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7452eef6e9
For current OpenCV-CL architecture, if the data buffer
allocated in UMat are cpu buffer(not ocl buffer) under
cpu mode, and then pass this UMat to an OpenCL kernel
as an argument, the OpenCL path will fail and fallback
to cpu mode. Take HOGDescriptor::oclSvmDetector as an example:
ocl::setUseOpenCL(false);
//data allocated in hog.oclSvmDetector will be cpu buffer
hog.setSVMDetector(HOGDescriptor::getDaimlerPeopleDetector());
ocl::setUseOpenCL(true);
//We enabled OpenCL, but hog.oclSvmDetector are cpu buffer,
//so it will fail in the function ocl_classify_hists
//when reach to this line
//idx = k.set(idx, ocl::KernelArg::PtrReadOnly(detector));
hog.detectMultiScale(img, found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
Similar problems heppen on img_aux and img. So we should re-define
or re-set these UMat when do mode switch (CPU -> OpenCL) in order
to make their data be allocated by ocl and then OpenCL path will
succeed.
Signed-off-by: Chuanbo Weng <chuanbo.weng@intel.com>
382 lines
11 KiB
C++
382 lines
11 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/video.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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string message() const;
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// This function test if gpu_rst matches cpu_rst.
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// If the two vectors are not equal, it will return the difference in vector size
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// Else if will return
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// (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
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double checkRectSimilarity(Size sz,
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std::vector<Rect>& cpu_rst,
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std::vector<Rect>& gpu_rst);
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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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bool use_ocl;
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bool ocl_switch;
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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 | false | 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 | 768x576.avi | use video as input }"
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"{ g gray | false | 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 | | 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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cout << "Usage : hog [options]" << endl;
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cout << "Available options:" << endl;
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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 = 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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use_ocl = ocl::useOpenCL();
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ocl_switch = true;
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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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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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Mat 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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if(ocl_switch){
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hog.setSVMDetector( HOGDescriptor::getDaimlerPeopleDetector() );
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ocl_switch = false;
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}
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UMat img_aux, img;
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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);
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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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if(use_ocl)
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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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else
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hog.detectMultiScale(img.getMat(ACCESS_READ), 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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Rect r = found[i];
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rectangle(img_to_show, r.tl(), r.br(), Scalar(0, 255, 0), 3);
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}
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putText(img_to_show, use_ocl ? "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!="") // wirte 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.getMat(ACCESS_READ);
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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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ocl_switch = true;
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use_ocl = ocl::useOpenCL();
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cout << "Switched to " << (use_ocl ? "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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