mirror of https://github.com/davisking/dlib.git
98 lines
3.8 KiB
C++
98 lines
3.8 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 OpenCV's VideoCapture 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/opencv.h>
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#include <opencv2/highgui/highgui.hpp>
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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/gui_widgets.h>
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using namespace dlib;
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using namespace std;
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int main()
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{
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try
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{
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cv::VideoCapture cap(0);
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if (!cap.isOpened())
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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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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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// Grab and process frames until the main window is closed by the user.
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while(!win.is_closed())
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{
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// Grab a frame
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cv::Mat temp;
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cap >> temp;
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// Turn OpenCV's Mat into something dlib can deal with. Note that this just
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// wraps the Mat object, it doesn't copy anything. So cimg is only valid as
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// long as temp is valid. Also don't do anything to temp that would cause it
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// to reallocate the memory which stores the image as that will make cimg
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// contain dangling pointers. This basically means you shouldn't modify temp
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// while using cimg.
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cv_image<bgr_pixel> cimg(temp);
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// Detect faces
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std::vector<rectangle> faces = detector(cimg);
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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(cimg, 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(cimg);
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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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