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Diffstat (limited to 'src/ml/dlib/examples/face_landmark_detection_ex.cpp')
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diff --git a/src/ml/dlib/examples/face_landmark_detection_ex.cpp b/src/ml/dlib/examples/face_landmark_detection_ex.cpp new file mode 100644 index 000000000..6ab7fdf9d --- /dev/null +++ b/src/ml/dlib/examples/face_landmark_detection_ex.cpp @@ -0,0 +1,144 @@ +// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt +/* + + This example program shows how to find frontal human faces in an image and + estimate their pose. The pose takes the form of 68 landmarks. These are + points on the face such as the corners of the mouth, along the eyebrows, on + the eyes, and so forth. + + + + The face detector we use is made using the classic Histogram of Oriented + Gradients (HOG) feature combined with a linear classifier, an image pyramid, + and sliding window detection scheme. The pose estimator was created by + using dlib's implementation of the paper: + One Millisecond Face Alignment with an Ensemble of Regression Trees by + Vahid Kazemi and Josephine Sullivan, CVPR 2014 + and was trained on the iBUG 300-W face landmark dataset (see + https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/): + C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. + 300 faces In-the-wild challenge: Database and results. + Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016. + You can get the trained model file from: + http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2. + Note that the license for the iBUG 300-W dataset excludes commercial use. + So you should contact Imperial College London to find out if it's OK for + you to use this model file in a commercial product. + + + Also, note that you can train your own models using dlib's machine learning + tools. See train_shape_predictor_ex.cpp to see an example. + + + + + Finally, note that the face detector is 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/image_processing/frontal_face_detector.h> +#include <dlib/image_processing/render_face_detections.h> +#include <dlib/image_processing.h> +#include <dlib/gui_widgets.h> +#include <dlib/image_io.h> +#include <iostream> + +using namespace dlib; +using namespace std; + +// ---------------------------------------------------------------------------------------- + +int main(int argc, char** argv) +{ + try + { + // This example takes in a shape model file and then a list of images to + // process. We will take these filenames in as command line arguments. + // Dlib comes with example images in the examples/faces folder so give + // those as arguments to this program. + if (argc == 1) + { + cout << "Call this program like this:" << endl; + cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl; + cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n"; + cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl; + return 0; + } + + // We need a face detector. We will use this to get bounding boxes for + // each face in an image. + frontal_face_detector detector = get_frontal_face_detector(); + // And we also need a shape_predictor. This is the tool that will predict face + // landmark positions given an image and face bounding box. Here we are just + // loading the model from the shape_predictor_68_face_landmarks.dat file you gave + // as a command line argument. + shape_predictor sp; + deserialize(argv[1]) >> sp; + + + image_window win, win_faces; + // Loop over all the images provided on the command line. + for (int i = 2; i < argc; ++i) + { + cout << "processing image " << argv[i] << endl; + array2d<rgb_pixel> img; + load_image(img, argv[i]); + // Make the image larger so we can detect small faces. + pyramid_up(img); + + // Now tell the face detector to give us a list of bounding boxes + // around all the faces in the image. + std::vector<rectangle> dets = detector(img); + cout << "Number of faces detected: " << dets.size() << endl; + + // Now we will go ask the shape_predictor to tell us the pose of + // each face we detected. + std::vector<full_object_detection> shapes; + for (unsigned long j = 0; j < dets.size(); ++j) + { + full_object_detection shape = sp(img, dets[j]); + cout << "number of parts: "<< shape.num_parts() << endl; + cout << "pixel position of first part: " << shape.part(0) << endl; + cout << "pixel position of second part: " << shape.part(1) << endl; + // You get the idea, you can get all the face part locations if + // you want them. Here we just store them in shapes so we can + // put them on the screen. + shapes.push_back(shape); + } + + // Now let's view our face poses on the screen. + win.clear_overlay(); + win.set_image(img); + win.add_overlay(render_face_detections(shapes)); + + // We can also extract copies of each face that are cropped, rotated upright, + // and scaled to a standard size as shown here: + dlib::array<array2d<rgb_pixel> > face_chips; + extract_image_chips(img, get_face_chip_details(shapes), face_chips); + win_faces.set_image(tile_images(face_chips)); + + cout << "Hit enter to process the next image..." << endl; + cin.get(); + } + } + catch (exception& e) + { + cout << "\nexception thrown!" << endl; + cout << e.what() << endl; + } +} + +// ---------------------------------------------------------------------------------------- + |