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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, 0 insertions, 100 deletions
diff --git a/ml/dlib/examples/webcam_face_pose_ex.cpp b/ml/dlib/examples/webcam_face_pose_ex.cpp deleted file mode 100644 index e3b00d0f2..000000000 --- a/ml/dlib/examples/webcam_face_pose_ex.cpp +++ /dev/null @@ -1,100 +0,0 @@ -// 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; - } -} - |