718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
353 lines
9.6 KiB
C++
353 lines
9.6 KiB
C++
#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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