mirror of
https://github.com/opencv/opencv.git
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Move objdetect HaarCascadeClassifier and HOGDescriptor to contrib xobjdetect (#25198)
* Move objdetect parts to contrib * Move objdetect parts to contrib * Minor fixes.
This commit is contained in:
WU Jia
GitHub
@@ -1,111 +0,0 @@
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#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(ANDROID) || (defined(_MSC_VER) && _MSC_VER>=1800)
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#include <opencv2/imgproc.hpp> // Gaussian Blur
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#include <opencv2/core.hpp> // Basic OpenCV structures (cv::Mat, Scalar)
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp> // OpenCV window I/O
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#include <opencv2/features2d.hpp>
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#include <opencv2/objdetect.hpp>
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#include <stdio.h>
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using namespace std;
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using namespace cv;
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const string WindowName = "Face Detection example";
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class CascadeDetectorAdapter: public DetectionBasedTracker::IDetector
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{
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public:
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CascadeDetectorAdapter(cv::Ptr<cv::CascadeClassifier> detector):
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IDetector(),
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Detector(detector)
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{
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CV_Assert(detector);
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}
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void detect(const cv::Mat &Image, std::vector<cv::Rect> &objects) CV_OVERRIDE
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{
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Detector->detectMultiScale(Image, objects, scaleFactor, minNeighbours, 0, minObjSize, maxObjSize);
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}
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virtual ~CascadeDetectorAdapter() CV_OVERRIDE
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{}
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private:
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CascadeDetectorAdapter();
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cv::Ptr<cv::CascadeClassifier> Detector;
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};
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int main(int , char** )
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{
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namedWindow(WindowName);
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VideoCapture VideoStream(0);
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if (!VideoStream.isOpened())
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{
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printf("Error: Cannot open video stream from camera\n");
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return 1;
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}
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std::string cascadeFrontalfilename = samples::findFile("data/lbpcascades/lbpcascade_frontalface.xml");
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cv::Ptr<cv::CascadeClassifier> cascade = makePtr<cv::CascadeClassifier>(cascadeFrontalfilename);
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cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = makePtr<CascadeDetectorAdapter>(cascade);
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if ( cascade->empty() )
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{
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printf("Error: Cannot load %s\n", cascadeFrontalfilename.c_str());
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return 2;
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}
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cascade = makePtr<cv::CascadeClassifier>(cascadeFrontalfilename);
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cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = makePtr<CascadeDetectorAdapter>(cascade);
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if ( cascade->empty() )
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{
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printf("Error: Cannot load %s\n", cascadeFrontalfilename.c_str());
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return 2;
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}
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DetectionBasedTracker::Parameters params;
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DetectionBasedTracker Detector(MainDetector, TrackingDetector, params);
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if (!Detector.run())
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{
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printf("Error: Detector initialization failed\n");
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return 2;
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}
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Mat ReferenceFrame;
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Mat GrayFrame;
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vector<Rect> Faces;
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do
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{
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VideoStream >> ReferenceFrame;
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cvtColor(ReferenceFrame, GrayFrame, COLOR_BGR2GRAY);
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Detector.process(GrayFrame);
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Detector.getObjects(Faces);
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for (size_t i = 0; i < Faces.size(); i++)
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{
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rectangle(ReferenceFrame, Faces[i], Scalar(0,255,0));
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}
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imshow(WindowName, ReferenceFrame);
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} while (waitKey(30) < 0);
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Detector.stop();
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return 0;
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}
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#else
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#include <stdio.h>
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int main()
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{
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printf("This sample works for UNIX or ANDROID or Visual Studio 2013+ only\n");
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return 0;
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}
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#endif
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@@ -1,257 +0,0 @@
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#include "opencv2/objdetect.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/videoio.hpp"
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#include <iostream>
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using namespace std;
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using namespace cv;
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static void help(const char** argv)
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{
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cout << "\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n"
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"This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n"
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"It's most known use is for faces.\n"
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"Usage:\n"
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<< argv[0]
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<< " [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"
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" [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n"
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" [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n"
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" [--try-flip]\n"
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" [filename|camera_index]\n\n"
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"example:\n"
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<< argv[0]
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<< " --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n"
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"During execution:\n\tHit any key to quit.\n"
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"\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
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}
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void detectAndDraw( Mat& img, CascadeClassifier& cascade,
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CascadeClassifier& nestedCascade,
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double scale, bool tryflip );
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string cascadeName;
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string nestedCascadeName;
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int main( int argc, const char** argv )
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{
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VideoCapture capture;
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Mat frame, image;
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string inputName;
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bool tryflip;
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CascadeClassifier cascade, nestedCascade;
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double scale;
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cv::CommandLineParser parser(argc, argv,
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"{help h||}"
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"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
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"{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
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"{scale|1|}{try-flip||}{@filename||}"
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);
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if (parser.has("help"))
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{
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help(argv);
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return 0;
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}
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cascadeName = parser.get<string>("cascade");
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nestedCascadeName = parser.get<string>("nested-cascade");
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scale = parser.get<double>("scale");
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if (scale < 1)
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scale = 1;
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tryflip = parser.has("try-flip");
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inputName = parser.get<string>("@filename");
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if (!parser.check())
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{
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parser.printErrors();
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return 0;
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}
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if (!nestedCascade.load(samples::findFileOrKeep(nestedCascadeName)))
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cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
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if (!cascade.load(samples::findFile(cascadeName)))
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{
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cerr << "ERROR: Could not load classifier cascade" << endl;
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help(argv);
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return -1;
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}
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if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) )
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{
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int camera = inputName.empty() ? 0 : inputName[0] - '0';
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if(!capture.open(camera))
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{
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cout << "Capture from camera #" << camera << " didn't work" << endl;
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return 1;
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}
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}
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else if (!inputName.empty())
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{
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image = imread(samples::findFileOrKeep(inputName), IMREAD_COLOR);
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if (image.empty())
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{
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if (!capture.open(samples::findFileOrKeep(inputName)))
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{
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cout << "Could not read " << inputName << endl;
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return 1;
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}
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}
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}
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else
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{
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image = imread(samples::findFile("lena.jpg"), IMREAD_COLOR);
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if (image.empty())
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{
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cout << "Couldn't read lena.jpg" << endl;
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return 1;
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}
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}
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if( capture.isOpened() )
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{
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cout << "Video capturing has been started ..." << endl;
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for(;;)
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{
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capture >> frame;
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if( frame.empty() )
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break;
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Mat frame1 = frame.clone();
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detectAndDraw( frame1, cascade, nestedCascade, scale, tryflip );
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char c = (char)waitKey(10);
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if( c == 27 || c == 'q' || c == 'Q' )
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break;
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}
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}
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else
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{
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cout << "Detecting face(s) in " << inputName << endl;
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if( !image.empty() )
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{
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detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
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waitKey(0);
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}
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else if( !inputName.empty() )
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{
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/* assume it is a text file containing the
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list of the image filenames to be processed - one per line */
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FILE* f = fopen( inputName.c_str(), "rt" );
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if( f )
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{
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char buf[1000+1];
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while( fgets( buf, 1000, f ) )
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{
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int len = (int)strlen(buf);
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while( len > 0 && isspace(buf[len-1]) )
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len--;
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buf[len] = '\0';
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cout << "file " << buf << endl;
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image = imread( buf, IMREAD_COLOR );
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if( !image.empty() )
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{
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detectAndDraw( image, cascade, nestedCascade, scale, tryflip );
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char c = (char)waitKey(0);
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if( c == 27 || c == 'q' || c == 'Q' )
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break;
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}
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else
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{
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cerr << "Aw snap, couldn't read image " << buf << endl;
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}
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}
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fclose(f);
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}
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}
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}
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return 0;
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}
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void detectAndDraw( Mat& img, CascadeClassifier& cascade,
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CascadeClassifier& nestedCascade,
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double scale, bool tryflip )
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{
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double t = 0;
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vector<Rect> faces, faces2;
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const static Scalar colors[] =
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{
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Scalar(255,0,0),
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Scalar(255,128,0),
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Scalar(255,255,0),
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Scalar(0,255,0),
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Scalar(0,128,255),
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Scalar(0,255,255),
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Scalar(0,0,255),
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Scalar(255,0,255)
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};
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Mat gray, smallImg;
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cvtColor( img, gray, COLOR_BGR2GRAY );
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double fx = 1 / scale;
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resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT );
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equalizeHist( smallImg, smallImg );
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t = (double)getTickCount();
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cascade.detectMultiScale( smallImg, faces,
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1.1, 2, 0
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//|CASCADE_FIND_BIGGEST_OBJECT
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//|CASCADE_DO_ROUGH_SEARCH
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|CASCADE_SCALE_IMAGE,
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Size(30, 30) );
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if( tryflip )
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{
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flip(smallImg, smallImg, 1);
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cascade.detectMultiScale( smallImg, faces2,
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1.1, 2, 0
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//|CASCADE_FIND_BIGGEST_OBJECT
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//|CASCADE_DO_ROUGH_SEARCH
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|CASCADE_SCALE_IMAGE,
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Size(30, 30) );
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for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r )
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{
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faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
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}
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}
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t = (double)getTickCount() - t;
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printf( "detection time = %g ms\n", t*1000/getTickFrequency());
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for ( size_t i = 0; i < faces.size(); i++ )
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{
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Rect r = faces[i];
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Mat smallImgROI;
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vector<Rect> nestedObjects;
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Point center;
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Scalar color = colors[i%8];
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int radius;
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double aspect_ratio = (double)r.width/r.height;
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if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
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{
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center.x = cvRound((r.x + r.width*0.5)*scale);
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center.y = cvRound((r.y + r.height*0.5)*scale);
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radius = cvRound((r.width + r.height)*0.25*scale);
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circle( img, center, radius, color, 3, 8, 0 );
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}
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else
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rectangle( img, Point(cvRound(r.x*scale), cvRound(r.y*scale)),
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Point(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)),
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color, 3, 8, 0);
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if( nestedCascade.empty() )
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continue;
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smallImgROI = smallImg( r );
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nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
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1.1, 2, 0
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//|CASCADE_FIND_BIGGEST_OBJECT
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//|CASCADE_DO_ROUGH_SEARCH
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//|CASCADE_DO_CANNY_PRUNING
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|CASCADE_SCALE_IMAGE,
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Size(30, 30) );
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for ( size_t j = 0; j < nestedObjects.size(); j++ )
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{
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Rect nr = nestedObjects[j];
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center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale);
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center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale);
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radius = cvRound((nr.width + nr.height)*0.25*scale);
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circle( img, center, radius, color, 3, 8, 0 );
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}
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}
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imshow( "result", img );
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}
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@@ -1,215 +0,0 @@
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/*
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* Author: Samyak Datta (datta[dot]samyak[at]gmail.com)
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*
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* A program to detect facial feature points using
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* Haarcascade classifiers for face, eyes, nose and mouth
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*
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*/
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#include "opencv2/objdetect.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <cstdio>
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#include <vector>
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#include <algorithm>
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using namespace std;
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using namespace cv;
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// Functions for facial feature detection
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static void help(char** argv);
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static void detectFaces(Mat&, vector<Rect_<int> >&, string);
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static void detectEyes(Mat&, vector<Rect_<int> >&, string);
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static void detectNose(Mat&, vector<Rect_<int> >&, string);
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static void detectMouth(Mat&, vector<Rect_<int> >&, string);
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static void detectFacialFeaures(Mat&, const vector<Rect_<int> >, string, string, string);
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string input_image_path;
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string face_cascade_path, eye_cascade_path, nose_cascade_path, mouth_cascade_path;
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int main(int argc, char** argv)
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{
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cv::CommandLineParser parser(argc, argv,
|
||||
"{eyes||}{nose||}{mouth||}{help h||}{@image||}{@facexml||}");
|
||||
if (parser.has("help"))
|
||||
{
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help(argv);
|
||||
return 0;
|
||||
}
|
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input_image_path = parser.get<string>("@image");
|
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face_cascade_path = parser.get<string>("@facexml");
|
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eye_cascade_path = parser.has("eyes") ? parser.get<string>("eyes") : "";
|
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nose_cascade_path = parser.has("nose") ? parser.get<string>("nose") : "";
|
||||
mouth_cascade_path = parser.has("mouth") ? parser.get<string>("mouth") : "";
|
||||
if (input_image_path.empty() || face_cascade_path.empty())
|
||||
{
|
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cout << "IMAGE or FACE_CASCADE are not specified";
|
||||
return 1;
|
||||
}
|
||||
// Load image and cascade classifier files
|
||||
Mat image;
|
||||
image = imread(samples::findFile(input_image_path));
|
||||
|
||||
// Detect faces and facial features
|
||||
vector<Rect_<int> > faces;
|
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detectFaces(image, faces, face_cascade_path);
|
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detectFacialFeaures(image, faces, eye_cascade_path, nose_cascade_path, mouth_cascade_path);
|
||||
|
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imshow("Result", image);
|
||||
|
||||
waitKey(0);
|
||||
return 0;
|
||||
}
|
||||
|
||||
static void help(char** argv)
|
||||
{
|
||||
cout << "\nThis file demonstrates facial feature points detection using Haarcascade classifiers.\n"
|
||||
"The program detects a face and eyes, nose and mouth inside the face."
|
||||
"The code has been tested on the Japanese Female Facial Expression (JAFFE) database and found"
|
||||
"to give reasonably accurate results. \n";
|
||||
|
||||
cout << "\nUSAGE: " << argv[0] << " [IMAGE] [FACE_CASCADE] [OPTIONS]\n"
|
||||
"IMAGE\n\tPath to the image of a face taken as input.\n"
|
||||
"FACE_CASCSDE\n\t Path to a haarcascade classifier for face detection.\n"
|
||||
"OPTIONS: \nThere are 3 options available which are described in detail. There must be a "
|
||||
"space between the option and it's argument (All three options accept arguments).\n"
|
||||
"\t-eyes=<eyes_cascade> : Specify the haarcascade classifier for eye detection.\n"
|
||||
"\t-nose=<nose_cascade> : Specify the haarcascade classifier for nose detection.\n"
|
||||
"\t-mouth=<mouth-cascade> : Specify the haarcascade classifier for mouth detection.\n";
|
||||
|
||||
|
||||
cout << "EXAMPLE:\n"
|
||||
"(1) " << argv[0] << " image.jpg face.xml -eyes=eyes.xml -mouth=mouth.xml\n"
|
||||
"\tThis will detect the face, eyes and mouth in image.jpg.\n"
|
||||
"(2) " << argv[0] << " image.jpg face.xml -nose=nose.xml\n"
|
||||
"\tThis will detect the face and nose in image.jpg.\n"
|
||||
"(3) " << argv[0] << " image.jpg face.xml\n"
|
||||
"\tThis will detect only the face in image.jpg.\n";
|
||||
|
||||
cout << " \n\nThe classifiers for face and eyes can be downloaded from : "
|
||||
" \nhttps://github.com/opencv/opencv/tree/5.x/data/haarcascades";
|
||||
|
||||
cout << "\n\nThe classifiers for nose and mouth can be downloaded from : "
|
||||
" \nhttps://github.com/opencv/opencv_contrib/tree/5.x/modules/face/data/cascades\n";
|
||||
}
|
||||
|
||||
static void detectFaces(Mat& img, vector<Rect_<int> >& faces, string cascade_path)
|
||||
{
|
||||
CascadeClassifier face_cascade;
|
||||
face_cascade.load(samples::findFile(cascade_path));
|
||||
|
||||
if (!face_cascade.empty())
|
||||
face_cascade.detectMultiScale(img, faces, 1.15, 3, 0|CASCADE_SCALE_IMAGE, Size(30, 30));
|
||||
return;
|
||||
}
|
||||
|
||||
static void detectFacialFeaures(Mat& img, const vector<Rect_<int> > faces, string eye_cascade,
|
||||
string nose_cascade, string mouth_cascade)
|
||||
{
|
||||
for(unsigned int i = 0; i < faces.size(); ++i)
|
||||
{
|
||||
// Mark the bounding box enclosing the face
|
||||
Rect face = faces[i];
|
||||
rectangle(img, Point(face.x, face.y), Point(face.x+face.width, face.y+face.height),
|
||||
Scalar(255, 0, 0), 1, 4);
|
||||
|
||||
// Eyes, nose and mouth will be detected inside the face (region of interest)
|
||||
Mat ROI = img(Rect(face.x, face.y, face.width, face.height));
|
||||
|
||||
// Check if all features (eyes, nose and mouth) are being detected
|
||||
bool is_full_detection = false;
|
||||
if( (!eye_cascade.empty()) && (!nose_cascade.empty()) && (!mouth_cascade.empty()) )
|
||||
is_full_detection = true;
|
||||
|
||||
// Detect eyes if classifier provided by the user
|
||||
if(!eye_cascade.empty())
|
||||
{
|
||||
vector<Rect_<int> > eyes;
|
||||
detectEyes(ROI, eyes, eye_cascade);
|
||||
|
||||
// Mark points corresponding to the centre of the eyes
|
||||
for(unsigned int j = 0; j < eyes.size(); ++j)
|
||||
{
|
||||
Rect e = eyes[j];
|
||||
circle(ROI, Point(e.x+e.width/2, e.y+e.height/2), 3, Scalar(0, 255, 0), -1, 8);
|
||||
/* rectangle(ROI, Point(e.x, e.y), Point(e.x+e.width, e.y+e.height),
|
||||
Scalar(0, 255, 0), 1, 4); */
|
||||
}
|
||||
}
|
||||
|
||||
// Detect nose if classifier provided by the user
|
||||
double nose_center_height = 0.0;
|
||||
if(!nose_cascade.empty())
|
||||
{
|
||||
vector<Rect_<int> > nose;
|
||||
detectNose(ROI, nose, nose_cascade);
|
||||
|
||||
// Mark points corresponding to the centre (tip) of the nose
|
||||
for(unsigned int j = 0; j < nose.size(); ++j)
|
||||
{
|
||||
Rect n = nose[j];
|
||||
circle(ROI, Point(n.x+n.width/2, n.y+n.height/2), 3, Scalar(0, 255, 0), -1, 8);
|
||||
nose_center_height = (n.y + n.height/2);
|
||||
}
|
||||
}
|
||||
|
||||
// Detect mouth if classifier provided by the user
|
||||
double mouth_center_height = 0.0;
|
||||
if(!mouth_cascade.empty())
|
||||
{
|
||||
vector<Rect_<int> > mouth;
|
||||
detectMouth(ROI, mouth, mouth_cascade);
|
||||
|
||||
for(unsigned int j = 0; j < mouth.size(); ++j)
|
||||
{
|
||||
Rect m = mouth[j];
|
||||
mouth_center_height = (m.y + m.height/2);
|
||||
|
||||
// The mouth should lie below the nose
|
||||
if( (is_full_detection) && (mouth_center_height > nose_center_height) )
|
||||
{
|
||||
rectangle(ROI, Point(m.x, m.y), Point(m.x+m.width, m.y+m.height), Scalar(0, 255, 0), 1, 4);
|
||||
}
|
||||
else if( (is_full_detection) && (mouth_center_height <= nose_center_height) )
|
||||
continue;
|
||||
else
|
||||
rectangle(ROI, Point(m.x, m.y), Point(m.x+m.width, m.y+m.height), Scalar(0, 255, 0), 1, 4);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
static void detectEyes(Mat& img, vector<Rect_<int> >& eyes, string cascade_path)
|
||||
{
|
||||
CascadeClassifier eyes_cascade;
|
||||
eyes_cascade.load(samples::findFile(cascade_path, !cascade_path.empty()));
|
||||
|
||||
if (!eyes_cascade.empty())
|
||||
eyes_cascade.detectMultiScale(img, eyes, 1.20, 5, 0|CASCADE_SCALE_IMAGE, Size(30, 30));
|
||||
return;
|
||||
}
|
||||
|
||||
static void detectNose(Mat& img, vector<Rect_<int> >& nose, string cascade_path)
|
||||
{
|
||||
CascadeClassifier nose_cascade;
|
||||
nose_cascade.load(samples::findFile(cascade_path, !cascade_path.empty()));
|
||||
|
||||
if (!nose_cascade.empty())
|
||||
nose_cascade.detectMultiScale(img, nose, 1.20, 5, 0|CASCADE_SCALE_IMAGE, Size(30, 30));
|
||||
return;
|
||||
}
|
||||
|
||||
static void detectMouth(Mat& img, vector<Rect_<int> >& mouth, string cascade_path)
|
||||
{
|
||||
CascadeClassifier mouth_cascade;
|
||||
mouth_cascade.load(samples::findFile(cascade_path, !cascade_path.empty()));
|
||||
|
||||
if (!mouth_cascade.empty())
|
||||
mouth_cascade.detectMultiScale(img, mouth, 1.20, 5, 0|CASCADE_SCALE_IMAGE, Size(30, 30));
|
||||
return;
|
||||
}
|
||||
@@ -1,129 +0,0 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html
|
||||
|
||||
#include <opencv2/objdetect.hpp>
|
||||
#include <opencv2/highgui.hpp>
|
||||
#include <opencv2/imgproc.hpp>
|
||||
#include <opencv2/videoio.hpp>
|
||||
#include <iostream>
|
||||
#include <iomanip>
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
|
||||
class Detector
|
||||
{
|
||||
enum Mode { Default, Daimler } m;
|
||||
HOGDescriptor hog, hog_d;
|
||||
public:
|
||||
Detector() : m(Default), hog(), hog_d(Size(48, 96), Size(16, 16), Size(8, 8), Size(8, 8), 9)
|
||||
{
|
||||
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
|
||||
hog_d.setSVMDetector(HOGDescriptor::getDaimlerPeopleDetector());
|
||||
}
|
||||
void toggleMode() { m = (m == Default ? Daimler : Default); }
|
||||
string modeName() const { return (m == Default ? "Default" : "Daimler"); }
|
||||
vector<Rect> detect(InputArray img)
|
||||
{
|
||||
// Run the detector with default parameters. to get a higher hit-rate
|
||||
// (and more false alarms, respectively), decrease the hitThreshold and
|
||||
// groupThreshold (set groupThreshold to 0 to turn off the grouping completely).
|
||||
vector<Rect> found;
|
||||
if (m == Default)
|
||||
hog.detectMultiScale(img, found, 0, Size(8,8), Size(), 1.05, 2, false);
|
||||
else if (m == Daimler)
|
||||
hog_d.detectMultiScale(img, found, 0, Size(8,8), Size(), 1.05, 2, true);
|
||||
return found;
|
||||
}
|
||||
void adjustRect(Rect & r) const
|
||||
{
|
||||
// The HOG detector returns slightly larger rectangles than the real objects,
|
||||
// so we slightly shrink the rectangles to get a nicer output.
|
||||
r.x += cvRound(r.width*0.1);
|
||||
r.width = cvRound(r.width*0.8);
|
||||
r.y += cvRound(r.height*0.07);
|
||||
r.height = cvRound(r.height*0.8);
|
||||
}
|
||||
};
|
||||
|
||||
static const string keys = "{ help h | | print help message }"
|
||||
"{ camera c | 0 | capture video from camera (device index starting from 0) }"
|
||||
"{ video v | | use video as input }";
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
CommandLineParser parser(argc, argv, keys);
|
||||
parser.about("This sample demonstrates the use of the HoG descriptor.");
|
||||
if (parser.has("help"))
|
||||
{
|
||||
parser.printMessage();
|
||||
return 0;
|
||||
}
|
||||
int camera = parser.get<int>("camera");
|
||||
string file = parser.get<string>("video");
|
||||
if (!parser.check())
|
||||
{
|
||||
parser.printErrors();
|
||||
return 1;
|
||||
}
|
||||
|
||||
VideoCapture cap;
|
||||
if (file.empty())
|
||||
cap.open(camera);
|
||||
else
|
||||
{
|
||||
file = samples::findFileOrKeep(file);
|
||||
cap.open(file);
|
||||
}
|
||||
if (!cap.isOpened())
|
||||
{
|
||||
cout << "Can not open video stream: '" << (file.empty() ? "<camera>" : file) << "'" << endl;
|
||||
return 2;
|
||||
}
|
||||
|
||||
cout << "Press 'q' or <ESC> to quit." << endl;
|
||||
cout << "Press <space> to toggle between Default and Daimler detector" << endl;
|
||||
Detector detector;
|
||||
Mat frame;
|
||||
for (;;)
|
||||
{
|
||||
cap >> frame;
|
||||
if (frame.empty())
|
||||
{
|
||||
cout << "Finished reading: empty frame" << endl;
|
||||
break;
|
||||
}
|
||||
int64 t = getTickCount();
|
||||
vector<Rect> found = detector.detect(frame);
|
||||
t = getTickCount() - t;
|
||||
|
||||
// show the window
|
||||
{
|
||||
ostringstream buf;
|
||||
buf << "Mode: " << detector.modeName() << " ||| "
|
||||
<< "FPS: " << fixed << setprecision(1) << (getTickFrequency() / (double)t);
|
||||
putText(frame, buf.str(), Point(10, 30), FONT_HERSHEY_PLAIN, 2.0, Scalar(0, 0, 255), 2, LINE_AA);
|
||||
}
|
||||
for (vector<Rect>::iterator i = found.begin(); i != found.end(); ++i)
|
||||
{
|
||||
Rect &r = *i;
|
||||
detector.adjustRect(r);
|
||||
rectangle(frame, r.tl(), r.br(), cv::Scalar(0, 255, 0), 2);
|
||||
}
|
||||
imshow("People detector", frame);
|
||||
|
||||
// interact with user
|
||||
const char key = (char)waitKey(1);
|
||||
if (key == 27 || key == 'q') // ESC
|
||||
{
|
||||
cout << "Exit requested" << endl;
|
||||
break;
|
||||
}
|
||||
else if (key == ' ')
|
||||
{
|
||||
detector.toggleMode();
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
@@ -1,215 +0,0 @@
|
||||
#include "opencv2/objdetect.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/videoio.hpp"
|
||||
#include <iostream>
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
static void help(const char** argv)
|
||||
{
|
||||
cout << "\nThis program demonstrates the smile detector.\n"
|
||||
"Usage:\n" <<
|
||||
argv[0] << " [--cascade=<cascade_path> this is the frontal face classifier]\n"
|
||||
" [--smile-cascade=[<smile_cascade_path>]]\n"
|
||||
" [--scale=<image scale greater or equal to 1, try 2.0 for example. The larger the faster the processing>]\n"
|
||||
" [--try-flip]\n"
|
||||
" [video_filename|camera_index]\n\n"
|
||||
"Example:\n" <<
|
||||
argv[0] << " --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --smile-cascade=\"data/haarcascades/haarcascade_smile.xml\" --scale=2.0\n\n"
|
||||
"During execution:\n\tHit any key to quit.\n"
|
||||
"\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
|
||||
}
|
||||
|
||||
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
||||
CascadeClassifier& nestedCascade,
|
||||
double scale, bool tryflip );
|
||||
|
||||
string cascadeName;
|
||||
string nestedCascadeName;
|
||||
|
||||
int main( int argc, const char** argv )
|
||||
{
|
||||
VideoCapture capture;
|
||||
Mat frame, image;
|
||||
string inputName;
|
||||
bool tryflip;
|
||||
|
||||
help(argv);
|
||||
|
||||
CascadeClassifier cascade, nestedCascade;
|
||||
double scale;
|
||||
cv::CommandLineParser parser(argc, argv,
|
||||
"{help h||}{scale|1|}"
|
||||
"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
|
||||
"{smile-cascade|data/haarcascades/haarcascade_smile.xml|}"
|
||||
"{try-flip||}{@input||}");
|
||||
if (parser.has("help"))
|
||||
{
|
||||
help(argv);
|
||||
return 0;
|
||||
}
|
||||
cascadeName = samples::findFile(parser.get<string>("cascade"));
|
||||
nestedCascadeName = samples::findFile(parser.get<string>("smile-cascade"));
|
||||
tryflip = parser.has("try-flip");
|
||||
inputName = parser.get<string>("@input");
|
||||
scale = parser.get<int>("scale");
|
||||
if (!parser.check())
|
||||
{
|
||||
help(argv);
|
||||
return 1;
|
||||
}
|
||||
if (scale < 1)
|
||||
scale = 1;
|
||||
if( !cascade.load( cascadeName ) )
|
||||
{
|
||||
cerr << "ERROR: Could not load face cascade" << endl;
|
||||
help(argv);
|
||||
return -1;
|
||||
}
|
||||
if( !nestedCascade.load( nestedCascadeName ) )
|
||||
{
|
||||
cerr << "ERROR: Could not load smile cascade" << endl;
|
||||
help(argv);
|
||||
return -1;
|
||||
}
|
||||
if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) )
|
||||
{
|
||||
int c = inputName.empty() ? 0 : inputName[0] - '0' ;
|
||||
if(!capture.open(c))
|
||||
cout << "Capture from camera #" << c << " didn't work" << endl;
|
||||
}
|
||||
else if( inputName.size() )
|
||||
{
|
||||
inputName = samples::findFileOrKeep(inputName);
|
||||
if(!capture.open( inputName ))
|
||||
cout << "Could not read " << inputName << endl;
|
||||
}
|
||||
|
||||
if( capture.isOpened() )
|
||||
{
|
||||
cout << "Video capturing has been started ..." << endl;
|
||||
cout << endl << "NOTE: Smile intensity will only be valid after a first smile has been detected" << endl;
|
||||
|
||||
for(;;)
|
||||
{
|
||||
capture >> frame;
|
||||
if( frame.empty() )
|
||||
break;
|
||||
|
||||
Mat frame1 = frame.clone();
|
||||
detectAndDraw( frame1, cascade, nestedCascade, scale, tryflip );
|
||||
|
||||
char c = (char)waitKey(10);
|
||||
if( c == 27 || c == 'q' || c == 'Q' )
|
||||
break;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
cerr << "ERROR: Could not initiate capture" << endl;
|
||||
help(argv);
|
||||
return -1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
||||
CascadeClassifier& nestedCascade,
|
||||
double scale, bool tryflip)
|
||||
{
|
||||
vector<Rect> faces, faces2;
|
||||
const static Scalar colors[] =
|
||||
{
|
||||
Scalar(255,0,0),
|
||||
Scalar(255,128,0),
|
||||
Scalar(255,255,0),
|
||||
Scalar(0,255,0),
|
||||
Scalar(0,128,255),
|
||||
Scalar(0,255,255),
|
||||
Scalar(0,0,255),
|
||||
Scalar(255,0,255)
|
||||
};
|
||||
Mat gray, smallImg;
|
||||
|
||||
cvtColor( img, gray, COLOR_BGR2GRAY );
|
||||
|
||||
double fx = 1 / scale;
|
||||
resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT );
|
||||
equalizeHist( smallImg, smallImg );
|
||||
|
||||
cascade.detectMultiScale( smallImg, faces,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
|CASCADE_SCALE_IMAGE,
|
||||
Size(30, 30) );
|
||||
if( tryflip )
|
||||
{
|
||||
flip(smallImg, smallImg, 1);
|
||||
cascade.detectMultiScale( smallImg, faces2,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
|CASCADE_SCALE_IMAGE,
|
||||
Size(30, 30) );
|
||||
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r )
|
||||
{
|
||||
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
|
||||
}
|
||||
}
|
||||
|
||||
for ( size_t i = 0; i < faces.size(); i++ )
|
||||
{
|
||||
Rect r = faces[i];
|
||||
Mat smallImgROI;
|
||||
vector<Rect> nestedObjects;
|
||||
Point center;
|
||||
Scalar color = colors[i%8];
|
||||
int radius;
|
||||
|
||||
double aspect_ratio = (double)r.width/r.height;
|
||||
if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
|
||||
{
|
||||
center.x = cvRound((r.x + r.width*0.5)*scale);
|
||||
center.y = cvRound((r.y + r.height*0.5)*scale);
|
||||
radius = cvRound((r.width + r.height)*0.25*scale);
|
||||
circle( img, center, radius, color, 3, 8, 0 );
|
||||
}
|
||||
else
|
||||
rectangle( img, Point(cvRound(r.x*scale), cvRound(r.y*scale)),
|
||||
Point(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)),
|
||||
color, 3, 8, 0);
|
||||
|
||||
const int half_height=cvRound((float)r.height/2);
|
||||
r.y=r.y + half_height;
|
||||
r.height = half_height-1;
|
||||
smallImgROI = smallImg( r );
|
||||
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||||
1.1, 0, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
//|CASCADE_DO_CANNY_PRUNING
|
||||
|CASCADE_SCALE_IMAGE,
|
||||
Size(30, 30) );
|
||||
|
||||
// The number of detected neighbors depends on image size (and also illumination, etc.). The
|
||||
// following steps use a floating minimum and maximum of neighbors. Intensity thus estimated will be
|
||||
//accurate only after a first smile has been displayed by the user.
|
||||
const int smile_neighbors = (int)nestedObjects.size();
|
||||
static int max_neighbors=-1;
|
||||
static int min_neighbors=-1;
|
||||
if (min_neighbors == -1) min_neighbors = smile_neighbors;
|
||||
max_neighbors = MAX(max_neighbors, smile_neighbors);
|
||||
|
||||
// Draw rectangle on the left side of the image reflecting smile intensity
|
||||
float intensityZeroOne = ((float)smile_neighbors - min_neighbors) / (max_neighbors - min_neighbors + 1);
|
||||
int rect_height = cvRound((float)img.rows * intensityZeroOne);
|
||||
Scalar col = Scalar((float)255 * intensityZeroOne, 0, 0);
|
||||
rectangle(img, Point(0, img.rows), Point(img.cols/10, img.rows - rect_height), col, -1);
|
||||
}
|
||||
|
||||
imshow( "result", img );
|
||||
}
|
||||
@@ -1,107 +0,0 @@
|
||||
#include "opencv2/objdetect.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/videoio.hpp"
|
||||
#include <iostream>
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
/** Function Headers */
|
||||
void detectAndDisplay( Mat frame );
|
||||
|
||||
/** Global variables */
|
||||
CascadeClassifier face_cascade;
|
||||
CascadeClassifier eyes_cascade;
|
||||
|
||||
/** @function main */
|
||||
int main( int argc, const char** argv )
|
||||
{
|
||||
CommandLineParser parser(argc, argv,
|
||||
"{help h||}"
|
||||
"{face_cascade|data/haarcascades/haarcascade_frontalface_alt.xml|Path to face cascade.}"
|
||||
"{eyes_cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|Path to eyes cascade.}"
|
||||
"{camera|0|Camera device number.}");
|
||||
|
||||
parser.about( "\nThis program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.\n"
|
||||
"You can use Haar or LBP features.\n\n" );
|
||||
parser.printMessage();
|
||||
|
||||
String face_cascade_name = samples::findFile( parser.get<String>("face_cascade") );
|
||||
String eyes_cascade_name = samples::findFile( parser.get<String>("eyes_cascade") );
|
||||
|
||||
//-- 1. Load the cascades
|
||||
if( !face_cascade.load( face_cascade_name ) )
|
||||
{
|
||||
cout << "--(!)Error loading face cascade\n";
|
||||
return -1;
|
||||
};
|
||||
if( !eyes_cascade.load( eyes_cascade_name ) )
|
||||
{
|
||||
cout << "--(!)Error loading eyes cascade\n";
|
||||
return -1;
|
||||
};
|
||||
|
||||
int camera_device = parser.get<int>("camera");
|
||||
VideoCapture capture;
|
||||
//-- 2. Read the video stream
|
||||
capture.open( camera_device );
|
||||
if ( ! capture.isOpened() )
|
||||
{
|
||||
cout << "--(!)Error opening video capture\n";
|
||||
return -1;
|
||||
}
|
||||
|
||||
Mat frame;
|
||||
while ( capture.read(frame) )
|
||||
{
|
||||
if( frame.empty() )
|
||||
{
|
||||
cout << "--(!) No captured frame -- Break!\n";
|
||||
break;
|
||||
}
|
||||
|
||||
//-- 3. Apply the classifier to the frame
|
||||
detectAndDisplay( frame );
|
||||
|
||||
if( waitKey(10) == 27 )
|
||||
{
|
||||
break; // escape
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/** @function detectAndDisplay */
|
||||
void detectAndDisplay( Mat frame )
|
||||
{
|
||||
Mat frame_gray;
|
||||
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
|
||||
equalizeHist( frame_gray, frame_gray );
|
||||
|
||||
//-- Detect faces
|
||||
std::vector<Rect> faces;
|
||||
face_cascade.detectMultiScale( frame_gray, faces );
|
||||
|
||||
for ( size_t i = 0; i < faces.size(); i++ )
|
||||
{
|
||||
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
|
||||
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 255 ), 4 );
|
||||
|
||||
Mat faceROI = frame_gray( faces[i] );
|
||||
|
||||
//-- In each face, detect eyes
|
||||
std::vector<Rect> eyes;
|
||||
eyes_cascade.detectMultiScale( faceROI, eyes );
|
||||
|
||||
for ( size_t j = 0; j < eyes.size(); j++ )
|
||||
{
|
||||
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
|
||||
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
|
||||
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4 );
|
||||
}
|
||||
}
|
||||
|
||||
//-- Show what you got
|
||||
imshow( "Capture - Face detection", frame );
|
||||
}
|
||||
@@ -1,316 +0,0 @@
|
||||
// WARNING: this sample is under construction! Use it on your own risk.
|
||||
#if defined _MSC_VER && _MSC_VER >= 1400
|
||||
#pragma warning(disable : 4100)
|
||||
#endif
|
||||
|
||||
|
||||
#include <iostream>
|
||||
#include <iomanip>
|
||||
#include "opencv2/objdetect.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/cudaobjdetect.hpp"
|
||||
#include "opencv2/cudaimgproc.hpp"
|
||||
#include "opencv2/cudawarping.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::cuda;
|
||||
|
||||
|
||||
static void help()
|
||||
{
|
||||
cout << "Usage: ./cascadeclassifier \n\t--cascade <cascade_file>\n\t(<image>|--video <video>|--camera <camera_id>)\n"
|
||||
"Using OpenCV version " << CV_VERSION << endl << endl;
|
||||
}
|
||||
|
||||
|
||||
static void convertAndResize(const Mat& src, Mat& gray, Mat& resized, double scale)
|
||||
{
|
||||
if (src.channels() == 3)
|
||||
{
|
||||
cv::cvtColor( src, gray, COLOR_BGR2GRAY );
|
||||
}
|
||||
else
|
||||
{
|
||||
gray = src;
|
||||
}
|
||||
|
||||
Size sz(cvRound(gray.cols * scale), cvRound(gray.rows * scale));
|
||||
|
||||
if (scale != 1)
|
||||
{
|
||||
cv::resize(gray, resized, sz);
|
||||
}
|
||||
else
|
||||
{
|
||||
resized = gray;
|
||||
}
|
||||
}
|
||||
|
||||
static void convertAndResize(const GpuMat& src, GpuMat& gray, GpuMat& resized, double scale)
|
||||
{
|
||||
if (src.channels() == 3)
|
||||
{
|
||||
cv::cuda::cvtColor( src, gray, COLOR_BGR2GRAY );
|
||||
}
|
||||
else
|
||||
{
|
||||
gray = src;
|
||||
}
|
||||
|
||||
Size sz(cvRound(gray.cols * scale), cvRound(gray.rows * scale));
|
||||
|
||||
if (scale != 1)
|
||||
{
|
||||
cv::cuda::resize(gray, resized, sz);
|
||||
}
|
||||
else
|
||||
{
|
||||
resized = gray;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
static void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
|
||||
{
|
||||
int fontFace = FONT_HERSHEY_DUPLEX;
|
||||
double fontScale = 0.8;
|
||||
int fontThickness = 2;
|
||||
Size fontSize = cv::getTextSize("T[]", fontFace, fontScale, fontThickness, 0);
|
||||
|
||||
Point org;
|
||||
org.x = 1;
|
||||
org.y = 3 * fontSize.height * (lineOffsY + 1) / 2;
|
||||
putText(img, ss, org, fontFace, fontScale, Scalar(0,0,0), 5*fontThickness/2, 16);
|
||||
putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
|
||||
}
|
||||
|
||||
|
||||
static void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
|
||||
{
|
||||
Scalar fontColorRed = Scalar(255,0,0);
|
||||
Scalar fontColorNV = Scalar(118,185,0);
|
||||
|
||||
ostringstream ss;
|
||||
ss << "FPS = " << setprecision(1) << fixed << fps;
|
||||
matPrint(canvas, 0, fontColorRed, ss.str());
|
||||
ss.str("");
|
||||
ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
|
||||
(bGpu ? "GPU, " : "CPU, ") <<
|
||||
(bLargestFace ? "OneFace, " : "MultiFace, ") <<
|
||||
(bFilter ? "Filter:ON" : "Filter:OFF");
|
||||
matPrint(canvas, 1, fontColorRed, ss.str());
|
||||
|
||||
// by Anatoly. MacOS fix. ostringstream(const string&) is a private
|
||||
// matPrint(canvas, 2, fontColorNV, ostringstream("Space - switch GPU / CPU"));
|
||||
if (bHelp)
|
||||
{
|
||||
matPrint(canvas, 2, fontColorNV, "Space - switch GPU / CPU");
|
||||
matPrint(canvas, 3, fontColorNV, "M - switch OneFace / MultiFace");
|
||||
matPrint(canvas, 4, fontColorNV, "F - toggle rectangles Filter");
|
||||
matPrint(canvas, 5, fontColorNV, "H - toggle hotkeys help");
|
||||
matPrint(canvas, 6, fontColorNV, "1/Q - increase/decrease scale");
|
||||
}
|
||||
else
|
||||
{
|
||||
matPrint(canvas, 2, fontColorNV, "H - toggle hotkeys help");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, const char *argv[])
|
||||
{
|
||||
if (argc == 1)
|
||||
{
|
||||
help();
|
||||
return -1;
|
||||
}
|
||||
|
||||
if (getCudaEnabledDeviceCount() == 0)
|
||||
{
|
||||
return cerr << "No GPU found or the library is compiled without CUDA support" << endl, -1;
|
||||
}
|
||||
|
||||
cv::cuda::printShortCudaDeviceInfo(cv::cuda::getDevice());
|
||||
|
||||
string cascadeName;
|
||||
string inputName;
|
||||
bool isInputImage = false;
|
||||
bool isInputVideo = false;
|
||||
bool isInputCamera = false;
|
||||
|
||||
for (int i = 1; i < argc; ++i)
|
||||
{
|
||||
if (string(argv[i]) == "--cascade")
|
||||
cascadeName = argv[++i];
|
||||
else if (string(argv[i]) == "--video")
|
||||
{
|
||||
inputName = argv[++i];
|
||||
isInputVideo = true;
|
||||
}
|
||||
else if (string(argv[i]) == "--camera")
|
||||
{
|
||||
inputName = argv[++i];
|
||||
isInputCamera = true;
|
||||
}
|
||||
else if (string(argv[i]) == "--help")
|
||||
{
|
||||
help();
|
||||
return -1;
|
||||
}
|
||||
else if (!isInputImage)
|
||||
{
|
||||
inputName = argv[i];
|
||||
isInputImage = true;
|
||||
}
|
||||
else
|
||||
{
|
||||
cout << "Unknown key: " << argv[i] << endl;
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(cascadeName);
|
||||
|
||||
cv::CascadeClassifier cascade_cpu;
|
||||
if (!cascade_cpu.load(cascadeName))
|
||||
{
|
||||
return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName << "\"" << endl, help(), -1;
|
||||
}
|
||||
|
||||
VideoCapture capture;
|
||||
Mat image;
|
||||
|
||||
if (isInputImage)
|
||||
{
|
||||
image = imread(inputName);
|
||||
CV_Assert(!image.empty());
|
||||
}
|
||||
else if (isInputVideo)
|
||||
{
|
||||
capture.open(inputName);
|
||||
CV_Assert(capture.isOpened());
|
||||
}
|
||||
else
|
||||
{
|
||||
capture.open(atoi(inputName.c_str()));
|
||||
CV_Assert(capture.isOpened());
|
||||
}
|
||||
|
||||
namedWindow("result", 1);
|
||||
|
||||
Mat frame, frame_cpu, gray_cpu, resized_cpu, frameDisp;
|
||||
vector<Rect> faces;
|
||||
|
||||
GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
|
||||
|
||||
/* parameters */
|
||||
bool useGPU = true;
|
||||
double scaleFactor = 1.0;
|
||||
bool findLargestObject = false;
|
||||
bool filterRects = true;
|
||||
bool helpScreen = false;
|
||||
|
||||
for (;;)
|
||||
{
|
||||
if (isInputCamera || isInputVideo)
|
||||
{
|
||||
capture >> frame;
|
||||
if (frame.empty())
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
(image.empty() ? frame : image).copyTo(frame_cpu);
|
||||
frame_gpu.upload(image.empty() ? frame : image);
|
||||
|
||||
convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
|
||||
convertAndResize(frame_cpu, gray_cpu, resized_cpu, scaleFactor);
|
||||
|
||||
TickMeter tm;
|
||||
tm.start();
|
||||
|
||||
if (useGPU)
|
||||
{
|
||||
cascade_gpu->setFindLargestObject(findLargestObject);
|
||||
cascade_gpu->setScaleFactor(1.2);
|
||||
cascade_gpu->setMinNeighbors((filterRects || findLargestObject) ? 4 : 0);
|
||||
|
||||
cascade_gpu->detectMultiScale(resized_gpu, facesBuf_gpu);
|
||||
cascade_gpu->convert(facesBuf_gpu, faces);
|
||||
}
|
||||
else
|
||||
{
|
||||
Size minSize = cascade_gpu->getClassifierSize();
|
||||
cascade_cpu.detectMultiScale(resized_cpu, faces, 1.2,
|
||||
(filterRects || findLargestObject) ? 4 : 0,
|
||||
(findLargestObject ? CASCADE_FIND_BIGGEST_OBJECT : 0)
|
||||
| CASCADE_SCALE_IMAGE,
|
||||
minSize);
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < faces.size(); ++i)
|
||||
{
|
||||
rectangle(resized_cpu, faces[i], Scalar(255));
|
||||
}
|
||||
|
||||
tm.stop();
|
||||
double detectionTime = tm.getTimeMilli();
|
||||
double fps = 1000 / detectionTime;
|
||||
|
||||
//print detections to console
|
||||
cout << setfill(' ') << setprecision(2);
|
||||
cout << setw(6) << fixed << fps << " FPS, " << faces.size() << " det";
|
||||
if ((filterRects || findLargestObject) && !faces.empty())
|
||||
{
|
||||
for (size_t i = 0; i < faces.size(); ++i)
|
||||
{
|
||||
cout << ", [" << setw(4) << faces[i].x
|
||||
<< ", " << setw(4) << faces[i].y
|
||||
<< ", " << setw(4) << faces[i].width
|
||||
<< ", " << setw(4) << faces[i].height << "]";
|
||||
}
|
||||
}
|
||||
cout << endl;
|
||||
|
||||
cv::cvtColor(resized_cpu, frameDisp, COLOR_GRAY2BGR);
|
||||
displayState(frameDisp, helpScreen, useGPU, findLargestObject, filterRects, fps);
|
||||
imshow("result", frameDisp);
|
||||
|
||||
char key = (char)waitKey(5);
|
||||
if (key == 27)
|
||||
{
|
||||
break;
|
||||
}
|
||||
|
||||
switch (key)
|
||||
{
|
||||
case ' ':
|
||||
useGPU = !useGPU;
|
||||
break;
|
||||
case 'm':
|
||||
case 'M':
|
||||
findLargestObject = !findLargestObject;
|
||||
break;
|
||||
case 'f':
|
||||
case 'F':
|
||||
filterRects = !filterRects;
|
||||
break;
|
||||
case '1':
|
||||
scaleFactor *= 1.05;
|
||||
break;
|
||||
case 'q':
|
||||
case 'Q':
|
||||
scaleFactor /= 1.05;
|
||||
break;
|
||||
case 'h':
|
||||
case 'H':
|
||||
helpScreen = !helpScreen;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -1,552 +0,0 @@
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
#include <iomanip>
|
||||
#include <stdexcept>
|
||||
#include <opencv2/core/utility.hpp>
|
||||
#include "opencv2/cudaobjdetect.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/objdetect.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
bool help_showed = false;
|
||||
|
||||
class Args
|
||||
{
|
||||
public:
|
||||
Args();
|
||||
static Args read(int argc, char** argv);
|
||||
|
||||
string src;
|
||||
bool src_is_folder;
|
||||
bool src_is_video;
|
||||
bool src_is_camera;
|
||||
int camera_id;
|
||||
|
||||
bool svm_load;
|
||||
string svm;
|
||||
|
||||
bool write_video;
|
||||
string dst_video;
|
||||
double dst_video_fps;
|
||||
|
||||
bool make_gray;
|
||||
|
||||
bool resize_src;
|
||||
int width, height;
|
||||
|
||||
double scale;
|
||||
int nlevels;
|
||||
int gr_threshold;
|
||||
|
||||
double hit_threshold;
|
||||
bool hit_threshold_auto;
|
||||
|
||||
int win_width;
|
||||
int win_stride_width, win_stride_height;
|
||||
int block_width;
|
||||
int block_stride_width, block_stride_height;
|
||||
int cell_width;
|
||||
int nbins;
|
||||
|
||||
bool gamma_corr;
|
||||
};
|
||||
|
||||
|
||||
class App
|
||||
{
|
||||
public:
|
||||
App(const Args& s);
|
||||
void run();
|
||||
|
||||
void handleKey(char key);
|
||||
|
||||
void hogWorkBegin();
|
||||
void hogWorkEnd();
|
||||
string hogWorkFps() const;
|
||||
|
||||
void workBegin();
|
||||
void workEnd();
|
||||
string workFps() const;
|
||||
|
||||
string message() const;
|
||||
|
||||
private:
|
||||
App operator=(App&);
|
||||
|
||||
Args args;
|
||||
bool running;
|
||||
|
||||
bool use_gpu;
|
||||
bool make_gray;
|
||||
double scale;
|
||||
int gr_threshold;
|
||||
int nlevels;
|
||||
double hit_threshold;
|
||||
bool gamma_corr;
|
||||
|
||||
int64 hog_work_begin;
|
||||
double hog_work_fps;
|
||||
|
||||
int64 work_begin;
|
||||
double work_fps;
|
||||
};
|
||||
|
||||
static void printHelp()
|
||||
{
|
||||
cout << "Histogram of Oriented Gradients descriptor and detector sample.\n"
|
||||
<< "\nUsage: hog\n"
|
||||
<< " (<image>|--video <vide>|--camera <camera_id>) # frames source\n"
|
||||
<< " or"
|
||||
<< " (--folder <folder_path>) # load images from folder\n"
|
||||
<< " [--svm <file> # load svm file"
|
||||
<< " [--make_gray <true/false>] # convert image to gray one or not\n"
|
||||
<< " [--resize_src <true/false>] # do resize of the source image or not\n"
|
||||
<< " [--width <int>] # resized image width\n"
|
||||
<< " [--height <int>] # resized image height\n"
|
||||
<< " [--hit_threshold <double>] # classifying plane distance threshold (0.0 usually)\n"
|
||||
<< " [--scale <double>] # HOG window scale factor\n"
|
||||
<< " [--nlevels <int>] # max number of HOG window scales\n"
|
||||
<< " [--win_width <int>] # width of the window\n"
|
||||
<< " [--win_stride_width <int>] # distance by OX axis between neighbour wins\n"
|
||||
<< " [--win_stride_height <int>] # distance by OY axis between neighbour wins\n"
|
||||
<< " [--block_width <int>] # width of the block\n"
|
||||
<< " [--block_stride_width <int>] # distance by 0X axis between neighbour blocks\n"
|
||||
<< " [--block_stride_height <int>] # distance by 0Y axis between neighbour blocks\n"
|
||||
<< " [--cell_width <int>] # width of the cell\n"
|
||||
<< " [--nbins <int>] # number of bins\n"
|
||||
<< " [--gr_threshold <int>] # merging similar rects constant\n"
|
||||
<< " [--gamma_correct <int>] # do gamma correction or not\n"
|
||||
<< " [--write_video <bool>] # write video or not\n"
|
||||
<< " [--dst_video <path>] # output video path\n"
|
||||
<< " [--dst_video_fps <double>] # output video fps\n";
|
||||
help_showed = true;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
try
|
||||
{
|
||||
Args args;
|
||||
if (argc < 2)
|
||||
{
|
||||
printHelp();
|
||||
args.camera_id = 0;
|
||||
args.src_is_camera = true;
|
||||
}
|
||||
else
|
||||
{
|
||||
args = Args::read(argc, argv);
|
||||
if (help_showed)
|
||||
return -1;
|
||||
}
|
||||
App app(args);
|
||||
app.run();
|
||||
}
|
||||
catch (const Exception& e) { return cout << "error: " << e.what() << endl, 1; }
|
||||
catch (const exception& e) { return cout << "error: " << e.what() << endl, 1; }
|
||||
catch(...) { return cout << "unknown exception" << endl, 1; }
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
Args::Args()
|
||||
{
|
||||
src_is_video = false;
|
||||
src_is_camera = false;
|
||||
src_is_folder = false;
|
||||
svm_load = false;
|
||||
camera_id = 0;
|
||||
|
||||
write_video = false;
|
||||
dst_video_fps = 24.;
|
||||
|
||||
make_gray = false;
|
||||
|
||||
resize_src = false;
|
||||
width = 640;
|
||||
height = 480;
|
||||
|
||||
scale = 1.05;
|
||||
nlevels = 13;
|
||||
gr_threshold = 8;
|
||||
hit_threshold = 1.4;
|
||||
hit_threshold_auto = true;
|
||||
|
||||
win_width = 48;
|
||||
win_stride_width = 8;
|
||||
win_stride_height = 8;
|
||||
block_width = 16;
|
||||
block_stride_width = 8;
|
||||
block_stride_height = 8;
|
||||
cell_width = 8;
|
||||
nbins = 9;
|
||||
|
||||
gamma_corr = true;
|
||||
}
|
||||
|
||||
|
||||
Args Args::read(int argc, char** argv)
|
||||
{
|
||||
Args args;
|
||||
for (int i = 1; i < argc; i++)
|
||||
{
|
||||
if (string(argv[i]) == "--make_gray") args.make_gray = (string(argv[++i]) == "true");
|
||||
else if (string(argv[i]) == "--resize_src") args.resize_src = (string(argv[++i]) == "true");
|
||||
else if (string(argv[i]) == "--width") args.width = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--height") args.height = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--hit_threshold")
|
||||
{
|
||||
args.hit_threshold = atof(argv[++i]);
|
||||
args.hit_threshold_auto = false;
|
||||
}
|
||||
else if (string(argv[i]) == "--scale") args.scale = atof(argv[++i]);
|
||||
else if (string(argv[i]) == "--nlevels") args.nlevels = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--win_width") args.win_width = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--win_stride_width") args.win_stride_width = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--win_stride_height") args.win_stride_height = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--block_width") args.block_width = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--block_stride_width") args.block_stride_width = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--block_stride_height") args.block_stride_height = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--cell_width") args.cell_width = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--nbins") args.nbins = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--gr_threshold") args.gr_threshold = atoi(argv[++i]);
|
||||
else if (string(argv[i]) == "--gamma_correct") args.gamma_corr = (string(argv[++i]) == "true");
|
||||
else if (string(argv[i]) == "--write_video") args.write_video = (string(argv[++i]) == "true");
|
||||
else if (string(argv[i]) == "--dst_video") args.dst_video = argv[++i];
|
||||
else if (string(argv[i]) == "--dst_video_fps") args.dst_video_fps = atof(argv[++i]);
|
||||
else if (string(argv[i]) == "--help") printHelp();
|
||||
else if (string(argv[i]) == "--video") { args.src = argv[++i]; args.src_is_video = true; }
|
||||
else if (string(argv[i]) == "--camera") { args.camera_id = atoi(argv[++i]); args.src_is_camera = true; }
|
||||
else if (string(argv[i]) == "--folder") { args.src = argv[++i]; args.src_is_folder = true;}
|
||||
else if (string(argv[i]) == "--svm") { args.svm = argv[++i]; args.svm_load = true;}
|
||||
else if (args.src.empty()) args.src = argv[i];
|
||||
else throw runtime_error((string("unknown key: ") + argv[i]));
|
||||
}
|
||||
return args;
|
||||
}
|
||||
|
||||
|
||||
App::App(const Args& s)
|
||||
{
|
||||
cv::cuda::printShortCudaDeviceInfo(cv::cuda::getDevice());
|
||||
|
||||
args = s;
|
||||
cout << "\nControls:\n"
|
||||
<< "\tESC - exit\n"
|
||||
<< "\tm - change mode GPU <-> CPU\n"
|
||||
<< "\tg - convert image to gray or not\n"
|
||||
<< "\t1/q - increase/decrease HOG scale\n"
|
||||
<< "\t2/w - increase/decrease levels count\n"
|
||||
<< "\t3/e - increase/decrease HOG group threshold\n"
|
||||
<< "\t4/r - increase/decrease hit threshold\n"
|
||||
<< endl;
|
||||
|
||||
use_gpu = true;
|
||||
make_gray = args.make_gray;
|
||||
scale = args.scale;
|
||||
gr_threshold = args.gr_threshold;
|
||||
nlevels = args.nlevels;
|
||||
|
||||
if (args.hit_threshold_auto)
|
||||
args.hit_threshold = args.win_width == 48 ? 1.4 : 0.;
|
||||
hit_threshold = args.hit_threshold;
|
||||
|
||||
gamma_corr = args.gamma_corr;
|
||||
|
||||
cout << "Scale: " << scale << endl;
|
||||
if (args.resize_src)
|
||||
cout << "Resized source: (" << args.width << ", " << args.height << ")\n";
|
||||
cout << "Group threshold: " << gr_threshold << endl;
|
||||
cout << "Levels number: " << nlevels << endl;
|
||||
cout << "Win size: (" << args.win_width << ", " << args.win_width*2 << ")\n";
|
||||
cout << "Win stride: (" << args.win_stride_width << ", " << args.win_stride_height << ")\n";
|
||||
cout << "Block size: (" << args.block_width << ", " << args.block_width << ")\n";
|
||||
cout << "Block stride: (" << args.block_stride_width << ", " << args.block_stride_height << ")\n";
|
||||
cout << "Cell size: (" << args.cell_width << ", " << args.cell_width << ")\n";
|
||||
cout << "Bins number: " << args.nbins << endl;
|
||||
cout << "Hit threshold: " << hit_threshold << endl;
|
||||
cout << "Gamma correction: " << gamma_corr << endl;
|
||||
cout << endl;
|
||||
}
|
||||
|
||||
|
||||
void App::run()
|
||||
{
|
||||
running = true;
|
||||
cv::VideoWriter video_writer;
|
||||
|
||||
Size win_stride(args.win_stride_width, args.win_stride_height);
|
||||
Size win_size(args.win_width, args.win_width * 2);
|
||||
Size block_size(args.block_width, args.block_width);
|
||||
Size block_stride(args.block_stride_width, args.block_stride_height);
|
||||
Size cell_size(args.cell_width, args.cell_width);
|
||||
|
||||
cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size, block_size, block_stride, cell_size, args.nbins);
|
||||
cv::HOGDescriptor cpu_hog(win_size, block_size, block_stride, cell_size, args.nbins);
|
||||
|
||||
if(args.svm_load) {
|
||||
std::vector<float> svm_model;
|
||||
const std::string model_file_name = args.svm;
|
||||
FileStorage ifs(model_file_name, FileStorage::READ);
|
||||
if (ifs.isOpened()) {
|
||||
ifs["svm_detector"] >> svm_model;
|
||||
} else {
|
||||
const std::string what =
|
||||
"could not load model for hog classifier from file: "
|
||||
+ model_file_name;
|
||||
throw std::runtime_error(what);
|
||||
}
|
||||
|
||||
// check if the variables are initialized
|
||||
if (svm_model.empty()) {
|
||||
const std::string what =
|
||||
"HoG classifier: svm model could not be loaded from file"
|
||||
+ model_file_name;
|
||||
throw std::runtime_error(what);
|
||||
}
|
||||
|
||||
gpu_hog->setSVMDetector(svm_model);
|
||||
cpu_hog.setSVMDetector(svm_model);
|
||||
} else {
|
||||
// Create HOG descriptors and detectors here
|
||||
Mat detector = gpu_hog->getDefaultPeopleDetector();
|
||||
|
||||
gpu_hog->setSVMDetector(detector);
|
||||
cpu_hog.setSVMDetector(detector);
|
||||
}
|
||||
|
||||
cout << "gpusvmDescriptorSize : " << gpu_hog->getDescriptorSize()
|
||||
<< endl;
|
||||
cout << "cpusvmDescriptorSize : " << cpu_hog.getDescriptorSize()
|
||||
<< endl;
|
||||
|
||||
while (running)
|
||||
{
|
||||
VideoCapture vc;
|
||||
Mat frame;
|
||||
vector<String> filenames;
|
||||
|
||||
unsigned int count = 1;
|
||||
|
||||
if (args.src_is_video)
|
||||
{
|
||||
vc.open(args.src.c_str());
|
||||
if (!vc.isOpened())
|
||||
throw runtime_error(string("can't open video file: " + args.src));
|
||||
vc >> frame;
|
||||
}
|
||||
else if (args.src_is_folder) {
|
||||
String folder = args.src;
|
||||
cout << folder << endl;
|
||||
glob(folder, filenames);
|
||||
frame = imread(filenames[count]); // 0 --> .gitignore
|
||||
if (!frame.data)
|
||||
cerr << "Problem loading image from folder!!!" << endl;
|
||||
}
|
||||
else if (args.src_is_camera)
|
||||
{
|
||||
vc.open(args.camera_id);
|
||||
if (!vc.isOpened())
|
||||
{
|
||||
stringstream msg;
|
||||
msg << "can't open camera: " << args.camera_id;
|
||||
throw runtime_error(msg.str());
|
||||
}
|
||||
vc >> frame;
|
||||
}
|
||||
else
|
||||
{
|
||||
frame = imread(args.src);
|
||||
if (frame.empty())
|
||||
throw runtime_error(string("can't open image file: " + args.src));
|
||||
}
|
||||
|
||||
Mat img_aux, img, img_to_show;
|
||||
cuda::GpuMat gpu_img;
|
||||
|
||||
// Iterate over all frames
|
||||
while (running && !frame.empty())
|
||||
{
|
||||
workBegin();
|
||||
|
||||
// Change format of the image
|
||||
if (make_gray) cvtColor(frame, img_aux, COLOR_BGR2GRAY);
|
||||
else if (use_gpu) cvtColor(frame, img_aux, COLOR_BGR2BGRA);
|
||||
else frame.copyTo(img_aux);
|
||||
|
||||
// Resize image
|
||||
if (args.resize_src) resize(img_aux, img, Size(args.width, args.height));
|
||||
else img = img_aux;
|
||||
img_to_show = img;
|
||||
|
||||
vector<Rect> found;
|
||||
|
||||
// Perform HOG classification
|
||||
hogWorkBegin();
|
||||
if (use_gpu)
|
||||
{
|
||||
gpu_img.upload(img);
|
||||
gpu_hog->setNumLevels(nlevels);
|
||||
gpu_hog->setHitThreshold(hit_threshold);
|
||||
gpu_hog->setWinStride(win_stride);
|
||||
gpu_hog->setScaleFactor(scale);
|
||||
gpu_hog->setGroupThreshold(gr_threshold);
|
||||
gpu_hog->detectMultiScale(gpu_img, found);
|
||||
}
|
||||
else
|
||||
{
|
||||
cpu_hog.nlevels = nlevels;
|
||||
cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
|
||||
Size(0, 0), scale, gr_threshold);
|
||||
}
|
||||
hogWorkEnd();
|
||||
|
||||
// Draw positive classified windows
|
||||
for (size_t i = 0; i < found.size(); i++)
|
||||
{
|
||||
Rect r = found[i];
|
||||
rectangle(img_to_show, r.tl(), r.br(), Scalar(0, 255, 0), 3);
|
||||
}
|
||||
|
||||
if (use_gpu)
|
||||
putText(img_to_show, "Mode: GPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
else
|
||||
putText(img_to_show, "Mode: CPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
putText(img_to_show, "FPS HOG: " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
putText(img_to_show, "FPS total: " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
imshow("opencv_gpu_hog", img_to_show);
|
||||
|
||||
if (args.src_is_video || args.src_is_camera) vc >> frame;
|
||||
if (args.src_is_folder) {
|
||||
count++;
|
||||
if (count < filenames.size()) {
|
||||
frame = imread(filenames[count]);
|
||||
} else {
|
||||
Mat empty;
|
||||
frame = empty;
|
||||
}
|
||||
}
|
||||
|
||||
workEnd();
|
||||
|
||||
if (args.write_video)
|
||||
{
|
||||
if (!video_writer.isOpened())
|
||||
{
|
||||
video_writer.open(args.dst_video, VideoWriter::fourcc('x','v','i','d'), args.dst_video_fps,
|
||||
img_to_show.size(), true);
|
||||
if (!video_writer.isOpened())
|
||||
throw std::runtime_error("can't create video writer");
|
||||
}
|
||||
|
||||
if (make_gray) cvtColor(img_to_show, img, COLOR_GRAY2BGR);
|
||||
else cvtColor(img_to_show, img, COLOR_BGRA2BGR);
|
||||
|
||||
video_writer << img;
|
||||
}
|
||||
|
||||
handleKey((char)waitKey(3));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void App::handleKey(char key)
|
||||
{
|
||||
switch (key)
|
||||
{
|
||||
case 27:
|
||||
running = false;
|
||||
break;
|
||||
case 'm':
|
||||
case 'M':
|
||||
use_gpu = !use_gpu;
|
||||
cout << "Switched to " << (use_gpu ? "CUDA" : "CPU") << " mode\n";
|
||||
break;
|
||||
case 'g':
|
||||
case 'G':
|
||||
make_gray = !make_gray;
|
||||
cout << "Convert image to gray: " << (make_gray ? "YES" : "NO") << endl;
|
||||
break;
|
||||
case '1':
|
||||
scale *= 1.05;
|
||||
cout << "Scale: " << scale << endl;
|
||||
break;
|
||||
case 'q':
|
||||
case 'Q':
|
||||
scale /= 1.05;
|
||||
cout << "Scale: " << scale << endl;
|
||||
break;
|
||||
case '2':
|
||||
nlevels++;
|
||||
cout << "Levels number: " << nlevels << endl;
|
||||
break;
|
||||
case 'w':
|
||||
case 'W':
|
||||
nlevels = max(nlevels - 1, 1);
|
||||
cout << "Levels number: " << nlevels << endl;
|
||||
break;
|
||||
case '3':
|
||||
gr_threshold++;
|
||||
cout << "Group threshold: " << gr_threshold << endl;
|
||||
break;
|
||||
case 'e':
|
||||
case 'E':
|
||||
gr_threshold = max(0, gr_threshold - 1);
|
||||
cout << "Group threshold: " << gr_threshold << endl;
|
||||
break;
|
||||
case '4':
|
||||
hit_threshold+=0.25;
|
||||
cout << "Hit threshold: " << hit_threshold << endl;
|
||||
break;
|
||||
case 'r':
|
||||
case 'R':
|
||||
hit_threshold = max(0.0, hit_threshold - 0.25);
|
||||
cout << "Hit threshold: " << hit_threshold << endl;
|
||||
break;
|
||||
case 'c':
|
||||
case 'C':
|
||||
gamma_corr = !gamma_corr;
|
||||
cout << "Gamma correction: " << gamma_corr << endl;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
inline void App::hogWorkBegin() { hog_work_begin = getTickCount(); }
|
||||
|
||||
inline void App::hogWorkEnd()
|
||||
{
|
||||
int64 delta = getTickCount() - hog_work_begin;
|
||||
double freq = getTickFrequency();
|
||||
hog_work_fps = freq / delta;
|
||||
}
|
||||
|
||||
inline string App::hogWorkFps() const
|
||||
{
|
||||
stringstream ss;
|
||||
ss << hog_work_fps;
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
|
||||
inline void App::workBegin() { work_begin = getTickCount(); }
|
||||
|
||||
inline void App::workEnd()
|
||||
{
|
||||
int64 delta = getTickCount() - work_begin;
|
||||
double freq = getTickFrequency();
|
||||
work_fps = freq / delta;
|
||||
}
|
||||
|
||||
inline string App::workFps() const
|
||||
{
|
||||
stringstream ss;
|
||||
ss << work_fps;
|
||||
return ss.str();
|
||||
}
|
||||
@@ -6,7 +6,7 @@ import org.opencv.core.Rect;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.imgcodecs.Imgcodecs;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.objdetect.CascadeClassifier;
|
||||
import org.opencv.xobjdetect.CascadeClassifier;
|
||||
|
||||
/*
|
||||
* Detects faces in an image, draws boxes around them, and writes the results
|
||||
|
||||
@@ -4,7 +4,7 @@ import org.opencv.core.Point
|
||||
import org.opencv.core.Scalar
|
||||
import org.opencv.imgcodecs.Imgcodecs
|
||||
import org.opencv.imgproc.Imgproc
|
||||
import org.opencv.objdetect.CascadeClassifier
|
||||
import org.opencv.xobjdetect.CascadeClassifier
|
||||
import reflect._
|
||||
|
||||
/*
|
||||
|
||||
-98
@@ -1,98 +0,0 @@
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.Core;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfRect;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Rect;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.Size;
|
||||
import org.opencv.highgui.HighGui;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.objdetect.CascadeClassifier;
|
||||
import org.opencv.videoio.VideoCapture;
|
||||
|
||||
class ObjectDetection {
|
||||
public void detectAndDisplay(Mat frame, CascadeClassifier faceCascade, CascadeClassifier eyesCascade) {
|
||||
Mat frameGray = new Mat();
|
||||
Imgproc.cvtColor(frame, frameGray, Imgproc.COLOR_BGR2GRAY);
|
||||
Imgproc.equalizeHist(frameGray, frameGray);
|
||||
|
||||
// -- Detect faces
|
||||
MatOfRect faces = new MatOfRect();
|
||||
faceCascade.detectMultiScale(frameGray, faces);
|
||||
|
||||
List<Rect> listOfFaces = faces.toList();
|
||||
for (Rect face : listOfFaces) {
|
||||
Point center = new Point(face.x + face.width / 2, face.y + face.height / 2);
|
||||
Imgproc.ellipse(frame, center, new Size(face.width / 2, face.height / 2), 0, 0, 360,
|
||||
new Scalar(255, 0, 255));
|
||||
|
||||
Mat faceROI = frameGray.submat(face);
|
||||
|
||||
// -- In each face, detect eyes
|
||||
MatOfRect eyes = new MatOfRect();
|
||||
eyesCascade.detectMultiScale(faceROI, eyes);
|
||||
|
||||
List<Rect> listOfEyes = eyes.toList();
|
||||
for (Rect eye : listOfEyes) {
|
||||
Point eyeCenter = new Point(face.x + eye.x + eye.width / 2, face.y + eye.y + eye.height / 2);
|
||||
int radius = (int) Math.round((eye.width + eye.height) * 0.25);
|
||||
Imgproc.circle(frame, eyeCenter, radius, new Scalar(255, 0, 0), 4);
|
||||
}
|
||||
}
|
||||
|
||||
//-- Show what you got
|
||||
HighGui.imshow("Capture - Face detection", frame );
|
||||
}
|
||||
|
||||
public void run(String[] args) {
|
||||
String filenameFaceCascade = args.length > 2 ? args[0] : "../../data/haarcascades/haarcascade_frontalface_alt.xml";
|
||||
String filenameEyesCascade = args.length > 2 ? args[1] : "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml";
|
||||
int cameraDevice = args.length > 2 ? Integer.parseInt(args[2]) : 0;
|
||||
|
||||
CascadeClassifier faceCascade = new CascadeClassifier();
|
||||
CascadeClassifier eyesCascade = new CascadeClassifier();
|
||||
|
||||
if (!faceCascade.load(filenameFaceCascade)) {
|
||||
System.err.println("--(!)Error loading face cascade: " + filenameFaceCascade);
|
||||
System.exit(0);
|
||||
}
|
||||
if (!eyesCascade.load(filenameEyesCascade)) {
|
||||
System.err.println("--(!)Error loading eyes cascade: " + filenameEyesCascade);
|
||||
System.exit(0);
|
||||
}
|
||||
|
||||
VideoCapture capture = new VideoCapture(cameraDevice);
|
||||
if (!capture.isOpened()) {
|
||||
System.err.println("--(!)Error opening video capture");
|
||||
System.exit(0);
|
||||
}
|
||||
|
||||
Mat frame = new Mat();
|
||||
while (capture.read(frame)) {
|
||||
if (frame.empty()) {
|
||||
System.err.println("--(!) No captured frame -- Break!");
|
||||
break;
|
||||
}
|
||||
|
||||
//-- 3. Apply the classifier to the frame
|
||||
detectAndDisplay(frame, faceCascade, eyesCascade);
|
||||
|
||||
if (HighGui.waitKey(10) == 27) {
|
||||
break;// escape
|
||||
}
|
||||
}
|
||||
|
||||
System.exit(0);
|
||||
}
|
||||
}
|
||||
|
||||
public class ObjectDetectionDemo {
|
||||
public static void main(String[] args) {
|
||||
// Load the native OpenCV library
|
||||
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
|
||||
|
||||
new ObjectDetection().run(args);
|
||||
}
|
||||
}
|
||||
@@ -1,79 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
'''
|
||||
face detection using haar cascades
|
||||
|
||||
USAGE:
|
||||
facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
|
||||
'''
|
||||
|
||||
# Python 2/3 compatibility
|
||||
from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
|
||||
# local modules
|
||||
from video import create_capture
|
||||
from common import clock, draw_str
|
||||
|
||||
|
||||
def detect(img, cascade):
|
||||
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
|
||||
flags=cv.CASCADE_SCALE_IMAGE)
|
||||
if len(rects) == 0:
|
||||
return []
|
||||
rects[:,2:] += rects[:,:2]
|
||||
return rects
|
||||
|
||||
def draw_rects(img, rects, color):
|
||||
for x1, y1, x2, y2 in rects:
|
||||
cv.rectangle(img, (x1, y1), (x2, y2), color, 2)
|
||||
|
||||
def main():
|
||||
import sys, getopt
|
||||
|
||||
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
|
||||
try:
|
||||
video_src = video_src[0]
|
||||
except:
|
||||
video_src = 0
|
||||
args = dict(args)
|
||||
cascade_fn = args.get('--cascade', "haarcascades/haarcascade_frontalface_alt.xml")
|
||||
nested_fn = args.get('--nested-cascade', "haarcascades/haarcascade_eye.xml")
|
||||
|
||||
cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
|
||||
nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
|
||||
|
||||
cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('lena.jpg')))
|
||||
|
||||
while True:
|
||||
_ret, img = cam.read()
|
||||
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
|
||||
gray = cv.equalizeHist(gray)
|
||||
|
||||
t = clock()
|
||||
rects = detect(gray, cascade)
|
||||
vis = img.copy()
|
||||
draw_rects(vis, rects, (0, 255, 0))
|
||||
if not nested.empty():
|
||||
for x1, y1, x2, y2 in rects:
|
||||
roi = gray[y1:y2, x1:x2]
|
||||
vis_roi = vis[y1:y2, x1:x2]
|
||||
subrects = detect(roi.copy(), nested)
|
||||
draw_rects(vis_roi, subrects, (255, 0, 0))
|
||||
dt = clock() - t
|
||||
|
||||
draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
|
||||
cv.imshow('facedetect', vis)
|
||||
|
||||
if cv.waitKey(5) == 27:
|
||||
break
|
||||
|
||||
print('Done')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(__doc__)
|
||||
main()
|
||||
cv.destroyAllWindows()
|
||||
@@ -1,76 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
'''
|
||||
example to detect upright people in images using HOG features
|
||||
|
||||
Usage:
|
||||
peopledetect.py <image_names>
|
||||
|
||||
Press any key to continue, ESC to stop.
|
||||
'''
|
||||
|
||||
# Python 2/3 compatibility
|
||||
from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
|
||||
|
||||
def inside(r, q):
|
||||
rx, ry, rw, rh = r
|
||||
qx, qy, qw, qh = q
|
||||
return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
|
||||
|
||||
|
||||
def draw_detections(img, rects, thickness = 1):
|
||||
for x, y, w, h in rects:
|
||||
# the HOG detector returns slightly larger rectangles than the real objects.
|
||||
# so we slightly shrink the rectangles to get a nicer output.
|
||||
pad_w, pad_h = int(0.15*w), int(0.05*h)
|
||||
cv.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
|
||||
|
||||
|
||||
def main():
|
||||
import sys
|
||||
from glob import glob
|
||||
import itertools as it
|
||||
|
||||
hog = cv.HOGDescriptor()
|
||||
hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
|
||||
|
||||
default = [cv.samples.findFile('basketball2.png')] if len(sys.argv[1:]) == 0 else []
|
||||
|
||||
for fn in it.chain(*map(glob, default + sys.argv[1:])):
|
||||
print(fn, ' - ',)
|
||||
try:
|
||||
img = cv.imread(fn)
|
||||
if img is None:
|
||||
print('Failed to load image file:', fn)
|
||||
continue
|
||||
except:
|
||||
print('loading error')
|
||||
continue
|
||||
|
||||
found, _w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
|
||||
found_filtered = []
|
||||
for ri, r in enumerate(found):
|
||||
for qi, q in enumerate(found):
|
||||
if ri != qi and inside(r, q):
|
||||
break
|
||||
else:
|
||||
found_filtered.append(r)
|
||||
draw_detections(img, found)
|
||||
draw_detections(img, found_filtered, 3)
|
||||
print('%d (%d) found' % (len(found_filtered), len(found)))
|
||||
cv.imshow('img', img)
|
||||
ch = cv.waitKey()
|
||||
if ch == 27:
|
||||
break
|
||||
|
||||
print('Done')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(__doc__)
|
||||
main()
|
||||
cv.destroyAllWindows()
|
||||
@@ -1,61 +0,0 @@
|
||||
from __future__ import print_function
|
||||
import cv2 as cv
|
||||
import argparse
|
||||
|
||||
def detectAndDisplay(frame):
|
||||
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
|
||||
frame_gray = cv.equalizeHist(frame_gray)
|
||||
|
||||
#-- Detect faces
|
||||
faces = face_cascade.detectMultiScale(frame_gray)
|
||||
for (x,y,w,h) in faces:
|
||||
center = (x + w//2, y + h//2)
|
||||
frame = cv.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
|
||||
|
||||
faceROI = frame_gray[y:y+h,x:x+w]
|
||||
#-- In each face, detect eyes
|
||||
eyes = eyes_cascade.detectMultiScale(faceROI)
|
||||
for (x2,y2,w2,h2) in eyes:
|
||||
eye_center = (x + x2 + w2//2, y + y2 + h2//2)
|
||||
radius = int(round((w2 + h2)*0.25))
|
||||
frame = cv.circle(frame, eye_center, radius, (255, 0, 0 ), 4)
|
||||
|
||||
cv.imshow('Capture - Face detection', frame)
|
||||
|
||||
parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
|
||||
parser.add_argument('--face_cascade', help='Path to face cascade.', default='data/haarcascades/haarcascade_frontalface_alt.xml')
|
||||
parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
|
||||
parser.add_argument('--camera', help='Camera divide number.', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
||||
face_cascade_name = args.face_cascade
|
||||
eyes_cascade_name = args.eyes_cascade
|
||||
|
||||
face_cascade = cv.CascadeClassifier()
|
||||
eyes_cascade = cv.CascadeClassifier()
|
||||
|
||||
#-- 1. Load the cascades
|
||||
if not face_cascade.load(cv.samples.findFile(face_cascade_name)):
|
||||
print('--(!)Error loading face cascade')
|
||||
exit(0)
|
||||
if not eyes_cascade.load(cv.samples.findFile(eyes_cascade_name)):
|
||||
print('--(!)Error loading eyes cascade')
|
||||
exit(0)
|
||||
|
||||
camera_device = args.camera
|
||||
#-- 2. Read the video stream
|
||||
cap = cv.VideoCapture(camera_device)
|
||||
if not cap.isOpened:
|
||||
print('--(!)Error opening video capture')
|
||||
exit(0)
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if frame is None:
|
||||
print('--(!) No captured frame -- Break!')
|
||||
break
|
||||
|
||||
detectAndDisplay(frame)
|
||||
|
||||
if cv.waitKey(10) == 27:
|
||||
break
|
||||
@@ -1,352 +0,0 @@
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
#include <iomanip>
|
||||
#include <stdexcept>
|
||||
#include <opencv2/core/ocl.hpp>
|
||||
#include <opencv2/core/utility.hpp>
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include <opencv2/videoio.hpp>
|
||||
#include <opencv2/highgui.hpp>
|
||||
#include <opencv2/objdetect.hpp>
|
||||
#include <opencv2/imgproc.hpp>
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
class App
|
||||
{
|
||||
public:
|
||||
App(CommandLineParser& cmd);
|
||||
void run();
|
||||
void handleKey(char key);
|
||||
void hogWorkBegin();
|
||||
void hogWorkEnd();
|
||||
string hogWorkFps() const;
|
||||
void workBegin();
|
||||
void workEnd();
|
||||
string workFps() const;
|
||||
private:
|
||||
App operator=(App&);
|
||||
|
||||
//Args args;
|
||||
bool running;
|
||||
bool make_gray;
|
||||
double scale;
|
||||
double resize_scale;
|
||||
int win_width;
|
||||
int win_stride_width, win_stride_height;
|
||||
int gr_threshold;
|
||||
int nlevels;
|
||||
double hit_threshold;
|
||||
bool gamma_corr;
|
||||
|
||||
int64 hog_work_begin;
|
||||
double hog_work_fps;
|
||||
int64 work_begin;
|
||||
double work_fps;
|
||||
|
||||
string img_source;
|
||||
string vdo_source;
|
||||
string output;
|
||||
int camera_id;
|
||||
bool write_once;
|
||||
};
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
const char* keys =
|
||||
"{ h help | | print help message }"
|
||||
"{ i input | | specify input image}"
|
||||
"{ c camera | -1 | enable camera capturing }"
|
||||
"{ v video | vtest.avi | use video as input }"
|
||||
"{ g gray | | convert image to gray one or not}"
|
||||
"{ s scale | 1.0 | resize the image before detect}"
|
||||
"{ o output | output.avi | specify output path when input is images}";
|
||||
CommandLineParser cmd(argc, argv, keys);
|
||||
if (cmd.has("help"))
|
||||
{
|
||||
cmd.printMessage();
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
App app(cmd);
|
||||
try
|
||||
{
|
||||
app.run();
|
||||
}
|
||||
catch (const Exception& e)
|
||||
{
|
||||
return cout << "error: " << e.what() << endl, 1;
|
||||
}
|
||||
catch (const exception& e)
|
||||
{
|
||||
return cout << "error: " << e.what() << endl, 1;
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
return cout << "unknown exception" << endl, 1;
|
||||
}
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
App::App(CommandLineParser& cmd)
|
||||
{
|
||||
cout << "\nControls:\n"
|
||||
<< "\tESC - exit\n"
|
||||
<< "\tm - change mode GPU <-> CPU\n"
|
||||
<< "\tg - convert image to gray or not\n"
|
||||
<< "\to - save output image once, or switch on/off video save\n"
|
||||
<< "\t1/q - increase/decrease HOG scale\n"
|
||||
<< "\t2/w - increase/decrease levels count\n"
|
||||
<< "\t3/e - increase/decrease HOG group threshold\n"
|
||||
<< "\t4/r - increase/decrease hit threshold\n"
|
||||
<< endl;
|
||||
|
||||
make_gray = cmd.has("gray");
|
||||
resize_scale = cmd.get<double>("s");
|
||||
vdo_source = samples::findFileOrKeep(cmd.get<string>("v"));
|
||||
img_source = cmd.get<string>("i");
|
||||
output = cmd.get<string>("o");
|
||||
camera_id = cmd.get<int>("c");
|
||||
|
||||
win_width = 48;
|
||||
win_stride_width = 8;
|
||||
win_stride_height = 8;
|
||||
gr_threshold = 8;
|
||||
nlevels = 13;
|
||||
hit_threshold = 1.4;
|
||||
scale = 1.05;
|
||||
gamma_corr = true;
|
||||
write_once = false;
|
||||
|
||||
cout << "Group threshold: " << gr_threshold << endl;
|
||||
cout << "Levels number: " << nlevels << endl;
|
||||
cout << "Win width: " << win_width << endl;
|
||||
cout << "Win stride: (" << win_stride_width << ", " << win_stride_height << ")\n";
|
||||
cout << "Hit threshold: " << hit_threshold << endl;
|
||||
cout << "Gamma correction: " << gamma_corr << endl;
|
||||
cout << endl;
|
||||
}
|
||||
|
||||
void App::run()
|
||||
{
|
||||
running = true;
|
||||
VideoWriter video_writer;
|
||||
|
||||
Size win_size(win_width, win_width * 2);
|
||||
Size win_stride(win_stride_width, win_stride_height);
|
||||
|
||||
// Create HOG descriptors and detectors here
|
||||
|
||||
HOGDescriptor hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9, 1, -1,
|
||||
HOGDescriptor::L2Hys, 0.2, gamma_corr, cv::HOGDescriptor::DEFAULT_NLEVELS);
|
||||
hog.setSVMDetector( HOGDescriptor::getDaimlerPeopleDetector() );
|
||||
|
||||
while (running)
|
||||
{
|
||||
VideoCapture vc;
|
||||
UMat frame;
|
||||
|
||||
if (vdo_source!="")
|
||||
{
|
||||
vc.open(vdo_source.c_str());
|
||||
if (!vc.isOpened())
|
||||
throw runtime_error(string("can't open video file: " + vdo_source));
|
||||
vc >> frame;
|
||||
}
|
||||
else if (camera_id != -1)
|
||||
{
|
||||
vc.open(camera_id);
|
||||
if (!vc.isOpened())
|
||||
{
|
||||
stringstream msg;
|
||||
msg << "can't open camera: " << camera_id;
|
||||
throw runtime_error(msg.str());
|
||||
}
|
||||
vc >> frame;
|
||||
}
|
||||
else
|
||||
{
|
||||
imread(img_source).copyTo(frame);
|
||||
if (frame.empty())
|
||||
throw runtime_error(string("can't open image file: " + img_source));
|
||||
}
|
||||
|
||||
UMat img_aux, img, img_to_show;
|
||||
|
||||
// Iterate over all frames
|
||||
while (running && !frame.empty())
|
||||
{
|
||||
workBegin();
|
||||
|
||||
// Change format of the image
|
||||
if (make_gray) cvtColor(frame, img_aux, COLOR_BGR2GRAY );
|
||||
else frame.copyTo(img_aux);
|
||||
|
||||
// Resize image
|
||||
if (abs(scale-1.0)>0.001)
|
||||
{
|
||||
Size sz((int)((double)img_aux.cols/resize_scale), (int)((double)img_aux.rows/resize_scale));
|
||||
resize(img_aux, img, sz, 0, 0, INTER_LINEAR_EXACT);
|
||||
}
|
||||
else img = img_aux;
|
||||
img.copyTo(img_to_show);
|
||||
hog.nlevels = nlevels;
|
||||
vector<Rect> found;
|
||||
|
||||
// Perform HOG classification
|
||||
hogWorkBegin();
|
||||
|
||||
hog.detectMultiScale(img, found, hit_threshold, win_stride,
|
||||
Size(0, 0), scale, gr_threshold);
|
||||
hogWorkEnd();
|
||||
|
||||
|
||||
// Draw positive classified windows
|
||||
for (size_t i = 0; i < found.size(); i++)
|
||||
{
|
||||
rectangle(img_to_show, found[i], Scalar(0, 255, 0), 3);
|
||||
}
|
||||
|
||||
putText(img_to_show, ocl::useOpenCL() ? "Mode: OpenCL" : "Mode: CPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
putText(img_to_show, "FPS (HOG only): " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
putText(img_to_show, "FPS (total): " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
|
||||
imshow("opencv_hog", img_to_show);
|
||||
if (vdo_source!="" || camera_id!=-1) vc >> frame;
|
||||
|
||||
workEnd();
|
||||
|
||||
if (output!="" && write_once)
|
||||
{
|
||||
if (img_source!="") // write image
|
||||
{
|
||||
write_once = false;
|
||||
imwrite(output, img_to_show);
|
||||
}
|
||||
else //write video
|
||||
{
|
||||
if (!video_writer.isOpened())
|
||||
{
|
||||
video_writer.open(output, VideoWriter::fourcc('x','v','i','d'), 24,
|
||||
img_to_show.size(), true);
|
||||
if (!video_writer.isOpened())
|
||||
throw std::runtime_error("can't create video writer");
|
||||
}
|
||||
|
||||
if (make_gray) cvtColor(img_to_show, img, COLOR_GRAY2BGR);
|
||||
else cvtColor(img_to_show, img, COLOR_BGRA2BGR);
|
||||
|
||||
video_writer << img;
|
||||
}
|
||||
}
|
||||
|
||||
handleKey((char)waitKey(3));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void App::handleKey(char key)
|
||||
{
|
||||
switch (key)
|
||||
{
|
||||
case 27:
|
||||
running = false;
|
||||
break;
|
||||
case 'm':
|
||||
case 'M':
|
||||
ocl::setUseOpenCL(!cv::ocl::useOpenCL());
|
||||
cout << "Switched to " << (ocl::useOpenCL() ? "OpenCL enabled" : "CPU") << " mode\n";
|
||||
break;
|
||||
case 'g':
|
||||
case 'G':
|
||||
make_gray = !make_gray;
|
||||
cout << "Convert image to gray: " << (make_gray ? "YES" : "NO") << endl;
|
||||
break;
|
||||
case '1':
|
||||
scale *= 1.05;
|
||||
cout << "Scale: " << scale << endl;
|
||||
break;
|
||||
case 'q':
|
||||
case 'Q':
|
||||
scale /= 1.05;
|
||||
cout << "Scale: " << scale << endl;
|
||||
break;
|
||||
case '2':
|
||||
nlevels++;
|
||||
cout << "Levels number: " << nlevels << endl;
|
||||
break;
|
||||
case 'w':
|
||||
case 'W':
|
||||
nlevels = max(nlevels - 1, 1);
|
||||
cout << "Levels number: " << nlevels << endl;
|
||||
break;
|
||||
case '3':
|
||||
gr_threshold++;
|
||||
cout << "Group threshold: " << gr_threshold << endl;
|
||||
break;
|
||||
case 'e':
|
||||
case 'E':
|
||||
gr_threshold = max(0, gr_threshold - 1);
|
||||
cout << "Group threshold: " << gr_threshold << endl;
|
||||
break;
|
||||
case '4':
|
||||
hit_threshold+=0.25;
|
||||
cout << "Hit threshold: " << hit_threshold << endl;
|
||||
break;
|
||||
case 'r':
|
||||
case 'R':
|
||||
hit_threshold = max(0.0, hit_threshold - 0.25);
|
||||
cout << "Hit threshold: " << hit_threshold << endl;
|
||||
break;
|
||||
case 'c':
|
||||
case 'C':
|
||||
gamma_corr = !gamma_corr;
|
||||
cout << "Gamma correction: " << gamma_corr << endl;
|
||||
break;
|
||||
case 'o':
|
||||
case 'O':
|
||||
write_once = !write_once;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
inline void App::hogWorkBegin()
|
||||
{
|
||||
hog_work_begin = getTickCount();
|
||||
}
|
||||
|
||||
inline void App::hogWorkEnd()
|
||||
{
|
||||
int64 delta = getTickCount() - hog_work_begin;
|
||||
double freq = getTickFrequency();
|
||||
hog_work_fps = freq / delta;
|
||||
}
|
||||
|
||||
inline string App::hogWorkFps() const
|
||||
{
|
||||
stringstream ss;
|
||||
ss << hog_work_fps;
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
inline void App::workBegin()
|
||||
{
|
||||
work_begin = getTickCount();
|
||||
}
|
||||
|
||||
inline void App::workEnd()
|
||||
{
|
||||
int64 delta = getTickCount() - work_begin;
|
||||
double freq = getTickFrequency();
|
||||
work_fps = freq / delta;
|
||||
}
|
||||
|
||||
inline string App::workFps() const
|
||||
{
|
||||
stringstream ss;
|
||||
ss << work_fps;
|
||||
return ss.str();
|
||||
}
|
||||
@@ -1,253 +0,0 @@
|
||||
#include "opencv2/objdetect.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/core/ocl.hpp"
|
||||
#include <iostream>
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
static void help()
|
||||
{
|
||||
cout << "\nThis program demonstrates the cascade recognizer. Now you can use Haar or LBP features.\n"
|
||||
"This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n"
|
||||
"It's most known use is for faces.\n"
|
||||
"Usage:\n"
|
||||
"./ufacedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"
|
||||
" [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n"
|
||||
" [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n"
|
||||
" [--try-flip]\n"
|
||||
" [filename|camera_index]\n\n"
|
||||
"see facedetect.cmd for one call:\n"
|
||||
"./ufacedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n"
|
||||
"During execution:\n\tHit any key to quit.\n"
|
||||
"\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
|
||||
}
|
||||
|
||||
void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
CascadeClassifier& nestedCascade,
|
||||
double scale, bool tryflip );
|
||||
|
||||
int main( int argc, const char** argv )
|
||||
{
|
||||
VideoCapture capture;
|
||||
UMat frame, image;
|
||||
Mat canvas;
|
||||
|
||||
string inputName;
|
||||
bool tryflip;
|
||||
|
||||
CascadeClassifier cascade, nestedCascade;
|
||||
double scale;
|
||||
|
||||
cv::CommandLineParser parser(argc, argv,
|
||||
"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
|
||||
"{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
|
||||
"{help h ||}{scale|1|}{try-flip||}{@filename||}"
|
||||
);
|
||||
if (parser.has("help"))
|
||||
{
|
||||
help();
|
||||
return 0;
|
||||
}
|
||||
string cascadeName = samples::findFile(parser.get<string>("cascade"));
|
||||
string nestedCascadeName = samples::findFileOrKeep(parser.get<string>("nested-cascade"));
|
||||
scale = parser.get<double>("scale");
|
||||
tryflip = parser.has("try-flip");
|
||||
inputName = parser.get<string>("@filename");
|
||||
if ( !parser.check())
|
||||
{
|
||||
parser.printErrors();
|
||||
help();
|
||||
return -1;
|
||||
}
|
||||
|
||||
if ( !nestedCascade.load( nestedCascadeName ) )
|
||||
cerr << "WARNING: Could not load classifier cascade for nested objects: " << nestedCascadeName << endl;
|
||||
if( !cascade.load( cascadeName ) )
|
||||
{
|
||||
cerr << "ERROR: Could not load classifier cascade: " << cascadeName << endl;
|
||||
help();
|
||||
return -1;
|
||||
}
|
||||
|
||||
cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl;
|
||||
|
||||
if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) )
|
||||
{
|
||||
int camera = inputName.empty() ? 0 : inputName[0] - '0';
|
||||
if(!capture.open(camera))
|
||||
cout << "Capture from camera #" << camera << " didn't work" << endl;
|
||||
}
|
||||
else
|
||||
{
|
||||
inputName = samples::findFileOrKeep(inputName);
|
||||
imread(inputName, IMREAD_COLOR).copyTo(image);
|
||||
if( image.empty() )
|
||||
{
|
||||
if(!capture.open( inputName ))
|
||||
cout << "Could not read " << inputName << endl;
|
||||
}
|
||||
}
|
||||
|
||||
if( capture.isOpened() )
|
||||
{
|
||||
cout << "Video capturing has been started ..." << endl;
|
||||
for(;;)
|
||||
{
|
||||
capture >> frame;
|
||||
if( frame.empty() )
|
||||
break;
|
||||
|
||||
detectAndDraw( frame, canvas, cascade, nestedCascade, scale, tryflip );
|
||||
|
||||
char c = (char)waitKey(10);
|
||||
if( c == 27 || c == 'q' || c == 'Q' )
|
||||
break;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
cout << "Detecting face(s) in " << inputName << endl;
|
||||
if( !image.empty() )
|
||||
{
|
||||
detectAndDraw( image, canvas, cascade, nestedCascade, scale, tryflip );
|
||||
waitKey(0);
|
||||
}
|
||||
else if( !inputName.empty() )
|
||||
{
|
||||
/* assume it is a text file containing the
|
||||
list of the image filenames to be processed - one per line */
|
||||
FILE* f = fopen( inputName.c_str(), "rt" );
|
||||
if( f )
|
||||
{
|
||||
char buf[1000+1];
|
||||
while( fgets( buf, 1000, f ) )
|
||||
{
|
||||
int len = (int)strlen(buf);
|
||||
while( len > 0 && isspace(buf[len-1]) )
|
||||
len--;
|
||||
buf[len] = '\0';
|
||||
cout << "file " << buf << endl;
|
||||
imread(samples::findFile(buf), IMREAD_COLOR).copyTo(image);
|
||||
if( !image.empty() )
|
||||
{
|
||||
detectAndDraw( image, canvas, cascade, nestedCascade, scale, tryflip );
|
||||
char c = (char)waitKey(0);
|
||||
if( c == 27 || c == 'q' || c == 'Q' )
|
||||
break;
|
||||
}
|
||||
else
|
||||
{
|
||||
cerr << "Aw snap, couldn't read image " << buf << endl;
|
||||
}
|
||||
}
|
||||
fclose(f);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
CascadeClassifier& nestedCascade,
|
||||
double scale, bool tryflip )
|
||||
{
|
||||
double t = 0;
|
||||
vector<Rect> faces, faces2;
|
||||
const static Scalar colors[] =
|
||||
{
|
||||
Scalar(255,0,0),
|
||||
Scalar(255,128,0),
|
||||
Scalar(255,255,0),
|
||||
Scalar(0,255,0),
|
||||
Scalar(0,128,255),
|
||||
Scalar(0,255,255),
|
||||
Scalar(0,0,255),
|
||||
Scalar(255,0,255)
|
||||
};
|
||||
static UMat gray, smallImg;
|
||||
|
||||
t = (double)getTickCount();
|
||||
|
||||
cvtColor( img, gray, COLOR_BGR2GRAY );
|
||||
double fx = 1 / scale;
|
||||
resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT );
|
||||
equalizeHist( smallImg, smallImg );
|
||||
|
||||
cascade.detectMultiScale( smallImg, faces,
|
||||
1.1, 3, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
|CASCADE_SCALE_IMAGE,
|
||||
Size(30, 30) );
|
||||
if( tryflip )
|
||||
{
|
||||
flip(smallImg, smallImg, 1);
|
||||
cascade.detectMultiScale( smallImg, faces2,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
|CASCADE_SCALE_IMAGE,
|
||||
Size(30, 30) );
|
||||
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r )
|
||||
{
|
||||
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
|
||||
}
|
||||
}
|
||||
t = (double)getTickCount() - t;
|
||||
img.copyTo(canvas);
|
||||
|
||||
double fps = getTickFrequency()/t;
|
||||
static double avgfps = 0;
|
||||
static int nframes = 0;
|
||||
nframes++;
|
||||
double alpha = nframes > 50 ? 0.01 : 1./nframes;
|
||||
avgfps = avgfps*(1-alpha) + fps*alpha;
|
||||
|
||||
putText(canvas, cv::format("OpenCL: %s, fps: %.1f", ocl::useOpenCL() ? "ON" : "OFF", avgfps), Point(50, 30),
|
||||
FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0,255,0), 2);
|
||||
|
||||
for ( size_t i = 0; i < faces.size(); i++ )
|
||||
{
|
||||
Rect r = faces[i];
|
||||
vector<Rect> nestedObjects;
|
||||
Point center;
|
||||
Scalar color = colors[i%8];
|
||||
int radius;
|
||||
|
||||
double aspect_ratio = (double)r.width/r.height;
|
||||
if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
|
||||
{
|
||||
center.x = cvRound((r.x + r.width*0.5)*scale);
|
||||
center.y = cvRound((r.y + r.height*0.5)*scale);
|
||||
radius = cvRound((r.width + r.height)*0.25*scale);
|
||||
circle( canvas, center, radius, color, 3, 8, 0 );
|
||||
}
|
||||
else
|
||||
rectangle( canvas, Point(cvRound(r.x*scale), cvRound(r.y*scale)),
|
||||
Point(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)),
|
||||
color, 3, 8, 0);
|
||||
if( nestedCascade.empty() )
|
||||
continue;
|
||||
UMat smallImgROI = smallImg(r);
|
||||
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
//|CASCADE_DO_CANNY_PRUNING
|
||||
|CASCADE_SCALE_IMAGE,
|
||||
Size(30, 30) );
|
||||
|
||||
for ( size_t j = 0; j < nestedObjects.size(); j++ )
|
||||
{
|
||||
Rect nr = nestedObjects[j];
|
||||
center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale);
|
||||
center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale);
|
||||
radius = cvRound((nr.width + nr.height)*0.25*scale);
|
||||
circle( canvas, center, radius, color, 3, 8, 0 );
|
||||
}
|
||||
}
|
||||
imshow( "result", canvas );
|
||||
}
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
#include "MainPage.g.h"
|
||||
#include <opencv2\core\core.hpp>
|
||||
#include <opencv2\objdetect.hpp>
|
||||
#include <opencv2\xobjdetect.hpp>
|
||||
|
||||
|
||||
namespace FaceDetection
|
||||
|
||||
+1
-1
@@ -28,7 +28,7 @@
|
||||
|
||||
#include <opencv2/core.hpp>
|
||||
#include <opencv2/imgproc.hpp>
|
||||
#include <opencv2/objdetect.hpp>
|
||||
#include <opencv2/xobjdetect.hpp>
|
||||
#include <opencv2/features2d.hpp>
|
||||
#include <opencv2/videoio.hpp>
|
||||
#include <opencv2/videoio/cap_winrt.hpp>
|
||||
|
||||
Reference in New Issue
Block a user