mirror of https://github.com/davisking/dlib.git
89 lines
3.4 KiB
C++
89 lines
3.4 KiB
C++
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// 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 is an example illustrating the use of the extract_fhog_features() routine from
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the dlib C++ Library.
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The extract_fhog_features() routine performs the style of HOG feature extraction
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described in the paper:
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Object Detection with Discriminatively Trained Part Based Models by
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P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
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IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, Sep. 2010
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This means that it takes an input image and outputs Felzenszwalb's
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31 dimensional version of HOG features. We show it's use below.
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*/
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#include <dlib/gui_widgets.h>
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#include <dlib/image_io.h>
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#include <dlib/image_transforms.h>
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using namespace std;
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using namespace dlib;
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// ----------------------------------------------------------------------------
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int main(int argc, char** argv)
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{
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try
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{
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// Make sure the user entered an argument to this program. It should be the
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// filename for an image.
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if (argc != 2)
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{
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cout << "error, you have to enter a BMP file as an argument to this program" << endl;
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return 1;
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}
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// Here we declare an image object that can store color rgb_pixels.
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array2d<rgb_pixel> img;
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// Now load the image file into our image. If something is wrong then
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// load_image() will throw an exception. Also, if you linked with libpng
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// and libjpeg then load_image() can load PNG and JPEG files in addition
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// to BMP files.
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load_image(img, argv[1]);
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// Now convert the image into a FHOG feature image. The output, hog, is a 2D array
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// of 31 dimensional vectors.
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array2d<matrix<float,31,1> > hog;
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extract_fhog_features(img, hog);
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cout << "hog image has " << hog.nr() << " rows and " << hog.nc() << " columns." << endl;
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// Lets see what the image and also the FHOG features look like.
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image_window win(img);
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image_window winhog(draw_fhog(hog));
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// Another thing you might want to do is map between the pixels in img and the
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// cells in the hog image. dlib provides the image_to_fhog() and fhog_to_image()
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// routines for this. Their use is demonstrated in the following loop which
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// responds to the user clicking on pixels in the image img.
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point p; // A 2D point, used to represent pixel locations.
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while (win.get_next_double_click(p))
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{
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point hp = image_to_fhog(p);
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cout << "The point " << p << " in the input image corresponds to " << hp << " in hog space." << endl;
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cout << "FHOG features at this point: " << trans(hog[hp.y()][hp.x()]) << endl;
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}
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// Finally, sometimes you want to get a "planar" representation of the HOG features
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// rather than the explicit vector (i.e. interlaced) representation used above.
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dlib::array<array2d<float> > planar_hog;
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extract_fhog_features(img, planar_hog);
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// Now we have an array of 31 float valued images, each representing one of the
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// dimensions of the HOG feature vector.
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}
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catch (exception& e)
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{
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cout << "exception thrown: " << e.what() << endl;
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}
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}
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// ----------------------------------------------------------------------------
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