mirror of https://github.com/davisking/dlib.git
122 lines
4.6 KiB
C++
122 lines
4.6 KiB
C++
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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This example program shows how to find frontal human faces in an image and
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estimate their pose. The pose takes the form of 68 landmarks. These are
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points on the face such as the corners of the mouth, along the eyebrows, on
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the eyes, and so forth.
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This example is essentially just a version of the face_landmark_detection_ex.cpp
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example modified to use dlib's demuxer object to read from a camera instead
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of files.
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Finally, note that the face detector is fastest when compiled with at least
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SSE2 instructions enabled. So if you are using a PC with an Intel or AMD
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chip then you should enable at least SSE2 instructions. If you are using
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cmake to compile this program you can enable them by using one of the
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following commands when you create the build project:
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cmake path_to_dlib_root/examples -DUSE_SSE2_INSTRUCTIONS=ON
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cmake path_to_dlib_root/examples -DUSE_SSE4_INSTRUCTIONS=ON
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cmake path_to_dlib_root/examples -DUSE_AVX_INSTRUCTIONS=ON
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This will set the appropriate compiler options for GCC, clang, Visual
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Studio, or the Intel compiler. If you are using another compiler then you
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need to consult your compiler's manual to determine how to enable these
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instructions. Note that AVX is the fastest but requires a CPU from at least
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2011. SSE4 is the next fastest and is supported by most current machines.
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*/
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#include <dlib/image_processing/frontal_face_detector.h>
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#include <dlib/image_processing/render_face_detections.h>
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#include <dlib/image_processing.h>
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#include <dlib/cmd_line_parser.h>
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#include <dlib/gui_widgets.h>
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#include <dlib/media.h>
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using namespace std;
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using namespace dlib;
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using namespace dlib::ffmpeg;
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int main(int argc, const char** argv)
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{
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try
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{
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command_line_parser parser;
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parser.add_option("height", "height of frames", 1);
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parser.add_option("width", "width of frames", 1);
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parser.add_option("framerate", "webcam desired framerate", 1);
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parser.set_group_name("Help Options");
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parser.add_option("h", "alias of --help");
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parser.add_option("help", "display this message and exit");
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parser.parse(argc, argv);
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const char* one_time_opts[] = {"height", "width", "framerate"};
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parser.check_one_time_options(one_time_opts);
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if (parser.option("h") || parser.option("help"))
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{
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parser.print_options();
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cout << "Please use `v4l2-ctl --list-formats-ext` to view all supported hardware formats\n";
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return 0;
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}
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demuxer cap{[&]
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{
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ffmpeg::demuxer::args args;
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args.filepath = "/dev/video0";
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args.height = get_option(parser, "height", 0);
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args.width = get_option(parser, "width", 0);
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args.framerate = get_option(parser, "framerate", 0);
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return args;
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}()};
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if (!cap.is_open())
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{
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cerr << "Unable to connect to camera" << endl;
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return 1;
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}
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cout << "height : " << cap.height() << '\n';
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cout << "width : " << cap.width() << '\n';
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cout << "fps : " << cap.fps() << '\n';
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image_window win;
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// Load face detection and pose estimation models.
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frontal_face_detector detector = get_frontal_face_detector();
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shape_predictor pose_model;
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deserialize("shape_predictor_68_face_landmarks.dat") >> pose_model;
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array2d<rgb_pixel> img;
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// Grab and process frames until the main window is closed by the user.
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while(cap.read(img) && !win.is_closed())
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{
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// Detect faces
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std::vector<rectangle> faces = detector(img);
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// Find the pose of each face.
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std::vector<full_object_detection> shapes;
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for (unsigned long i = 0; i < faces.size(); ++i)
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shapes.push_back(pose_model(img, faces[i]));
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// Display it all on the screen
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win.clear_overlay();
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win.set_image(img);
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win.add_overlay(render_face_detections(shapes));
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}
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}
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catch(serialization_error& e)
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{
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cout << "You need dlib's default face landmarking model file to run this example." << endl;
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cout << "You can get it from the following URL: " << endl;
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cout << " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
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cout << endl << e.what() << endl;
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}
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catch(exception& e)
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{
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cout << e.what() << endl;
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}
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}
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