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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,41 @@ | ||
| // | ||
| // Created by caesar kekxv on 2020/5/20. | ||
| // | ||
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| #include <opencv_tool.h> | ||
| #include <logger.h> | ||
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| using namespace std; | ||
| using namespace cv; | ||
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| int main(int argc, char *argv[]) { | ||
| logger::instance()->init_default(); | ||
| logger::instance()->d(__FILENAME__, __LINE__, "running go"); | ||
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| opencv_tool opencvTool(logger::get_local_path() + "/data/haarcascades/haarcascade_frontalface_default.xml"); | ||
| logger::instance()->d(__FILENAME__, __LINE__, "opencvTool load %s", opencvTool.isLoad() ? "True" : "False"); | ||
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| cv::VideoCapture cap; | ||
| cap.open(0); | ||
| int fps = (int) (1000 / cap.get(CAP_PROP_FPS)); | ||
| while (cap.isOpened()) { | ||
| Mat frame; | ||
| cap >> frame; | ||
| opencv_tool::rotate180(frame); | ||
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| vector<Rect> faces; | ||
| opencvTool.findMaxFace(frame,faces); | ||
| for(const auto& item : faces){ | ||
| cv::rectangle(frame, item, Scalar(255, 255, 255),2, LINE_8,0); | ||
| } | ||
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| imshow("frame", frame); | ||
| if (27 == waitKey(fps)) { | ||
| break; | ||
| } | ||
| } | ||
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| logger::instance()->d(__FILENAME__, __LINE__, "running finish"); | ||
| return 0; | ||
| } | ||
|
|
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| Original file line number | Diff line number | Diff line change |
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| // 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. | ||
| */ | ||
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| #include <dlib/opencv.h> | ||
| #include <opencv2/opencv.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> | ||
| #include <src/logger/logger.h> | ||
| #include <opencv_tool.h> | ||
|
|
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| using namespace dlib; | ||
| using namespace std; | ||
|
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| int main() { | ||
| try { | ||
| cv::VideoCapture cap(0); | ||
| if (!cap.isOpened()) { | ||
| cerr << "Unable to connect to camera" << endl; | ||
| return 1; | ||
| } | ||
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| image_window win; | ||
| logger::instance()->init_default(); | ||
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| // 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; | ||
| deserialize("shape_predictor_5_face_landmarks.dat") >> pose_model; | ||
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| // 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; | ||
| } | ||
| auto start = logger::get_time_tick(); | ||
| resize(temp, temp, cv::Size(320, 180)); | ||
| // opencv_tool::rotate180(temp); | ||
| // 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); | ||
| logger::instance()->i(__FILENAME__, __LINE__, "%lld", logger::get_time_tick() - start); | ||
| // Detect faces | ||
| std::vector<rectangle> faces = detector(cimg); | ||
| logger::instance()->d(__FILENAME__, __LINE__, "%lld : faces.size() : %lu", logger::get_time_tick() - start, | ||
| faces.size()); | ||
| // 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])); | ||
| logger::instance()->d(__FILENAME__, __LINE__, "%lld", logger::get_time_tick() - start); | ||
|
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| // 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; | ||
| } | ||
| } | ||
|
|
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