(150, images_train, face_boxes_train, mini_batch_samples, mini_batch_labels);
// We can also randomly jitter the colors and that often helps a detector
// generalize better to new images.
for (auto&& img : mini_batch_samples)
disturb_colors(img, rnd);
trainer.train_one_step(mini_batch_samples, mini_batch_labels);
}
// wait for training threads to stop
trainer.get_net();
cout << "done training" << endl;
// Save the network to disk
net.clean();
serialize("mmod_network.dat") << net;
// 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.
// This statement should indicate that the network works perfectly on the
// training data.
cout << "training results: " << test_object_detection_function(net, 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,
// this statement indicates that the detector finds most of the faces in the
// testing data.
cout << "testing results: " << test_object_detection_function(net, images_test, face_boxes_test) << endl;
// If you are running many experiments, it's also useful to log the settings used
// during the training experiment. This statement will print the settings we used to
// the screen.
cout << trainer << cropper << endl;
// Now lets run the detector on the testing images and look at the outputs.
image_window win;
for (auto&& img : images_test)
{
pyramid_up(img);
auto dets = net(img);
win.clear_overlay();
win.set_image(img);
for (auto&& d : dets)
win.add_overlay(d);
cin.get();
}
return 0;
// Now that you finished this example, you should read dnn_mmod_train_find_cars_ex.cpp,
// which is a more advanced example. It discusses many issues surrounding properly
// setting the MMOD parameters and creating a good training dataset.
}
catch(std::exception& e)
{
cout << e.what() << endl;
}