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Merge pull request #19078 from zihaomu:dasiamrpn_tracker_c_plus_plus
Add DaSiamRPN tracker sample of c++ version * add sample dasiamrpn_tracker of c++ version. * samples(dasiamrpn_tracker.cpp): apply clang-format - exclude "keys" variable * samples(dasiamrpn_tracker.cpp): coding style and UX fixes
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samples/dnn/dasiamrpn_tracker.cpp
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samples/dnn/dasiamrpn_tracker.cpp
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// DaSiamRPN tracker.
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// Original paper: https://arxiv.org/abs/1808.06048
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// Link to original repo: https://github.com/foolwood/DaSiamRPN
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// Links to onnx models:
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// - network: https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=0
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// - kernel_r1: https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=0
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// - kernel_cls1: https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=0
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#include <iostream>
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#include <cmath>
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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using namespace cv;
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using namespace cv::dnn;
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const char *keys =
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"{ help h | | Print help message }"
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"{ input i | | Full path to input video folder, the specific camera index. (empty for camera 0) }"
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"{ net | dasiamrpn_model.onnx | Path to onnx model of net}"
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"{ kernel_cls1 | dasiamrpn_kernel_cls1.onnx | Path to onnx model of kernel_r1 }"
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"{ kernel_r1 | dasiamrpn_kernel_r1.onnx | Path to onnx model of kernel_cls1 }"
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"{ backend | 0 | Choose one of computation backends: "
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"0: automatically (by default), "
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"1: Halide language (http://halide-lang.org/), "
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"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"3: OpenCV implementation }"
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"{ target | 0 | Choose one of target computation devices: "
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"0: CPU target (by default), "
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"1: OpenCL, "
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"2: OpenCL fp16 (half-float precision), "
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"3: VPU }"
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;
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// Initial parameters of the model
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struct trackerConfig
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{
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float windowInfluence = 0.43f;
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float lr = 0.4f;
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int scale = 8;
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bool swapRB = false;
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int totalStride = 8;
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float penaltyK = 0.055f;
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int exemplarSize = 127;
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int instanceSize = 271;
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float contextAmount = 0.5f;
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std::vector<float> ratios = { 0.33f, 0.5f, 1.0f, 2.0f, 3.0f };
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int anchorNum = int(ratios.size());
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Mat anchors;
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Mat windows;
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Scalar avgChans;
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Size imgSize = { 0, 0 };
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Rect2f targetBox = { 0, 0, 0, 0 };
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int scoreSize = (instanceSize - exemplarSize) / totalStride + 1;
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void update_scoreSize()
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{
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scoreSize = int((instanceSize - exemplarSize) / totalStride + 1);
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}
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};
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static void softmax(const Mat& src, Mat& dst);
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static void elementMax(Mat& src);
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static Mat generateHanningWindow(const trackerConfig& trackState);
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static Mat generateAnchors(trackerConfig& trackState);
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static Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans);
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static float trackerEval(Mat img, trackerConfig& trackState, Net& siamRPN);
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static void trackerInit(Mat img, trackerConfig& trackState, Net& siamRPN, Net& siamKernelR1, Net& siamKernelCL1);
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template <typename T> static
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T sizeCal(const T& w, const T& h)
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{
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T pad = (w + h) * T(0.5);
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T sz2 = (w + pad) * (h + pad);
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return sqrt(sz2);
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}
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template <>
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Mat sizeCal(const Mat& w, const Mat& h)
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{
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Mat pad = (w + h) * 0.5;
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Mat sz2 = (w + pad).mul((h + pad));
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cv::sqrt(sz2, sz2);
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return sz2;
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}
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static
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int run(int argc, char** argv)
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{
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// Parse command line arguments.
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CommandLineParser parser(argc, argv, keys);
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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std::string inputName = parser.get<String>("input");
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std::string net = parser.get<String>("net");
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std::string kernel_cls1 = parser.get<String>("kernel_cls1");
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std::string kernel_r1 = parser.get<String>("kernel_r1");
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int backend = parser.get<int>("backend");
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int target = parser.get<int>("target");
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// Read nets.
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Net siamRPN, siamKernelCL1, siamKernelR1;
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try
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{
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siamRPN = readNet(samples::findFile(net));
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siamKernelCL1 = readNet(samples::findFile(kernel_cls1));
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siamKernelR1 = readNet(samples::findFile(kernel_r1));
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}
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catch (const cv::Exception& ee)
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{
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std::cerr << "Exception: " << ee.what() << std::endl;
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std::cout << "Can't load the network by using the following files:" << std::endl;
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std::cout << "siamRPN : " << net << std::endl;
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std::cout << "siamKernelCL1 : " << kernel_cls1 << std::endl;
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std::cout << "siamKernelR1 : " << kernel_r1 << std::endl;
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return 2;
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}
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// Set model backend.
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siamRPN.setPreferableBackend(backend);
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siamRPN.setPreferableTarget(target);
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siamKernelR1.setPreferableBackend(backend);
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siamKernelR1.setPreferableTarget(target);
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siamKernelCL1.setPreferableBackend(backend);
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siamKernelCL1.setPreferableTarget(target);
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const std::string winName = "DaSiamRPN";
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namedWindow(winName, WINDOW_AUTOSIZE);
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// Open a video file or an image file or a camera stream.
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VideoCapture cap;
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if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1))
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{
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int c = inputName.empty() ? 0 : inputName[0] - '0';
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std::cout << "Trying to open camera #" << c << " ..." << std::endl;
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if (!cap.open(c))
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{
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std::cout << "Capture from camera #" << c << " didn't work. Specify -i=<video> parameter to read from video file" << std::endl;
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return 2;
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}
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}
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else if (inputName.size())
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{
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inputName = samples::findFileOrKeep(inputName);
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if (!cap.open(inputName))
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{
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std::cout << "Could not open: " << inputName << std::endl;
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return 2;
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}
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}
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// Read the first image.
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Mat image;
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cap >> image;
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if (image.empty())
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{
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std::cerr << "Can't capture frame!" << std::endl;
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return 2;
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}
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Mat image_select = image.clone();
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putText(image_select, "Select initial bounding box you want to track.", Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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putText(image_select, "And Press the ENTER key.", Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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Rect selectRect = selectROI(winName, image_select);
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std::cout << "ROI=" << selectRect << std::endl;
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trackerConfig trackState;
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trackState.update_scoreSize();
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trackState.targetBox = Rect2f(
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float(selectRect.x) + float(selectRect.width) * 0.5f, // FIXIT don't use center in Rect structures, it is confusing
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float(selectRect.y) + float(selectRect.height) * 0.5f,
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float(selectRect.width),
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float(selectRect.height)
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);
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// Set tracking template.
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trackerInit(image, trackState, siamRPN, siamKernelR1, siamKernelCL1);
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TickMeter tickMeter;
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for (int count = 0; ; ++count)
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{
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cap >> image;
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if (image.empty())
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{
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std::cerr << "Can't capture frame " << count << ". End of video stream?" << std::endl;
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break;
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}
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tickMeter.start();
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float score = trackerEval(image, trackState, siamRPN);
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tickMeter.stop();
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Rect rect = {
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int(trackState.targetBox.x - int(trackState.targetBox.width / 2)),
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int(trackState.targetBox.y - int(trackState.targetBox.height / 2)),
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int(trackState.targetBox.width),
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int(trackState.targetBox.height)
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};
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std::cout << "frame " << count <<
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": predicted score=" << score <<
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" rect=" << rect <<
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" time=" << tickMeter.getTimeMilli() << "ms" <<
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std::endl;
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Mat render_image = image.clone();
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rectangle(render_image, rect, Scalar(0, 255, 0), 2);
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std::string timeLabel = format("Inference time: %.2f ms", tickMeter.getTimeMilli());
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std::string scoreLabel = format("Score: %f", score);
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putText(render_image, timeLabel, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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putText(render_image, scoreLabel, Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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imshow(winName, render_image);
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tickMeter.reset();
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int c = waitKey(1);
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if (c == 27 /*ESC*/)
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break;
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}
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std::cout << "Exit" << std::endl;
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return 0;
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}
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Mat generateHanningWindow(const trackerConfig& trackState)
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{
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Mat baseWindows, HanningWindows;
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createHanningWindow(baseWindows, Size(trackState.scoreSize, trackState.scoreSize), CV_32F);
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baseWindows = baseWindows.reshape(0, { 1, trackState.scoreSize, trackState.scoreSize });
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HanningWindows = baseWindows.clone();
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for (int i = 1; i < trackState.anchorNum; i++)
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{
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HanningWindows.push_back(baseWindows);
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}
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return HanningWindows;
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}
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Mat generateAnchors(trackerConfig& trackState)
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{
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int totalStride = trackState.totalStride, scales = trackState.scale, scoreSize = trackState.scoreSize;
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std::vector<float> ratios = trackState.ratios;
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std::vector<Rect2f> baseAnchors;
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int anchorNum = int(ratios.size());
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int size = totalStride * totalStride;
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float ori = -(float(scoreSize / 2)) * float(totalStride);
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for (auto i = 0; i < anchorNum; i++)
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{
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int ws = int(sqrt(size / ratios[i]));
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int hs = int(ws * ratios[i]);
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float wws = float(ws) * scales;
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float hhs = float(hs) * scales;
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Rect2f anchor = { 0, 0, wws, hhs };
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baseAnchors.push_back(anchor);
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}
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int anchorIndex[] = { 0, 0, 0, 0 };
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const int sizes[] = { 4, (int)ratios.size(), scoreSize, scoreSize };
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Mat anchors(4, sizes, CV_32F);
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for (auto i = 0; i < scoreSize; i++)
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{
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for (auto j = 0; j < scoreSize; j++)
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{
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for (auto k = 0; k < anchorNum; k++)
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{
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anchorIndex[0] = 1, anchorIndex[1] = k, anchorIndex[2] = i, anchorIndex[3] = j;
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anchors.at<float>(anchorIndex) = ori + totalStride * i;
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anchorIndex[0] = 0;
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anchors.at<float>(anchorIndex) = ori + totalStride * j;
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anchorIndex[0] = 2;
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anchors.at<float>(anchorIndex) = baseAnchors[k].width;
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anchorIndex[0] = 3;
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anchors.at<float>(anchorIndex) = baseAnchors[k].height;
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}
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}
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}
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return anchors;
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}
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Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans)
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{
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Mat zCrop, dst;
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Size imgSize = img.size();
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float c = (originalSize + 1) / 2;
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float xMin = (float)cvRound(targetBox.x - c);
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float xMax = xMin + originalSize - 1;
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float yMin = (float)cvRound(targetBox.y - c);
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float yMax = yMin + originalSize - 1;
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int leftPad = (int)(fmax(0., -xMin));
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int topPad = (int)(fmax(0., -yMin));
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int rightPad = (int)(fmax(0., xMax - imgSize.width + 1));
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int bottomPad = (int)(fmax(0., yMax - imgSize.height + 1));
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xMin = xMin + leftPad;
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xMax = xMax + leftPad;
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yMax = yMax + topPad;
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yMin = yMin + topPad;
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if (topPad == 0 && bottomPad == 0 && leftPad == 0 && rightPad == 0)
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{
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img(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
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}
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else
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{
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copyMakeBorder(img, dst, topPad, bottomPad, leftPad, rightPad, BORDER_CONSTANT, avgChans);
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dst(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
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}
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return zCrop;
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}
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void softmax(const Mat& src, Mat& dst)
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{
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Mat maxVal;
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cv::max(src.row(1), src.row(0), maxVal);
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src.row(1) -= maxVal;
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src.row(0) -= maxVal;
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exp(src, dst);
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Mat sumVal = dst.row(0) + dst.row(1);
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dst.row(0) = dst.row(0) / sumVal;
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dst.row(1) = dst.row(1) / sumVal;
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}
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void elementMax(Mat& src)
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{
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int* p = src.size.p;
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int index[] = { 0, 0, 0, 0 };
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for (int n = 0; n < *p; n++)
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{
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for (int k = 0; k < *(p + 1); k++)
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{
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for (int i = 0; i < *(p + 2); i++)
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{
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for (int j = 0; j < *(p + 3); j++)
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{
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index[0] = n, index[1] = k, index[2] = i, index[3] = j;
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float& v = src.at<float>(index);
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v = fmax(v, 1.0f / v);
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}
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}
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}
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}
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}
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float trackerEval(Mat img, trackerConfig& trackState, Net& siamRPN)
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{
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Rect2f targetBox = trackState.targetBox;
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float wc = targetBox.height + trackState.contextAmount * (targetBox.width + targetBox.height);
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float hc = targetBox.width + trackState.contextAmount * (targetBox.width + targetBox.height);
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float sz = sqrt(wc * hc);
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float scaleZ = trackState.exemplarSize / sz;
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float searchSize = float((trackState.instanceSize - trackState.exemplarSize) / 2);
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float pad = searchSize / scaleZ;
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float sx = sz + 2 * pad;
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Mat xCrop = getSubwindow(img, targetBox, (float)cvRound(sx), trackState.avgChans);
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static Mat blob;
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std::vector<Mat> outs;
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std::vector<String> outNames;
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Mat delta, score;
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Mat sc, rc, penalty, pscore;
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blobFromImage(xCrop, blob, 1.0, Size(trackState.instanceSize, trackState.instanceSize), Scalar(), trackState.swapRB, false, CV_32F);
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siamRPN.setInput(blob);
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outNames = siamRPN.getUnconnectedOutLayersNames();
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siamRPN.forward(outs, outNames);
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delta = outs[0];
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score = outs[1];
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score = score.reshape(0, { 2, trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
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delta = delta.reshape(0, { 4, trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
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softmax(score, score);
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targetBox.width *= scaleZ;
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targetBox.height *= scaleZ;
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score = score.row(1);
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score = score.reshape(0, { 5, 19, 19 });
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// Post processing
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delta.row(0) = delta.row(0).mul(trackState.anchors.row(2)) + trackState.anchors.row(0);
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delta.row(1) = delta.row(1).mul(trackState.anchors.row(3)) + trackState.anchors.row(1);
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exp(delta.row(2), delta.row(2));
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delta.row(2) = delta.row(2).mul(trackState.anchors.row(2));
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exp(delta.row(3), delta.row(3));
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delta.row(3) = delta.row(3).mul(trackState.anchors.row(3));
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sc = sizeCal(delta.row(2), delta.row(3)) / sizeCal(targetBox.width, targetBox.height);
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elementMax(sc);
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rc = delta.row(2).mul(1 / delta.row(3));
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rc = (targetBox.width / targetBox.height) / rc;
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elementMax(rc);
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// Calculating the penalty
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exp(((rc.mul(sc) - 1.) * trackState.penaltyK * (-1.0)), penalty);
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penalty = penalty.reshape(0, { trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
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||||
|
||||
pscore = penalty.mul(score);
|
||||
pscore = pscore * (1.0 - trackState.windowInfluence) + trackState.windows * trackState.windowInfluence;
|
||||
|
||||
int bestID[] = { 0 };
|
||||
// Find the index of best score.
|
||||
minMaxIdx(pscore.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 }), 0, 0, 0, bestID);
|
||||
delta = delta.reshape(0, { 4, trackState.anchorNum * trackState.scoreSize * trackState.scoreSize });
|
||||
penalty = penalty.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 });
|
||||
score = score.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 });
|
||||
|
||||
int index[] = { 0, bestID[0] };
|
||||
Rect2f resBox = { 0, 0, 0, 0 };
|
||||
|
||||
resBox.x = delta.at<float>(index) / scaleZ;
|
||||
index[0] = 1;
|
||||
resBox.y = delta.at<float>(index) / scaleZ;
|
||||
index[0] = 2;
|
||||
resBox.width = delta.at<float>(index) / scaleZ;
|
||||
index[0] = 3;
|
||||
resBox.height = delta.at<float>(index) / scaleZ;
|
||||
|
||||
float lr = penalty.at<float>(bestID) * score.at<float>(bestID) * trackState.lr;
|
||||
|
||||
resBox.x = resBox.x + targetBox.x;
|
||||
resBox.y = resBox.y + targetBox.y;
|
||||
targetBox.width /= scaleZ;
|
||||
targetBox.height /= scaleZ;
|
||||
|
||||
resBox.width = targetBox.width * (1 - lr) + resBox.width * lr;
|
||||
resBox.height = targetBox.height * (1 - lr) + resBox.height * lr;
|
||||
|
||||
resBox.x = float(fmax(0., fmin(float(trackState.imgSize.width), resBox.x)));
|
||||
resBox.y = float(fmax(0., fmin(float(trackState.imgSize.height), resBox.y)));
|
||||
resBox.width = float(fmax(10., fmin(float(trackState.imgSize.width), resBox.width)));
|
||||
resBox.height = float(fmax(10., fmin(float(trackState.imgSize.height), resBox.height)));
|
||||
|
||||
trackState.targetBox = resBox;
|
||||
return score.at<float>(bestID);
|
||||
}
|
||||
|
||||
void trackerInit(Mat img, trackerConfig& trackState, Net& siamRPN, Net& siamKernelR1, Net& siamKernelCL1)
|
||||
{
|
||||
Rect2f targetBox = trackState.targetBox;
|
||||
Mat anchors = generateAnchors(trackState);
|
||||
trackState.anchors = anchors;
|
||||
|
||||
Mat windows = generateHanningWindow(trackState);
|
||||
|
||||
trackState.windows = windows;
|
||||
trackState.imgSize = img.size();
|
||||
|
||||
trackState.avgChans = mean(img);
|
||||
float wc = targetBox.width + trackState.contextAmount * (targetBox.width + targetBox.height);
|
||||
float hc = targetBox.height + trackState.contextAmount * (targetBox.width + targetBox.height);
|
||||
float sz = (float)cvRound(sqrt(wc * hc));
|
||||
|
||||
Mat zCrop = getSubwindow(img, targetBox, sz, trackState.avgChans);
|
||||
static Mat blob;
|
||||
|
||||
blobFromImage(zCrop, blob, 1.0, Size(trackState.exemplarSize, trackState.exemplarSize), Scalar(), trackState.swapRB, false, CV_32F);
|
||||
siamRPN.setInput(blob);
|
||||
Mat out1;
|
||||
siamRPN.forward(out1, "63");
|
||||
|
||||
siamKernelCL1.setInput(out1);
|
||||
siamKernelR1.setInput(out1);
|
||||
|
||||
Mat cls1 = siamKernelCL1.forward();
|
||||
Mat r1 = siamKernelR1.forward();
|
||||
std::vector<int> r1_shape = { 20, 256, 4, 4 }, cls1_shape = { 10, 256, 4, 4 };
|
||||
|
||||
siamRPN.setParam(siamRPN.getLayerId("65"), 0, r1.reshape(0, r1_shape));
|
||||
siamRPN.setParam(siamRPN.getLayerId("68"), 0, cls1.reshape(0, cls1_shape));
|
||||
}
|
||||
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
try
|
||||
{
|
||||
return run(argc, argv);
|
||||
}
|
||||
catch (const std::exception& e)
|
||||
{
|
||||
std::cerr << "FATAL: C++ exception: " << e.what() << std::endl;
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user