mirror of
https://github.com/opencv/opencv.git
synced 2026-07-29 15:23:05 +04:00
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
@@ -1,352 +0,0 @@
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#include <iostream>
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#include <fstream>
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#include <string>
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#include <sstream>
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#include <iomanip>
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#include <stdexcept>
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#include <opencv2/core/ocl.hpp>
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#include <opencv2/core/utility.hpp>
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#include "opencv2/imgcodecs.hpp"
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/objdetect.hpp>
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#include <opencv2/imgproc.hpp>
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using namespace std;
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using namespace cv;
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class App
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{
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public:
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App(CommandLineParser& cmd);
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void run();
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void handleKey(char key);
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void hogWorkBegin();
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void hogWorkEnd();
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string hogWorkFps() const;
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void workBegin();
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void workEnd();
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string workFps() const;
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private:
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App operator=(App&);
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//Args args;
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bool running;
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bool make_gray;
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double scale;
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double resize_scale;
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int win_width;
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int win_stride_width, win_stride_height;
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int gr_threshold;
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int nlevels;
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double hit_threshold;
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bool gamma_corr;
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int64 hog_work_begin;
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double hog_work_fps;
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int64 work_begin;
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double work_fps;
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string img_source;
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string vdo_source;
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string output;
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int camera_id;
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bool write_once;
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};
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int main(int argc, char** argv)
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{
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const char* keys =
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"{ h help | | print help message }"
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"{ i input | | specify input image}"
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"{ c camera | -1 | enable camera capturing }"
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"{ v video | vtest.avi | use video as input }"
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"{ g gray | | convert image to gray one or not}"
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"{ s scale | 1.0 | resize the image before detect}"
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"{ o output | output.avi | specify output path when input is images}";
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CommandLineParser cmd(argc, argv, keys);
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if (cmd.has("help"))
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{
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cmd.printMessage();
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return EXIT_SUCCESS;
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}
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App app(cmd);
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try
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{
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app.run();
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}
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catch (const Exception& e)
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{
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return cout << "error: " << e.what() << endl, 1;
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}
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catch (const exception& e)
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{
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return cout << "error: " << e.what() << endl, 1;
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}
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catch(...)
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{
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return cout << "unknown exception" << endl, 1;
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}
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return EXIT_SUCCESS;
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}
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App::App(CommandLineParser& cmd)
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{
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cout << "\nControls:\n"
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<< "\tESC - exit\n"
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<< "\tm - change mode GPU <-> CPU\n"
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<< "\tg - convert image to gray or not\n"
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<< "\to - save output image once, or switch on/off video save\n"
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<< "\t1/q - increase/decrease HOG scale\n"
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<< "\t2/w - increase/decrease levels count\n"
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<< "\t3/e - increase/decrease HOG group threshold\n"
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<< "\t4/r - increase/decrease hit threshold\n"
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<< endl;
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make_gray = cmd.has("gray");
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resize_scale = cmd.get<double>("s");
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vdo_source = samples::findFileOrKeep(cmd.get<string>("v"));
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img_source = cmd.get<string>("i");
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output = cmd.get<string>("o");
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camera_id = cmd.get<int>("c");
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win_width = 48;
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win_stride_width = 8;
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win_stride_height = 8;
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gr_threshold = 8;
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nlevels = 13;
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hit_threshold = 1.4;
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scale = 1.05;
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gamma_corr = true;
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write_once = false;
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cout << "Group threshold: " << gr_threshold << endl;
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cout << "Levels number: " << nlevels << endl;
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cout << "Win width: " << win_width << endl;
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cout << "Win stride: (" << win_stride_width << ", " << win_stride_height << ")\n";
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cout << "Hit threshold: " << hit_threshold << endl;
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cout << "Gamma correction: " << gamma_corr << endl;
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cout << endl;
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}
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void App::run()
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{
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running = true;
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VideoWriter video_writer;
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Size win_size(win_width, win_width * 2);
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Size win_stride(win_stride_width, win_stride_height);
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// Create HOG descriptors and detectors here
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HOGDescriptor hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9, 1, -1,
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HOGDescriptor::L2Hys, 0.2, gamma_corr, cv::HOGDescriptor::DEFAULT_NLEVELS);
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hog.setSVMDetector( HOGDescriptor::getDaimlerPeopleDetector() );
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while (running)
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{
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VideoCapture vc;
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UMat frame;
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if (vdo_source!="")
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{
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vc.open(vdo_source.c_str());
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if (!vc.isOpened())
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throw runtime_error(string("can't open video file: " + vdo_source));
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vc >> frame;
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}
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else if (camera_id != -1)
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{
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vc.open(camera_id);
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if (!vc.isOpened())
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{
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stringstream msg;
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msg << "can't open camera: " << camera_id;
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throw runtime_error(msg.str());
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}
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vc >> frame;
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}
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else
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{
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imread(img_source).copyTo(frame);
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if (frame.empty())
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throw runtime_error(string("can't open image file: " + img_source));
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}
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UMat img_aux, img, img_to_show;
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// Iterate over all frames
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while (running && !frame.empty())
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{
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workBegin();
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// Change format of the image
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if (make_gray) cvtColor(frame, img_aux, COLOR_BGR2GRAY );
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else frame.copyTo(img_aux);
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// Resize image
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if (abs(scale-1.0)>0.001)
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{
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Size sz((int)((double)img_aux.cols/resize_scale), (int)((double)img_aux.rows/resize_scale));
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resize(img_aux, img, sz, 0, 0, INTER_LINEAR_EXACT);
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}
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else img = img_aux;
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img.copyTo(img_to_show);
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hog.nlevels = nlevels;
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vector<Rect> found;
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// Perform HOG classification
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hogWorkBegin();
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hog.detectMultiScale(img, found, hit_threshold, win_stride,
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Size(0, 0), scale, gr_threshold);
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hogWorkEnd();
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// Draw positive classified windows
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for (size_t i = 0; i < found.size(); i++)
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{
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rectangle(img_to_show, found[i], Scalar(0, 255, 0), 3);
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}
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putText(img_to_show, ocl::useOpenCL() ? "Mode: OpenCL" : "Mode: CPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
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putText(img_to_show, "FPS (HOG only): " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
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putText(img_to_show, "FPS (total): " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
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imshow("opencv_hog", img_to_show);
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if (vdo_source!="" || camera_id!=-1) vc >> frame;
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workEnd();
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if (output!="" && write_once)
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{
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if (img_source!="") // write image
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{
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write_once = false;
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imwrite(output, img_to_show);
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}
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else //write video
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{
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if (!video_writer.isOpened())
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{
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video_writer.open(output, VideoWriter::fourcc('x','v','i','d'), 24,
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img_to_show.size(), true);
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if (!video_writer.isOpened())
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throw std::runtime_error("can't create video writer");
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}
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if (make_gray) cvtColor(img_to_show, img, COLOR_GRAY2BGR);
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else cvtColor(img_to_show, img, COLOR_BGRA2BGR);
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video_writer << img;
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}
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}
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handleKey((char)waitKey(3));
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}
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}
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}
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void App::handleKey(char key)
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{
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switch (key)
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{
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case 27:
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running = false;
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break;
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case 'm':
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case 'M':
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ocl::setUseOpenCL(!cv::ocl::useOpenCL());
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cout << "Switched to " << (ocl::useOpenCL() ? "OpenCL enabled" : "CPU") << " mode\n";
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break;
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case 'g':
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case 'G':
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make_gray = !make_gray;
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cout << "Convert image to gray: " << (make_gray ? "YES" : "NO") << endl;
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break;
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case '1':
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scale *= 1.05;
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cout << "Scale: " << scale << endl;
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break;
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case 'q':
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case 'Q':
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scale /= 1.05;
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cout << "Scale: " << scale << endl;
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break;
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case '2':
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nlevels++;
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cout << "Levels number: " << nlevels << endl;
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break;
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case 'w':
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case 'W':
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nlevels = max(nlevels - 1, 1);
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cout << "Levels number: " << nlevels << endl;
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break;
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case '3':
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gr_threshold++;
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cout << "Group threshold: " << gr_threshold << endl;
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break;
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case 'e':
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case 'E':
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gr_threshold = max(0, gr_threshold - 1);
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cout << "Group threshold: " << gr_threshold << endl;
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break;
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case '4':
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hit_threshold+=0.25;
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cout << "Hit threshold: " << hit_threshold << endl;
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break;
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case 'r':
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case 'R':
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hit_threshold = max(0.0, hit_threshold - 0.25);
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cout << "Hit threshold: " << hit_threshold << endl;
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break;
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case 'c':
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case 'C':
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gamma_corr = !gamma_corr;
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cout << "Gamma correction: " << gamma_corr << endl;
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break;
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case 'o':
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case 'O':
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write_once = !write_once;
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break;
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}
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}
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inline void App::hogWorkBegin()
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{
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hog_work_begin = getTickCount();
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}
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inline void App::hogWorkEnd()
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{
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int64 delta = getTickCount() - hog_work_begin;
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double freq = getTickFrequency();
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hog_work_fps = freq / delta;
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}
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inline string App::hogWorkFps() const
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{
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stringstream ss;
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ss << hog_work_fps;
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return ss.str();
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}
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inline void App::workBegin()
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{
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work_begin = getTickCount();
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}
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inline void App::workEnd()
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{
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int64 delta = getTickCount() - work_begin;
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double freq = getTickFrequency();
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work_fps = freq / delta;
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}
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inline string App::workFps() const
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{
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stringstream ss;
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ss << work_fps;
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return ss.str();
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}
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@@ -1,253 +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/core/ocl.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()
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{
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cout << "\nThis program demonstrates the cascade recognizer. Now 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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"./ufacedetect [--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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"see facedetect.cmd for one call:\n"
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"./ufacedetect --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( UMat& img, Mat& canvas, CascadeClassifier& cascade,
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CascadeClassifier& nestedCascade,
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double scale, bool tryflip );
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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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UMat frame, image;
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Mat canvas;
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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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"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
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"{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
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"{help h ||}{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();
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return 0;
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}
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string cascadeName = samples::findFile(parser.get<string>("cascade"));
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string nestedCascadeName = samples::findFileOrKeep(parser.get<string>("nested-cascade"));
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scale = parser.get<double>("scale");
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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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help();
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return -1;
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}
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if ( !nestedCascade.load( nestedCascadeName ) )
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cerr << "WARNING: Could not load classifier cascade for nested objects: " << nestedCascadeName << endl;
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if( !cascade.load( cascadeName ) )
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{
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cerr << "ERROR: Could not load classifier cascade: " << cascadeName << endl;
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help();
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return -1;
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}
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cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl;
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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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cout << "Capture from camera #" << camera << " didn't work" << endl;
|
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}
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else
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{
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inputName = samples::findFileOrKeep(inputName);
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imread(inputName, IMREAD_COLOR).copyTo(image);
|
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if( image.empty() )
|
||||
{
|
||||
if(!capture.open( inputName ))
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cout << "Could not read " << inputName << endl;
|
||||
}
|
||||
}
|
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|
||||
if( capture.isOpened() )
|
||||
{
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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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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 );
|
||||
}
|
||||
Reference in New Issue
Block a user