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Diffstat (limited to 'ml/dlib/examples/webcam_face_pose_ex.cpp')
| -rw-r--r-- | ml/dlib/examples/webcam_face_pose_ex.cpp | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/ml/dlib/examples/webcam_face_pose_ex.cpp b/ml/dlib/examples/webcam_face_pose_ex.cpp new file mode 100644 index 000000000..e3b00d0f2 --- /dev/null +++ b/ml/dlib/examples/webcam_face_pose_ex.cpp @@ -0,0 +1,100 @@ +// 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. + + + This example is essentially just a version of the face_landmark_detection_ex.cpp + example modified to use OpenCV's VideoCapture object to read from a camera instead + of files. + + + 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/opencv.h> +#include <opencv2/highgui/highgui.hpp> +#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> + +using namespace dlib; +using namespace std; + +int main() +{ + try + { + cv::VideoCapture cap(0); + if (!cap.isOpened()) + { + cerr << "Unable to connect to camera" << endl; + return 1; + } + + image_window win; + + // Load face detection and pose estimation models. + frontal_face_detector detector = get_frontal_face_detector(); + shape_predictor pose_model; + deserialize("shape_predictor_68_face_landmarks.dat") >> pose_model; + + // Grab and process frames until the main window is closed by the user. + while(!win.is_closed()) + { + // Grab a frame + cv::Mat temp; + if (!cap.read(temp)) + { + break; + } + // Turn OpenCV's Mat into something dlib can deal with. Note that this just + // wraps the Mat object, it doesn't copy anything. So cimg is only valid as + // long as temp is valid. Also don't do anything to temp that would cause it + // to reallocate the memory which stores the image as that will make cimg + // contain dangling pointers. This basically means you shouldn't modify temp + // while using cimg. + cv_image<bgr_pixel> cimg(temp); + + // Detect faces + std::vector<rectangle> faces = detector(cimg); + // Find the pose of each face. + std::vector<full_object_detection> shapes; + for (unsigned long i = 0; i < faces.size(); ++i) + shapes.push_back(pose_model(cimg, faces[i])); + + // Display it all on the screen + win.clear_overlay(); + win.set_image(cimg); + win.add_overlay(render_face_detections(shapes)); + } + } + catch(serialization_error& e) + { + cout << "You need dlib's default face landmarking model file to run this example." << endl; + cout << "You can get it from the following URL: " << endl; + cout << " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl; + cout << endl << e.what() << endl; + } + catch(exception& e) + { + cout << e.what() << endl; + } +} + |