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Open Source Computer Vision Library
https://opencv.org/
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313 lines
8.6 KiB
313 lines
8.6 KiB
// 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/contrib/contrib.hpp" |
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#include "opencv2/objdetect/objdetect.hpp" |
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#include "opencv2/highgui/highgui.hpp" |
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#include "opencv2/imgproc/imgproc.hpp" |
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#include "opencv2/gpu/gpu.hpp" |
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using namespace std; |
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using namespace cv; |
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using namespace cv::gpu; |
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static void help() |
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{ |
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cout << "Usage: ./cascadeclassifier_gpu \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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template<class T> |
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void convertAndResize(const T& src, T& gray, T& resized, double scale) |
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{ |
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if (src.channels() == 3) |
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{ |
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cvtColor( src, gray, CV_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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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, CV_RGB(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 = CV_RGB(255,0,0); |
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Scalar fontColorNV = CV_RGB(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 GPU support" << endl, -1; |
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} |
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cv::gpu::printShortCudaDeviceInfo(cv::gpu::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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CascadeClassifier_GPU cascade_gpu; |
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if (!cascade_gpu.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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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, faces_downloaded, frameDisp; |
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vector<Rect> facesBuf_cpu; |
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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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int detections_num; |
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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.visualizeInPlace = true; |
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cascade_gpu.findLargestObject = findLargestObject; |
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detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2, |
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(filterRects || findLargestObject) ? 4 : 0); |
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facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded); |
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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, facesBuf_cpu, 1.2, |
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(filterRects || findLargestObject) ? 4 : 0, |
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(findLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0) |
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| CV_HAAR_SCALE_IMAGE, |
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minSize); |
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detections_num = (int)facesBuf_cpu.size(); |
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} |
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if (!useGPU && detections_num) |
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{ |
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for (int i = 0; i < detections_num; ++i) |
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{ |
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rectangle(resized_cpu, facesBuf_cpu[i], Scalar(255)); |
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} |
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} |
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if (useGPU) |
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{ |
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resized_gpu.download(resized_cpu); |
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for (int i = 0; i < detections_num; ++i) |
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{ |
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rectangle(resized_cpu, faces_downloaded.ptr<cv::Rect>()[i], Scalar(255)); |
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} |
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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, " << detections_num << " det"; |
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if ((filterRects || findLargestObject) && detections_num > 0) |
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{ |
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Rect *faceRects = useGPU ? faces_downloaded.ptr<Rect>() : &facesBuf_cpu[0]; |
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for (int i = 0; i < min(detections_num, 2); ++i) |
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{ |
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cout << ", [" << setw(4) << faceRects[i].x |
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<< ", " << setw(4) << faceRects[i].y |
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<< ", " << setw(4) << faceRects[i].width |
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<< ", " << setw(4) << faceRects[i].height << "]"; |
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} |
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} |
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cout << endl; |
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cvtColor(resized_cpu, frameDisp, CV_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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