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
@@ -1,316 +0,0 @@
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// WARNING: this sample is under construction! Use it on your own risk.
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#if defined _MSC_VER && _MSC_VER >= 1400
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#pragma warning(disable : 4100)
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#endif
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#include <iostream>
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#include <iomanip>
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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/cudaobjdetect.hpp"
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#include "opencv2/cudaimgproc.hpp"
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#include "opencv2/cudawarping.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::cuda;
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static void help()
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{
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cout << "Usage: ./cascadeclassifier \n\t--cascade <cascade_file>\n\t(<image>|--video <video>|--camera <camera_id>)\n"
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"Using OpenCV version " << CV_VERSION << endl << endl;
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}
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static void convertAndResize(const Mat& src, Mat& gray, Mat& resized, double scale)
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{
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if (src.channels() == 3)
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{
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cv::cvtColor( src, gray, COLOR_BGR2GRAY );
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}
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else
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{
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gray = src;
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}
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Size sz(cvRound(gray.cols * scale), cvRound(gray.rows * scale));
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if (scale != 1)
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{
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cv::resize(gray, resized, sz);
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}
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else
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{
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resized = gray;
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}
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}
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static void convertAndResize(const GpuMat& src, GpuMat& gray, GpuMat& resized, double scale)
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{
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if (src.channels() == 3)
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{
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cv::cuda::cvtColor( src, gray, COLOR_BGR2GRAY );
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}
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else
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{
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gray = src;
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}
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Size sz(cvRound(gray.cols * scale), cvRound(gray.rows * scale));
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if (scale != 1)
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{
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cv::cuda::resize(gray, resized, sz);
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}
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else
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{
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resized = gray;
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}
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}
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static void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
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{
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int fontFace = FONT_HERSHEY_DUPLEX;
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double fontScale = 0.8;
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int fontThickness = 2;
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Size fontSize = cv::getTextSize("T[]", fontFace, fontScale, fontThickness, 0);
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Point org;
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org.x = 1;
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org.y = 3 * fontSize.height * (lineOffsY + 1) / 2;
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putText(img, ss, org, fontFace, fontScale, Scalar(0,0,0), 5*fontThickness/2, 16);
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putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
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}
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static void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
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{
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Scalar fontColorRed = Scalar(255,0,0);
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Scalar fontColorNV = Scalar(118,185,0);
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ostringstream ss;
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ss << "FPS = " << setprecision(1) << fixed << fps;
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matPrint(canvas, 0, fontColorRed, ss.str());
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ss.str("");
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ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
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(bGpu ? "GPU, " : "CPU, ") <<
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(bLargestFace ? "OneFace, " : "MultiFace, ") <<
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(bFilter ? "Filter:ON" : "Filter:OFF");
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matPrint(canvas, 1, fontColorRed, ss.str());
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// by Anatoly. MacOS fix. ostringstream(const string&) is a private
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// matPrint(canvas, 2, fontColorNV, ostringstream("Space - switch GPU / CPU"));
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if (bHelp)
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{
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matPrint(canvas, 2, fontColorNV, "Space - switch GPU / CPU");
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matPrint(canvas, 3, fontColorNV, "M - switch OneFace / MultiFace");
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matPrint(canvas, 4, fontColorNV, "F - toggle rectangles Filter");
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matPrint(canvas, 5, fontColorNV, "H - toggle hotkeys help");
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matPrint(canvas, 6, fontColorNV, "1/Q - increase/decrease scale");
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}
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else
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{
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matPrint(canvas, 2, fontColorNV, "H - toggle hotkeys help");
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}
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}
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int main(int argc, const char *argv[])
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{
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if (argc == 1)
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{
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help();
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return -1;
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}
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if (getCudaEnabledDeviceCount() == 0)
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{
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return cerr << "No GPU found or the library is compiled without CUDA support" << endl, -1;
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}
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cv::cuda::printShortCudaDeviceInfo(cv::cuda::getDevice());
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string cascadeName;
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string inputName;
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bool isInputImage = false;
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bool isInputVideo = false;
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bool isInputCamera = false;
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for (int i = 1; i < argc; ++i)
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{
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if (string(argv[i]) == "--cascade")
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cascadeName = argv[++i];
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else if (string(argv[i]) == "--video")
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{
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inputName = argv[++i];
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isInputVideo = true;
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}
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else if (string(argv[i]) == "--camera")
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{
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inputName = argv[++i];
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isInputCamera = true;
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}
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else if (string(argv[i]) == "--help")
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{
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help();
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return -1;
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}
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else if (!isInputImage)
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{
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inputName = argv[i];
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isInputImage = true;
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}
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else
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{
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cout << "Unknown key: " << argv[i] << endl;
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return -1;
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}
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}
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Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(cascadeName);
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cv::CascadeClassifier cascade_cpu;
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if (!cascade_cpu.load(cascadeName))
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{
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return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName << "\"" << endl, help(), -1;
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}
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VideoCapture capture;
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Mat image;
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if (isInputImage)
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{
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image = imread(inputName);
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CV_Assert(!image.empty());
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}
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else if (isInputVideo)
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{
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capture.open(inputName);
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CV_Assert(capture.isOpened());
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}
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else
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{
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capture.open(atoi(inputName.c_str()));
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CV_Assert(capture.isOpened());
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}
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namedWindow("result", 1);
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Mat frame, frame_cpu, gray_cpu, resized_cpu, frameDisp;
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vector<Rect> faces;
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GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
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/* parameters */
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bool useGPU = true;
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double scaleFactor = 1.0;
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bool findLargestObject = false;
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bool filterRects = true;
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bool helpScreen = false;
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for (;;)
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{
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if (isInputCamera || isInputVideo)
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{
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capture >> frame;
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if (frame.empty())
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{
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break;
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}
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}
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(image.empty() ? frame : image).copyTo(frame_cpu);
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frame_gpu.upload(image.empty() ? frame : image);
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convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
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convertAndResize(frame_cpu, gray_cpu, resized_cpu, scaleFactor);
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TickMeter tm;
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tm.start();
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if (useGPU)
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{
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cascade_gpu->setFindLargestObject(findLargestObject);
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cascade_gpu->setScaleFactor(1.2);
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cascade_gpu->setMinNeighbors((filterRects || findLargestObject) ? 4 : 0);
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cascade_gpu->detectMultiScale(resized_gpu, facesBuf_gpu);
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cascade_gpu->convert(facesBuf_gpu, faces);
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}
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else
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{
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Size minSize = cascade_gpu->getClassifierSize();
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cascade_cpu.detectMultiScale(resized_cpu, faces, 1.2,
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(filterRects || findLargestObject) ? 4 : 0,
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(findLargestObject ? CASCADE_FIND_BIGGEST_OBJECT : 0)
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| CASCADE_SCALE_IMAGE,
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minSize);
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}
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for (size_t i = 0; i < faces.size(); ++i)
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{
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rectangle(resized_cpu, faces[i], Scalar(255));
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}
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tm.stop();
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double detectionTime = tm.getTimeMilli();
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double fps = 1000 / detectionTime;
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//print detections to console
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cout << setfill(' ') << setprecision(2);
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cout << setw(6) << fixed << fps << " FPS, " << faces.size() << " det";
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if ((filterRects || findLargestObject) && !faces.empty())
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{
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for (size_t i = 0; i < faces.size(); ++i)
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{
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cout << ", [" << setw(4) << faces[i].x
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<< ", " << setw(4) << faces[i].y
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<< ", " << setw(4) << faces[i].width
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<< ", " << setw(4) << faces[i].height << "]";
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}
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}
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cout << endl;
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cv::cvtColor(resized_cpu, frameDisp, COLOR_GRAY2BGR);
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displayState(frameDisp, helpScreen, useGPU, findLargestObject, filterRects, fps);
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imshow("result", frameDisp);
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char key = (char)waitKey(5);
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if (key == 27)
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{
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break;
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}
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switch (key)
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{
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case ' ':
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useGPU = !useGPU;
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break;
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case 'm':
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case 'M':
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findLargestObject = !findLargestObject;
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break;
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case 'f':
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case 'F':
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filterRects = !filterRects;
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break;
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case '1':
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scaleFactor *= 1.05;
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break;
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case 'q':
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case 'Q':
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scaleFactor /= 1.05;
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break;
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case 'h':
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case 'H':
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helpScreen = !helpScreen;
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break;
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}
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}
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return 0;
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}
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@@ -1,552 +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/utility.hpp>
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#include "opencv2/cudaobjdetect.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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bool help_showed = false;
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class Args
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{
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public:
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Args();
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static Args read(int argc, char** argv);
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string src;
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bool src_is_folder;
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bool src_is_video;
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bool src_is_camera;
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int camera_id;
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bool svm_load;
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string svm;
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bool write_video;
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string dst_video;
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double dst_video_fps;
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bool make_gray;
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bool resize_src;
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int width, height;
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double scale;
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int nlevels;
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int gr_threshold;
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double hit_threshold;
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bool hit_threshold_auto;
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int win_width;
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int win_stride_width, win_stride_height;
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int block_width;
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int block_stride_width, block_stride_height;
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int cell_width;
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int nbins;
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bool gamma_corr;
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};
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class App
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{
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public:
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App(const Args& s);
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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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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 use_gpu;
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bool make_gray;
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double scale;
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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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};
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static void printHelp()
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{
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cout << "Histogram of Oriented Gradients descriptor and detector sample.\n"
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<< "\nUsage: hog\n"
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<< " (<image>|--video <vide>|--camera <camera_id>) # frames source\n"
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<< " or"
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<< " (--folder <folder_path>) # load images from folder\n"
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<< " [--svm <file> # load svm file"
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<< " [--make_gray <true/false>] # convert image to gray one or not\n"
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<< " [--resize_src <true/false>] # do resize of the source image or not\n"
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<< " [--width <int>] # resized image width\n"
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<< " [--height <int>] # resized image height\n"
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<< " [--hit_threshold <double>] # classifying plane distance threshold (0.0 usually)\n"
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<< " [--scale <double>] # HOG window scale factor\n"
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<< " [--nlevels <int>] # max number of HOG window scales\n"
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<< " [--win_width <int>] # width of the window\n"
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<< " [--win_stride_width <int>] # distance by OX axis between neighbour wins\n"
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<< " [--win_stride_height <int>] # distance by OY axis between neighbour wins\n"
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<< " [--block_width <int>] # width of the block\n"
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<< " [--block_stride_width <int>] # distance by 0X axis between neighbour blocks\n"
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<< " [--block_stride_height <int>] # distance by 0Y axis between neighbour blocks\n"
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<< " [--cell_width <int>] # width of the cell\n"
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<< " [--nbins <int>] # number of bins\n"
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<< " [--gr_threshold <int>] # merging similar rects constant\n"
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<< " [--gamma_correct <int>] # do gamma correction or not\n"
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<< " [--write_video <bool>] # write video or not\n"
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<< " [--dst_video <path>] # output video path\n"
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<< " [--dst_video_fps <double>] # output video fps\n";
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help_showed = true;
|
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}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
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try
|
||||
{
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Args args;
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if (argc < 2)
|
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{
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printHelp();
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args.camera_id = 0;
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args.src_is_camera = true;
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||||
}
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else
|
||||
{
|
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args = Args::read(argc, argv);
|
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if (help_showed)
|
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return -1;
|
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}
|
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App app(args);
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app.run();
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||||
}
|
||||
catch (const Exception& e) { return cout << "error: " << e.what() << endl, 1; }
|
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catch (const exception& e) { return cout << "error: " << e.what() << endl, 1; }
|
||||
catch(...) { return cout << "unknown exception" << endl, 1; }
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return 0;
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}
|
||||
|
||||
|
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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.;
|
||||
|
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make_gray = false;
|
||||
|
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resize_src = false;
|
||||
width = 640;
|
||||
height = 480;
|
||||
|
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scale = 1.05;
|
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nlevels = 13;
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gr_threshold = 8;
|
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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();
|
||||
}
|
||||
Reference in New Issue
Block a user