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| -rw-r--r-- | ml/dlib/examples/face_detection_ex.cpp | 103 |
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diff --git a/ml/dlib/examples/face_detection_ex.cpp b/ml/dlib/examples/face_detection_ex.cpp new file mode 100644 index 000000000..9569d44e9 --- /dev/null +++ b/ml/dlib/examples/face_detection_ex.cpp @@ -0,0 +1,103 @@ +// 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. In + particular, this program shows how you can take a list of images from the + command line and display each on the screen with red boxes overlaid on each + human face. + + The examples/faces folder contains some jpg images of people. You can run + this program on them and see the detections by executing the following command: + ./face_detection_ex faces/*.jpg + + + This face detector is made using the now classic Histogram of Oriented + Gradients (HOG) feature combined with a linear classifier, an image pyramid, + and sliding window detection scheme. This type of object detector is fairly + general and capable of detecting many types of semi-rigid objects in + addition to human faces. Therefore, if you are interested in making your + own object detectors then read the fhog_object_detector_ex.cpp example + program. It shows how to use the machine learning tools which were used to + create dlib's face detector. + + + 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/image_processing/frontal_face_detector.h> +#include <dlib/gui_widgets.h> +#include <dlib/image_io.h> +#include <iostream> + +using namespace dlib; +using namespace std; + +// ---------------------------------------------------------------------------------------- + +int main(int argc, char** argv) +{ + try + { + if (argc == 1) + { + cout << "Give some image files as arguments to this program." << endl; + return 0; + } + + frontal_face_detector detector = get_frontal_face_detector(); + image_window win; + + // Loop over all the images provided on the command line. + for (int i = 1; i < argc; ++i) + { + cout << "processing image " << argv[i] << endl; + array2d<unsigned char> img; + load_image(img, argv[i]); + // Make the image bigger by a factor of two. This is useful since + // the face detector looks for faces that are about 80 by 80 pixels + // or larger. Therefore, if you want to find faces that are smaller + // than that then you need to upsample the image as we do here by + // calling pyramid_up(). So this will allow it to detect faces that + // are at least 40 by 40 pixels in size. We could call pyramid_up() + // again to find even smaller faces, but note that every time we + // upsample the image we make the detector run slower since it must + // process a larger image. + pyramid_up(img); + + // Now tell the face detector to give us a list of bounding boxes + // around all the faces it can find in the image. + std::vector<rectangle> dets = detector(img); + + cout << "Number of faces detected: " << dets.size() << endl; + // Now we show the image on the screen and the face detections as + // red overlay boxes. + win.clear_overlay(); + win.set_image(img); + win.add_overlay(dets, rgb_pixel(255,0,0)); + + cout << "Hit enter to process the next image..." << endl; + cin.get(); + } + } + catch (exception& e) + { + cout << "\nexception thrown!" << endl; + cout << e.what() << endl; + } +} + +// ---------------------------------------------------------------------------------------- + |