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Merge pull request #20036 from APrigarina:tracking_api
Tracking API: added DaSiamRPN tracker * added dasiamrpn tracker * dasiamrpn: add test, rewrite sample * change python samples * fix tests * fix params
This commit is contained in:
Anna Prigarina
GitHub
parent
73ee01a7f4
commit
478663b08c
@ -818,6 +818,36 @@ public:
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//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
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};
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class CV_EXPORTS_W TrackerDaSiamRPN : public Tracker
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{
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protected:
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TrackerDaSiamRPN(); // use ::create()
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public:
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virtual ~TrackerDaSiamRPN() CV_OVERRIDE;
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struct CV_EXPORTS_W_SIMPLE Params
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{
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CV_WRAP Params();
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CV_PROP_RW std::string model;
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CV_PROP_RW std::string kernel_cls1;
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CV_PROP_RW std::string kernel_r1;
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CV_PROP_RW int backend;
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CV_PROP_RW int target;
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};
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/** @brief Constructor
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@param parameters DaSiamRPN parameters TrackerDaSiamRPN::Params
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*/
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static CV_WRAP
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Ptr<TrackerDaSiamRPN> create(const TrackerDaSiamRPN::Params& parameters = TrackerDaSiamRPN::Params());
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/** @brief Return tracking score
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*/
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CV_WRAP virtual float getTrackingScore() = 0;
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//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
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//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
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};
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//! @} video_track
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@ -1,4 +1,5 @@
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#ifdef HAVE_OPENCV_VIDEO
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typedef TrackerMIL::Params TrackerMIL_Params;
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typedef TrackerGOTURN::Params TrackerGOTURN_Params;
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typedef TrackerDaSiamRPN::Params TrackerDaSiamRPN_Params;
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#endif
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440
modules/video/src/tracking/tracker_dasiamrpn.cpp
Normal file
440
modules/video/src/tracking/tracker_dasiamrpn.cpp
Normal file
@ -0,0 +1,440 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "../precomp.hpp"
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#ifdef HAVE_OPENCV_DNN
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#include "opencv2/dnn.hpp"
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#endif
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namespace cv {
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TrackerDaSiamRPN::TrackerDaSiamRPN()
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{
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// nothing
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}
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TrackerDaSiamRPN::~TrackerDaSiamRPN()
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{
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// nothing
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}
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TrackerDaSiamRPN::Params::Params()
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{
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model = "dasiamrpn_model.onnx";
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kernel_cls1 = "dasiamrpn_kernel_cls1.onnx";
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kernel_r1 = "dasiamrpn_kernel_r1.onnx";
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#ifdef HAVE_OPENCV_DNN
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backend = dnn::DNN_BACKEND_DEFAULT;
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target = dnn::DNN_TARGET_CPU;
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#else
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backend = -1; // invalid value
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target = -1; // invalid value
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#endif
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}
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#ifdef HAVE_OPENCV_DNN
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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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class TrackerDaSiamRPNImpl : public TrackerDaSiamRPN
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{
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public:
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TrackerDaSiamRPNImpl(const TrackerDaSiamRPN::Params& parameters)
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: params(parameters)
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{
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siamRPN = dnn::readNet(params.model);
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siamKernelCL1 = dnn::readNet(params.kernel_cls1);
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siamKernelR1 = dnn::readNet(params.kernel_r1);
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CV_Assert(!siamRPN.empty());
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CV_Assert(!siamKernelCL1.empty());
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CV_Assert(!siamKernelR1.empty());
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siamRPN.setPreferableBackend(params.backend);
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siamRPN.setPreferableTarget(params.target);
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siamKernelR1.setPreferableBackend(params.backend);
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siamKernelR1.setPreferableTarget(params.target);
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siamKernelCL1.setPreferableBackend(params.backend);
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siamKernelCL1.setPreferableTarget(params.target);
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}
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void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
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bool update(InputArray image, Rect& boundingBox) CV_OVERRIDE;
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float getTrackingScore() CV_OVERRIDE;
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TrackerDaSiamRPN::Params params;
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protected:
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dnn::Net siamRPN, siamKernelR1, siamKernelCL1;
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Rect boundingBox_;
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Mat image_;
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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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float tracking_score;
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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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trackerConfig trackState;
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void softmax(const Mat& src, Mat& dst);
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void elementMax(Mat& src);
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Mat generateHanningWindow();
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Mat generateAnchors();
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Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans);
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void trackerInit(Mat img);
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void trackerEval(Mat img);
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};
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void TrackerDaSiamRPNImpl::init(InputArray image, const Rect& boundingBox)
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{
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image_ = image.getMat().clone();
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trackState.update_scoreSize();
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trackState.targetBox = Rect2f(
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float(boundingBox.x) + float(boundingBox.width) * 0.5f, // FIXIT don't use center in Rect structures, it is confusing
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float(boundingBox.y) + float(boundingBox.height) * 0.5f,
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float(boundingBox.width),
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float(boundingBox.height)
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);
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trackerInit(image_);
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}
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void TrackerDaSiamRPNImpl::trackerInit(Mat img)
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{
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Rect2f targetBox = trackState.targetBox;
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Mat anchors = generateAnchors();
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trackState.anchors = anchors;
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Mat windows = generateHanningWindow();
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trackState.windows = windows;
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trackState.imgSize = img.size();
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trackState.avgChans = mean(img);
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float wc = targetBox.width + trackState.contextAmount * (targetBox.width + targetBox.height);
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float hc = targetBox.height + trackState.contextAmount * (targetBox.width + targetBox.height);
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float sz = (float)cvRound(sqrt(wc * hc));
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Mat zCrop = getSubwindow(img, targetBox, sz, trackState.avgChans);
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Mat blob;
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dnn::blobFromImage(zCrop, blob, 1.0, Size(trackState.exemplarSize, trackState.exemplarSize), Scalar(), trackState.swapRB, false, CV_32F);
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siamRPN.setInput(blob);
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Mat out1;
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siamRPN.forward(out1, "63");
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siamKernelCL1.setInput(out1);
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siamKernelR1.setInput(out1);
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Mat cls1 = siamKernelCL1.forward();
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Mat r1 = siamKernelR1.forward();
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std::vector<int> r1_shape = { 20, 256, 4, 4 }, cls1_shape = { 10, 256, 4, 4 };
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siamRPN.setParam(siamRPN.getLayerId("65"), 0, r1.reshape(0, r1_shape));
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siamRPN.setParam(siamRPN.getLayerId("68"), 0, cls1.reshape(0, cls1_shape));
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}
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bool TrackerDaSiamRPNImpl::update(InputArray image, Rect& boundingBox)
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{
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image_ = image.getMat().clone();
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trackerEval(image_);
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boundingBox = {
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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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return true;
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}
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void TrackerDaSiamRPNImpl::trackerEval(Mat img)
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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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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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dnn::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);
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pscore = pscore * (1.0 - trackState.windowInfluence) + trackState.windows * trackState.windowInfluence;
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int bestID[] = { 0 };
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// Find the index of best score.
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minMaxIdx(pscore.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 }), 0, 0, 0, bestID);
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delta = delta.reshape(0, { 4, trackState.anchorNum * trackState.scoreSize * trackState.scoreSize });
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penalty = penalty.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 });
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score = score.reshape(0, { trackState.anchorNum * trackState.scoreSize * trackState.scoreSize, 1 });
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int index[] = { 0, bestID[0] };
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Rect2f resBox = { 0, 0, 0, 0 };
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resBox.x = delta.at<float>(index) / scaleZ;
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index[0] = 1;
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resBox.y = delta.at<float>(index) / scaleZ;
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index[0] = 2;
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resBox.width = delta.at<float>(index) / scaleZ;
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index[0] = 3;
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resBox.height = delta.at<float>(index) / scaleZ;
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float lr = penalty.at<float>(bestID) * score.at<float>(bestID) * trackState.lr;
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resBox.x = resBox.x + targetBox.x;
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resBox.y = resBox.y + targetBox.y;
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targetBox.width /= scaleZ;
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targetBox.height /= scaleZ;
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resBox.width = targetBox.width * (1 - lr) + resBox.width * lr;
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resBox.height = targetBox.height * (1 - lr) + resBox.height * lr;
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resBox.x = float(fmax(0., fmin(float(trackState.imgSize.width), resBox.x)));
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resBox.y = float(fmax(0., fmin(float(trackState.imgSize.height), resBox.y)));
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resBox.width = float(fmax(10., fmin(float(trackState.imgSize.width), resBox.width)));
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resBox.height = float(fmax(10., fmin(float(trackState.imgSize.height), resBox.height)));
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trackState.targetBox = resBox;
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trackState.tracking_score = score.at<float>(bestID);
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}
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float TrackerDaSiamRPNImpl::getTrackingScore()
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{
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return trackState.tracking_score;
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}
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void TrackerDaSiamRPNImpl::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 TrackerDaSiamRPNImpl::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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Mat TrackerDaSiamRPNImpl::generateHanningWindow()
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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 TrackerDaSiamRPNImpl::generateAnchors()
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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 TrackerDaSiamRPNImpl::getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans)
|
||||
{
|
||||
Mat zCrop, dst;
|
||||
Size imgSize = img.size();
|
||||
float c = (originalSize + 1) / 2;
|
||||
float xMin = (float)cvRound(targetBox.x - c);
|
||||
float xMax = xMin + originalSize - 1;
|
||||
float yMin = (float)cvRound(targetBox.y - c);
|
||||
float yMax = yMin + originalSize - 1;
|
||||
|
||||
int leftPad = (int)(fmax(0., -xMin));
|
||||
int topPad = (int)(fmax(0., -yMin));
|
||||
int rightPad = (int)(fmax(0., xMax - imgSize.width + 1));
|
||||
int bottomPad = (int)(fmax(0., yMax - imgSize.height + 1));
|
||||
|
||||
xMin = xMin + leftPad;
|
||||
xMax = xMax + leftPad;
|
||||
yMax = yMax + topPad;
|
||||
yMin = yMin + topPad;
|
||||
|
||||
if (topPad == 0 && bottomPad == 0 && leftPad == 0 && rightPad == 0)
|
||||
{
|
||||
img(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
|
||||
}
|
||||
else
|
||||
{
|
||||
copyMakeBorder(img, dst, topPad, bottomPad, leftPad, rightPad, BORDER_CONSTANT, avgChans);
|
||||
dst(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
|
||||
}
|
||||
|
||||
return zCrop;
|
||||
}
|
||||
Ptr<TrackerDaSiamRPN> TrackerDaSiamRPN::create(const TrackerDaSiamRPN::Params& parameters)
|
||||
{
|
||||
return makePtr<TrackerDaSiamRPNImpl>(parameters);
|
||||
}
|
||||
|
||||
#else // OPENCV_HAVE_DNN
|
||||
Ptr<TrackerDaSiamRPN> TrackerDaSiamRPN::create(const TrackerDaSiamRPN::Params& parameters)
|
||||
{
|
||||
(void)(parameters);
|
||||
CV_Error(cv::Error::StsNotImplemented, "to use GOTURN, the tracking module needs to be built with opencv_dnn !");
|
||||
}
|
||||
#endif // OPENCV_HAVE_DNN
|
||||
}
|
||||
@ -94,4 +94,36 @@ TEST(GOTURN, memory_usage)
|
||||
}
|
||||
}
|
||||
|
||||
TEST(DaSiamRPN, memory_usage)
|
||||
{
|
||||
cv::Rect roi(145, 70, 85, 85);
|
||||
|
||||
std::string model = cvtest::findDataFile("dnn/onnx/models/dasiamrpn_model.onnx", false);
|
||||
std::string kernel_r1 = cvtest::findDataFile("dnn/onnx/models/dasiamrpn_kernel_r1.onnx", false);
|
||||
std::string kernel_cls1 = cvtest::findDataFile("dnn/onnx/models/dasiamrpn_kernel_cls1.onnx", false);
|
||||
cv::TrackerDaSiamRPN::Params params;
|
||||
params.model = model;
|
||||
params.kernel_r1 = kernel_r1;
|
||||
params.kernel_cls1 = kernel_cls1;
|
||||
cv::Ptr<Tracker> tracker = TrackerDaSiamRPN::create(params);
|
||||
|
||||
string inputVideo = cvtest::findDataFile("tracking/david/data/david.webm");
|
||||
cv::VideoCapture video(inputVideo);
|
||||
ASSERT_TRUE(video.isOpened()) << inputVideo;
|
||||
|
||||
cv::Mat frame;
|
||||
video >> frame;
|
||||
ASSERT_FALSE(frame.empty()) << inputVideo;
|
||||
tracker->init(frame, roi);
|
||||
string ground_truth_bb;
|
||||
for (int nframes = 0; nframes < 15; ++nframes)
|
||||
{
|
||||
std::cout << "Frame: " << nframes << std::endl;
|
||||
video >> frame;
|
||||
bool res = tracker->update(frame, roi);
|
||||
ASSERT_TRUE(res);
|
||||
std::cout << "Predicted ROI: " << roi << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
}} // namespace opencv_test::
|
||||
|
||||
@ -4,6 +4,7 @@ set(OPENCV_DNN_SAMPLES_REQUIRED_DEPS
|
||||
opencv_core
|
||||
opencv_imgproc
|
||||
opencv_dnn
|
||||
opencv_video
|
||||
opencv_imgcodecs
|
||||
opencv_videoio
|
||||
opencv_highgui)
|
||||
|
||||
@ -12,6 +12,7 @@
|
||||
#include <opencv2/dnn.hpp>
|
||||
#include <opencv2/imgproc.hpp>
|
||||
#include <opencv2/highgui.hpp>
|
||||
#include <opencv2/video.hpp>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::dnn;
|
||||
@ -34,59 +35,6 @@ const char *keys =
|
||||
"3: VPU }"
|
||||
;
|
||||
|
||||
// Initial parameters of the model
|
||||
struct trackerConfig
|
||||
{
|
||||
float windowInfluence = 0.43f;
|
||||
float lr = 0.4f;
|
||||
int scale = 8;
|
||||
bool swapRB = false;
|
||||
int totalStride = 8;
|
||||
float penaltyK = 0.055f;
|
||||
int exemplarSize = 127;
|
||||
int instanceSize = 271;
|
||||
float contextAmount = 0.5f;
|
||||
std::vector<float> ratios = { 0.33f, 0.5f, 1.0f, 2.0f, 3.0f };
|
||||
int anchorNum = int(ratios.size());
|
||||
Mat anchors;
|
||||
Mat windows;
|
||||
Scalar avgChans;
|
||||
Size imgSize = { 0, 0 };
|
||||
Rect2f targetBox = { 0, 0, 0, 0 };
|
||||
int scoreSize = (instanceSize - exemplarSize) / totalStride + 1;
|
||||
|
||||
void update_scoreSize()
|
||||
{
|
||||
scoreSize = int((instanceSize - exemplarSize) / totalStride + 1);
|
||||
}
|
||||
};
|
||||
|
||||
static void softmax(const Mat& src, Mat& dst);
|
||||
static void elementMax(Mat& src);
|
||||
static Mat generateHanningWindow(const trackerConfig& trackState);
|
||||
static Mat generateAnchors(trackerConfig& trackState);
|
||||
static Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans);
|
||||
static float trackerEval(Mat img, trackerConfig& trackState, Net& siamRPN);
|
||||
static void trackerInit(Mat img, trackerConfig& trackState, Net& siamRPN, Net& siamKernelR1, Net& siamKernelCL1);
|
||||
|
||||
template <typename T> static
|
||||
T sizeCal(const T& w, const T& h)
|
||||
{
|
||||
T pad = (w + h) * T(0.5);
|
||||
T sz2 = (w + pad) * (h + pad);
|
||||
return sqrt(sz2);
|
||||
}
|
||||
|
||||
template <>
|
||||
Mat sizeCal(const Mat& w, const Mat& h)
|
||||
{
|
||||
Mat pad = (w + h) * 0.5;
|
||||
Mat sz2 = (w + pad).mul((h + pad));
|
||||
|
||||
cv::sqrt(sz2, sz2);
|
||||
return sz2;
|
||||
}
|
||||
|
||||
static
|
||||
int run(int argc, char** argv)
|
||||
{
|
||||
@ -106,13 +54,16 @@ int run(int argc, char** argv)
|
||||
int backend = parser.get<int>("backend");
|
||||
int target = parser.get<int>("target");
|
||||
|
||||
// Read nets.
|
||||
Net siamRPN, siamKernelCL1, siamKernelR1;
|
||||
Ptr<TrackerDaSiamRPN> tracker;
|
||||
try
|
||||
{
|
||||
siamRPN = readNet(samples::findFile(net));
|
||||
siamKernelCL1 = readNet(samples::findFile(kernel_cls1));
|
||||
siamKernelR1 = readNet(samples::findFile(kernel_r1));
|
||||
TrackerDaSiamRPN::Params params;
|
||||
params.model = samples::findFile(net);
|
||||
params.kernel_cls1 = samples::findFile(kernel_cls1);
|
||||
params.kernel_r1 = samples::findFile(kernel_r1);
|
||||
params.backend = backend;
|
||||
params.target = target;
|
||||
tracker = TrackerDaSiamRPN::create(params);
|
||||
}
|
||||
catch (const cv::Exception& ee)
|
||||
{
|
||||
@ -124,14 +75,6 @@ int run(int argc, char** argv)
|
||||
return 2;
|
||||
}
|
||||
|
||||
// Set model backend.
|
||||
siamRPN.setPreferableBackend(backend);
|
||||
siamRPN.setPreferableTarget(target);
|
||||
siamKernelR1.setPreferableBackend(backend);
|
||||
siamKernelR1.setPreferableTarget(target);
|
||||
siamKernelCL1.setPreferableBackend(backend);
|
||||
siamKernelCL1.setPreferableTarget(target);
|
||||
|
||||
const std::string winName = "DaSiamRPN";
|
||||
namedWindow(winName, WINDOW_AUTOSIZE);
|
||||
|
||||
@ -174,17 +117,7 @@ int run(int argc, char** argv)
|
||||
Rect selectRect = selectROI(winName, image_select);
|
||||
std::cout << "ROI=" << selectRect << std::endl;
|
||||
|
||||
trackerConfig trackState;
|
||||
trackState.update_scoreSize();
|
||||
trackState.targetBox = Rect2f(
|
||||
float(selectRect.x) + float(selectRect.width) * 0.5f, // FIXIT don't use center in Rect structures, it is confusing
|
||||
float(selectRect.y) + float(selectRect.height) * 0.5f,
|
||||
float(selectRect.width),
|
||||
float(selectRect.height)
|
||||
);
|
||||
|
||||
// Set tracking template.
|
||||
trackerInit(image, trackState, siamRPN, siamKernelR1, siamKernelCL1);
|
||||
tracker->init(image, selectRect);
|
||||
|
||||
TickMeter tickMeter;
|
||||
|
||||
@ -197,16 +130,14 @@ int run(int argc, char** argv)
|
||||
break;
|
||||
}
|
||||
|
||||
Rect rect;
|
||||
|
||||
tickMeter.start();
|
||||
float score = trackerEval(image, trackState, siamRPN);
|
||||
bool ok = tracker->update(image, rect);
|
||||
tickMeter.stop();
|
||||
|
||||
Rect rect = {
|
||||
int(trackState.targetBox.x - int(trackState.targetBox.width / 2)),
|
||||
int(trackState.targetBox.y - int(trackState.targetBox.height / 2)),
|
||||
int(trackState.targetBox.width),
|
||||
int(trackState.targetBox.height)
|
||||
};
|
||||
float score = tracker->getTrackingScore();
|
||||
|
||||
std::cout << "frame " << count <<
|
||||
": predicted score=" << score <<
|
||||
" rect=" << rect <<
|
||||
@ -214,12 +145,16 @@ int run(int argc, char** argv)
|
||||
std::endl;
|
||||
|
||||
Mat render_image = image.clone();
|
||||
rectangle(render_image, rect, Scalar(0, 255, 0), 2);
|
||||
|
||||
std::string timeLabel = format("Inference time: %.2f ms", tickMeter.getTimeMilli());
|
||||
std::string scoreLabel = format("Score: %f", score);
|
||||
putText(render_image, timeLabel, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
putText(render_image, scoreLabel, Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
if (ok)
|
||||
{
|
||||
rectangle(render_image, rect, Scalar(0, 255, 0), 2);
|
||||
|
||||
std::string timeLabel = format("Inference time: %.2f ms", tickMeter.getTimeMilli());
|
||||
std::string scoreLabel = format("Score: %f", score);
|
||||
putText(render_image, timeLabel, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
putText(render_image, scoreLabel, Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
}
|
||||
|
||||
imshow(winName, render_image);
|
||||
|
||||
@ -234,275 +169,6 @@ int run(int argc, char** argv)
|
||||
return 0;
|
||||
}
|
||||
|
||||
Mat generateHanningWindow(const trackerConfig& trackState)
|
||||
{
|
||||
Mat baseWindows, HanningWindows;
|
||||
|
||||
createHanningWindow(baseWindows, Size(trackState.scoreSize, trackState.scoreSize), CV_32F);
|
||||
baseWindows = baseWindows.reshape(0, { 1, trackState.scoreSize, trackState.scoreSize });
|
||||
HanningWindows = baseWindows.clone();
|
||||
for (int i = 1; i < trackState.anchorNum; i++)
|
||||
{
|
||||
HanningWindows.push_back(baseWindows);
|
||||
}
|
||||
|
||||
return HanningWindows;
|
||||
}
|
||||
|
||||
Mat generateAnchors(trackerConfig& trackState)
|
||||
{
|
||||
int totalStride = trackState.totalStride, scales = trackState.scale, scoreSize = trackState.scoreSize;
|
||||
std::vector<float> ratios = trackState.ratios;
|
||||
std::vector<Rect2f> baseAnchors;
|
||||
int anchorNum = int(ratios.size());
|
||||
int size = totalStride * totalStride;
|
||||
|
||||
float ori = -(float(scoreSize / 2)) * float(totalStride);
|
||||
|
||||
for (auto i = 0; i < anchorNum; i++)
|
||||
{
|
||||
int ws = int(sqrt(size / ratios[i]));
|
||||
int hs = int(ws * ratios[i]);
|
||||
|
||||
float wws = float(ws) * scales;
|
||||
float hhs = float(hs) * scales;
|
||||
Rect2f anchor = { 0, 0, wws, hhs };
|
||||
baseAnchors.push_back(anchor);
|
||||
}
|
||||
|
||||
int anchorIndex[] = { 0, 0, 0, 0 };
|
||||
const int sizes[] = { 4, (int)ratios.size(), scoreSize, scoreSize };
|
||||
Mat anchors(4, sizes, CV_32F);
|
||||
|
||||
for (auto i = 0; i < scoreSize; i++)
|
||||
{
|
||||
for (auto j = 0; j < scoreSize; j++)
|
||||
{
|
||||
for (auto k = 0; k < anchorNum; k++)
|
||||
{
|
||||
anchorIndex[0] = 1, anchorIndex[1] = k, anchorIndex[2] = i, anchorIndex[3] = j;
|
||||
anchors.at<float>(anchorIndex) = ori + totalStride * i;
|
||||
|
||||
anchorIndex[0] = 0;
|
||||
anchors.at<float>(anchorIndex) = ori + totalStride * j;
|
||||
|
||||
anchorIndex[0] = 2;
|
||||
anchors.at<float>(anchorIndex) = baseAnchors[k].width;
|
||||
|
||||
anchorIndex[0] = 3;
|
||||
anchors.at<float>(anchorIndex) = baseAnchors[k].height;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return anchors;
|
||||
}
|
||||
|
||||
Mat getSubwindow(Mat& img, const Rect2f& targetBox, float originalSize, Scalar avgChans)
|
||||
{
|
||||
Mat zCrop, dst;
|
||||
Size imgSize = img.size();
|
||||
float c = (originalSize + 1) / 2;
|
||||
float xMin = (float)cvRound(targetBox.x - c);
|
||||
float xMax = xMin + originalSize - 1;
|
||||
float yMin = (float)cvRound(targetBox.y - c);
|
||||
float yMax = yMin + originalSize - 1;
|
||||
|
||||
int leftPad = (int)(fmax(0., -xMin));
|
||||
int topPad = (int)(fmax(0., -yMin));
|
||||
int rightPad = (int)(fmax(0., xMax - imgSize.width + 1));
|
||||
int bottomPad = (int)(fmax(0., yMax - imgSize.height + 1));
|
||||
|
||||
xMin = xMin + leftPad;
|
||||
xMax = xMax + leftPad;
|
||||
yMax = yMax + topPad;
|
||||
yMin = yMin + topPad;
|
||||
|
||||
if (topPad == 0 && bottomPad == 0 && leftPad == 0 && rightPad == 0)
|
||||
{
|
||||
img(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
|
||||
}
|
||||
else
|
||||
{
|
||||
copyMakeBorder(img, dst, topPad, bottomPad, leftPad, rightPad, BORDER_CONSTANT, avgChans);
|
||||
dst(Rect(int(xMin), int(yMin), int(xMax - xMin + 1), int(yMax - yMin + 1))).copyTo(zCrop);
|
||||
}
|
||||
|
||||
return zCrop;
|
||||
}
|
||||
|
||||
void softmax(const Mat& src, Mat& dst)
|
||||
{
|
||||
Mat maxVal;
|
||||
cv::max(src.row(1), src.row(0), maxVal);
|
||||
|
||||
src.row(1) -= maxVal;
|
||||
src.row(0) -= maxVal;
|
||||
|
||||
exp(src, dst);
|
||||
|
||||
Mat sumVal = dst.row(0) + dst.row(1);
|
||||
dst.row(0) = dst.row(0) / sumVal;
|
||||
dst.row(1) = dst.row(1) / sumVal;
|
||||
}
|
||||
|
||||
void elementMax(Mat& src)
|
||||
{
|
||||
int* p = src.size.p;
|
||||
int index[] = { 0, 0, 0, 0 };
|
||||
for (int n = 0; n < *p; n++)
|
||||
{
|
||||
for (int k = 0; k < *(p + 1); k++)
|
||||
{
|
||||
for (int i = 0; i < *(p + 2); i++)
|
||||
{
|
||||
for (int j = 0; j < *(p + 3); j++)
|
||||
{
|
||||
index[0] = n, index[1] = k, index[2] = i, index[3] = j;
|
||||
float& v = src.at<float>(index);
|
||||
v = fmax(v, 1.0f / v);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
float trackerEval(Mat img, trackerConfig& trackState, Net& siamRPN)
|
||||
{
|
||||
Rect2f targetBox = trackState.targetBox;
|
||||
|
||||
float wc = targetBox.height + trackState.contextAmount * (targetBox.width + targetBox.height);
|
||||
float hc = targetBox.width + trackState.contextAmount * (targetBox.width + targetBox.height);
|
||||
|
||||
float sz = sqrt(wc * hc);
|
||||
float scaleZ = trackState.exemplarSize / sz;
|
||||
|
||||
float searchSize = float((trackState.instanceSize - trackState.exemplarSize) / 2);
|
||||
float pad = searchSize / scaleZ;
|
||||
float sx = sz + 2 * pad;
|
||||
|
||||
Mat xCrop = getSubwindow(img, targetBox, (float)cvRound(sx), trackState.avgChans);
|
||||
|
||||
static Mat blob;
|
||||
std::vector<Mat> outs;
|
||||
std::vector<String> outNames;
|
||||
Mat delta, score;
|
||||
Mat sc, rc, penalty, pscore;
|
||||
|
||||
blobFromImage(xCrop, blob, 1.0, Size(trackState.instanceSize, trackState.instanceSize), Scalar(), trackState.swapRB, false, CV_32F);
|
||||
|
||||
siamRPN.setInput(blob);
|
||||
|
||||
outNames = siamRPN.getUnconnectedOutLayersNames();
|
||||
siamRPN.forward(outs, outNames);
|
||||
|
||||
delta = outs[0];
|
||||
score = outs[1];
|
||||
|
||||
score = score.reshape(0, { 2, trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
|
||||
delta = delta.reshape(0, { 4, trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
|
||||
|
||||
softmax(score, score);
|
||||
|
||||
targetBox.width *= scaleZ;
|
||||
targetBox.height *= scaleZ;
|
||||
|
||||
score = score.row(1);
|
||||
score = score.reshape(0, { 5, 19, 19 });
|
||||
|
||||
// Post processing
|
||||
delta.row(0) = delta.row(0).mul(trackState.anchors.row(2)) + trackState.anchors.row(0);
|
||||
delta.row(1) = delta.row(1).mul(trackState.anchors.row(3)) + trackState.anchors.row(1);
|
||||
exp(delta.row(2), delta.row(2));
|
||||
delta.row(2) = delta.row(2).mul(trackState.anchors.row(2));
|
||||
exp(delta.row(3), delta.row(3));
|
||||
delta.row(3) = delta.row(3).mul(trackState.anchors.row(3));
|
||||
|
||||
sc = sizeCal(delta.row(2), delta.row(3)) / sizeCal(targetBox.width, targetBox.height);
|
||||
elementMax(sc);
|
||||
|
||||
rc = delta.row(2).mul(1 / delta.row(3));
|
||||
rc = (targetBox.width / targetBox.height) / rc;
|
||||
elementMax(rc);
|
||||
|
||||
// Calculating the penalty
|
||||
exp(((rc.mul(sc) - 1.) * trackState.penaltyK * (-1.0)), penalty);
|
||||
penalty = penalty.reshape(0, { trackState.anchorNum, trackState.scoreSize, trackState.scoreSize });
|
||||
|
||||
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)
|
||||
{
|
||||
|
||||
@ -1,291 +0,0 @@
|
||||
"""
|
||||
DaSiamRPN tracker.
|
||||
Original paper: https://arxiv.org/abs/1808.06048
|
||||
Link to original repo: https://github.com/foolwood/DaSiamRPN
|
||||
Links to onnx models:
|
||||
network: https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=0
|
||||
kernel_r1: https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=0
|
||||
kernel_cls1: https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=0
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
import argparse
|
||||
import sys
|
||||
|
||||
class DaSiamRPNTracker:
|
||||
# Initialization of used values, initial bounding box, used network
|
||||
def __init__(self, net="dasiamrpn_model.onnx", kernel_r1="dasiamrpn_kernel_r1.onnx", kernel_cls1="dasiamrpn_kernel_cls1.onnx"):
|
||||
self.windowing = "cosine"
|
||||
self.exemplar_size = 127
|
||||
self.instance_size = 271
|
||||
self.total_stride = 8
|
||||
self.score_size = (self.instance_size - self.exemplar_size) // self.total_stride + 1
|
||||
self.context_amount = 0.5
|
||||
self.ratios = [0.33, 0.5, 1, 2, 3]
|
||||
self.scales = [8, ]
|
||||
self.anchor_num = len(self.ratios) * len(self.scales)
|
||||
self.penalty_k = 0.055
|
||||
self.window_influence = 0.42
|
||||
self.lr = 0.295
|
||||
self.score = []
|
||||
if self.windowing == "cosine":
|
||||
self.window = np.outer(np.hanning(self.score_size), np.hanning(self.score_size))
|
||||
elif self.windowing == "uniform":
|
||||
self.window = np.ones((self.score_size, self.score_size))
|
||||
self.window = np.tile(self.window.flatten(), self.anchor_num)
|
||||
# Loading network`s and kernel`s models
|
||||
self.net = cv.dnn.readNet(net)
|
||||
self.kernel_r1 = cv.dnn.readNet(kernel_r1)
|
||||
self.kernel_cls1 = cv.dnn.readNet(kernel_cls1)
|
||||
|
||||
def init(self, im, init_bb):
|
||||
target_pos, target_sz = np.array([init_bb[0], init_bb[1]]), np.array([init_bb[2], init_bb[3]])
|
||||
self.im_h = im.shape[0]
|
||||
self.im_w = im.shape[1]
|
||||
self.target_pos = target_pos
|
||||
self.target_sz = target_sz
|
||||
self.avg_chans = np.mean(im, axis=(0, 1))
|
||||
|
||||
# When we trying to generate ONNX model from the pre-trained .pth model
|
||||
# we are using only one state of the network. In our case used state
|
||||
# with big bounding box, so we were forced to add assertion for
|
||||
# too small bounding boxes - current state of the network can not
|
||||
# work properly with such small bounding boxes
|
||||
if ((self.target_sz[0] * self.target_sz[1]) / float(self.im_h * self.im_w)) < 0.004:
|
||||
raise AssertionError(
|
||||
"Initializing BB is too small-try to restart tracker with larger BB")
|
||||
|
||||
self.anchor = self.__generate_anchor()
|
||||
wc_z = self.target_sz[0] + self.context_amount * sum(self.target_sz)
|
||||
hc_z = self.target_sz[1] + self.context_amount * sum(self.target_sz)
|
||||
s_z = round(np.sqrt(wc_z * hc_z))
|
||||
z_crop = self.__get_subwindow_tracking(im, self.exemplar_size, s_z)
|
||||
z_crop = z_crop.transpose(2, 0, 1).reshape(1, 3, 127, 127).astype(np.float32)
|
||||
self.net.setInput(z_crop)
|
||||
z_f = self.net.forward('63')
|
||||
self.kernel_r1.setInput(z_f)
|
||||
r1 = self.kernel_r1.forward()
|
||||
self.kernel_cls1.setInput(z_f)
|
||||
cls1 = self.kernel_cls1.forward()
|
||||
r1 = r1.reshape(20, 256, 4, 4)
|
||||
cls1 = cls1.reshape(10, 256 , 4, 4)
|
||||
self.net.setParam(self.net.getLayerId('65'), 0, r1)
|
||||
self.net.setParam(self.net.getLayerId('68'), 0, cls1)
|
||||
|
||||
# Сreating anchor for tracking bounding box
|
||||
def __generate_anchor(self):
|
||||
self.anchor = np.zeros((self.anchor_num, 4), dtype = np.float32)
|
||||
size = self.total_stride * self.total_stride
|
||||
count = 0
|
||||
|
||||
for ratio in self.ratios:
|
||||
ws = int(np.sqrt(size / ratio))
|
||||
hs = int(ws * ratio)
|
||||
for scale in self.scales:
|
||||
wws = ws * scale
|
||||
hhs = hs * scale
|
||||
self.anchor[count] = [0, 0, wws, hhs]
|
||||
count += 1
|
||||
|
||||
score_sz = int(self.score_size)
|
||||
self.anchor = np.tile(self.anchor, score_sz * score_sz).reshape((-1, 4))
|
||||
ori = - (score_sz / 2) * self.total_stride
|
||||
xx, yy = np.meshgrid([ori + self.total_stride * dx for dx in range(score_sz)], [ori + self.total_stride * dy for dy in range(score_sz)])
|
||||
xx, yy = np.tile(xx.flatten(), (self.anchor_num, 1)).flatten(), np.tile(yy.flatten(), (self.anchor_num, 1)).flatten()
|
||||
self.anchor[:, 0], self.anchor[:, 1] = xx.astype(np.float32), yy.astype(np.float32)
|
||||
return self.anchor
|
||||
|
||||
# Function for updating tracker state
|
||||
def update(self, im):
|
||||
wc_z = self.target_sz[1] + self.context_amount * sum(self.target_sz)
|
||||
hc_z = self.target_sz[0] + self.context_amount * sum(self.target_sz)
|
||||
s_z = np.sqrt(wc_z * hc_z)
|
||||
scale_z = self.exemplar_size / s_z
|
||||
d_search = (self.instance_size - self.exemplar_size) / 2
|
||||
pad = d_search / scale_z
|
||||
s_x = round(s_z + 2 * pad)
|
||||
|
||||
# Region preprocessing part
|
||||
x_crop = self.__get_subwindow_tracking(im, self.instance_size, s_x)
|
||||
x_crop = x_crop.transpose(2, 0, 1).reshape(1, 3, 271, 271).astype(np.float32)
|
||||
self.score = self.__tracker_eval(x_crop, scale_z)
|
||||
self.target_pos[0] = max(0, min(self.im_w, self.target_pos[0]))
|
||||
self.target_pos[1] = max(0, min(self.im_h, self.target_pos[1]))
|
||||
self.target_sz[0] = max(10, min(self.im_w, self.target_sz[0]))
|
||||
self.target_sz[1] = max(10, min(self.im_h, self.target_sz[1]))
|
||||
|
||||
cx, cy = self.target_pos
|
||||
w, h = self.target_sz
|
||||
updated_bb = (cx, cy, w, h)
|
||||
return True, updated_bb
|
||||
|
||||
# Function for updating position of the bounding box
|
||||
def __tracker_eval(self, x_crop, scale_z):
|
||||
target_size = self.target_sz * scale_z
|
||||
self.net.setInput(x_crop)
|
||||
outNames = self.net.getUnconnectedOutLayersNames()
|
||||
outNames = ['66', '68']
|
||||
delta, score = self.net.forward(outNames)
|
||||
delta = np.transpose(delta, (1, 2, 3, 0))
|
||||
delta = np.ascontiguousarray(delta, dtype = np.float32)
|
||||
delta = np.reshape(delta, (4, -1))
|
||||
score = np.transpose(score, (1, 2, 3, 0))
|
||||
score = np.ascontiguousarray(score, dtype = np.float32)
|
||||
score = np.reshape(score, (2, -1))
|
||||
score = self.__softmax(score)[1, :]
|
||||
delta[0, :] = delta[0, :] * self.anchor[:, 2] + self.anchor[:, 0]
|
||||
delta[1, :] = delta[1, :] * self.anchor[:, 3] + self.anchor[:, 1]
|
||||
delta[2, :] = np.exp(delta[2, :]) * self.anchor[:, 2]
|
||||
delta[3, :] = np.exp(delta[3, :]) * self.anchor[:, 3]
|
||||
|
||||
def __change(r):
|
||||
return np.maximum(r, 1./r)
|
||||
|
||||
def __sz(w, h):
|
||||
pad = (w + h) * 0.5
|
||||
sz2 = (w + pad) * (h + pad)
|
||||
return np.sqrt(sz2)
|
||||
|
||||
def __sz_wh(wh):
|
||||
pad = (wh[0] + wh[1]) * 0.5
|
||||
sz2 = (wh[0] + pad) * (wh[1] + pad)
|
||||
return np.sqrt(sz2)
|
||||
|
||||
s_c = __change(__sz(delta[2, :], delta[3, :]) / (__sz_wh(target_size)))
|
||||
r_c = __change((target_size[0] / target_size[1]) / (delta[2, :] / delta[3, :]))
|
||||
penalty = np.exp(-(r_c * s_c - 1.) * self.penalty_k)
|
||||
pscore = penalty * score
|
||||
pscore = pscore * (1 - self.window_influence) + self.window * self.window_influence
|
||||
best_pscore_id = np.argmax(pscore)
|
||||
target = delta[:, best_pscore_id] / scale_z
|
||||
target_size /= scale_z
|
||||
lr = penalty[best_pscore_id] * score[best_pscore_id] * self.lr
|
||||
res_x = target[0] + self.target_pos[0]
|
||||
res_y = target[1] + self.target_pos[1]
|
||||
res_w = target_size[0] * (1 - lr) + target[2] * lr
|
||||
res_h = target_size[1] * (1 - lr) + target[3] * lr
|
||||
self.target_pos = np.array([res_x, res_y])
|
||||
self.target_sz = np.array([res_w, res_h])
|
||||
return score[best_pscore_id]
|
||||
|
||||
def __softmax(self, x):
|
||||
x_max = x.max(0)
|
||||
e_x = np.exp(x - x_max)
|
||||
y = e_x / e_x.sum(axis = 0)
|
||||
return y
|
||||
|
||||
# Reshaping cropped image for using in the model
|
||||
def __get_subwindow_tracking(self, im, model_size, original_sz):
|
||||
im_sz = im.shape
|
||||
c = (original_sz + 1) / 2
|
||||
context_xmin = round(self.target_pos[0] - c)
|
||||
context_xmax = context_xmin + original_sz - 1
|
||||
context_ymin = round(self.target_pos[1] - c)
|
||||
context_ymax = context_ymin + original_sz - 1
|
||||
left_pad = int(max(0., -context_xmin))
|
||||
top_pad = int(max(0., -context_ymin))
|
||||
right_pad = int(max(0., context_xmax - im_sz[1] + 1))
|
||||
bot_pad = int(max(0., context_ymax - im_sz[0] + 1))
|
||||
context_xmin += left_pad
|
||||
context_xmax += left_pad
|
||||
context_ymin += top_pad
|
||||
context_ymax += top_pad
|
||||
r, c, k = im.shape
|
||||
|
||||
if any([top_pad, bot_pad, left_pad, right_pad]):
|
||||
te_im = np.zeros((
|
||||
r + top_pad + bot_pad, c + left_pad + right_pad, k), np.uint8)
|
||||
te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im
|
||||
if top_pad:
|
||||
te_im[0:top_pad, left_pad:left_pad + c, :] = self.avg_chans
|
||||
if bot_pad:
|
||||
te_im[r + top_pad:, left_pad:left_pad + c, :] = self.avg_chans
|
||||
if left_pad:
|
||||
te_im[:, 0:left_pad, :] = self.avg_chans
|
||||
if right_pad:
|
||||
te_im[:, c + left_pad:, :] = self.avg_chans
|
||||
im_patch_original = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :]
|
||||
else:
|
||||
im_patch_original = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :]
|
||||
|
||||
if not np.array_equal(model_size, original_sz):
|
||||
im_patch_original = cv.resize(im_patch_original, (model_size, model_size))
|
||||
return im_patch_original
|
||||
|
||||
# Sample for using DaSiamRPN tracker
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Run tracker")
|
||||
parser.add_argument("--input", type=str, help="Full path to input (empty for camera)")
|
||||
parser.add_argument("--net", type=str, default="dasiamrpn_model.onnx", help="Full path to onnx model of net")
|
||||
parser.add_argument("--kernel_r1", type=str, default="dasiamrpn_kernel_r1.onnx", help="Full path to onnx model of kernel_r1")
|
||||
parser.add_argument("--kernel_cls1", type=str, default="dasiamrpn_kernel_cls1.onnx", help="Full path to onnx model of kernel_cls1")
|
||||
args = parser.parse_args()
|
||||
point1 = ()
|
||||
point2 = ()
|
||||
mark = True
|
||||
drawing = False
|
||||
cx, cy, w, h = 0.0, 0.0, 0, 0
|
||||
# Fucntion for drawing during videostream
|
||||
def get_bb(event, x, y, flag, param):
|
||||
nonlocal point1, point2, cx, cy, w, h, drawing, mark
|
||||
|
||||
if event == cv.EVENT_LBUTTONDOWN:
|
||||
if not drawing:
|
||||
drawing = True
|
||||
point1 = (x, y)
|
||||
else:
|
||||
drawing = False
|
||||
|
||||
elif event == cv.EVENT_MOUSEMOVE:
|
||||
if drawing:
|
||||
point2 = (x, y)
|
||||
|
||||
elif event == cv.EVENT_LBUTTONUP:
|
||||
cx = point1[0] - (point1[0] - point2[0]) / 2
|
||||
cy = point1[1] - (point1[1] - point2[1]) / 2
|
||||
w = abs(point1[0] - point2[0])
|
||||
h = abs(point1[1] - point2[1])
|
||||
mark = False
|
||||
|
||||
# Creating window for visualization
|
||||
cap = cv.VideoCapture(args.input if args.input else 0)
|
||||
cv.namedWindow("DaSiamRPN")
|
||||
cv.setMouseCallback("DaSiamRPN", get_bb)
|
||||
|
||||
whitespace_key = 32
|
||||
while cv.waitKey(40) != whitespace_key:
|
||||
has_frame, frame = cap.read()
|
||||
if not has_frame:
|
||||
sys.exit(0)
|
||||
cv.imshow("DaSiamRPN", frame)
|
||||
|
||||
while mark:
|
||||
twin = np.copy(frame)
|
||||
if point1 and point2:
|
||||
cv.rectangle(twin, point1, point2, (0, 255, 255), 3)
|
||||
cv.imshow("DaSiamRPN", twin)
|
||||
cv.waitKey(40)
|
||||
|
||||
init_bb = (cx, cy, w, h)
|
||||
tracker = DaSiamRPNTracker(args.net, args.kernel_r1, args.kernel_cls1)
|
||||
tracker.init(frame, init_bb)
|
||||
|
||||
# Tracking loop
|
||||
while cap.isOpened():
|
||||
has_frame, frame = cap.read()
|
||||
if not has_frame:
|
||||
sys.exit(0)
|
||||
_, new_bb = tracker.update(frame)
|
||||
cx, cy, w, h = new_bb
|
||||
cv.rectangle(frame, (int(cx - w // 2), int(cy - h // 2)), (int(cx - w // 2) + int(w), int(cy - h // 2) + int(h)),(0, 255, 255), 3)
|
||||
cv.imshow("DaSiamRPN", frame)
|
||||
key = cv.waitKey(1)
|
||||
if key == ord("q"):
|
||||
break
|
||||
|
||||
cap.release()
|
||||
cv.destroyAllWindows()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -3,8 +3,22 @@
|
||||
'''
|
||||
Tracker demo
|
||||
|
||||
For usage download models by following links
|
||||
For GOTURN:
|
||||
goturn.prototxt and goturn.caffemodel: https://github.com/opencv/opencv_extra/tree/c4219d5eb3105ed8e634278fad312a1a8d2c182d/testdata/tracking
|
||||
For DaSiamRPN:
|
||||
network: https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=0
|
||||
kernel_r1: https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=0
|
||||
kernel_cls1: https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=0
|
||||
|
||||
USAGE:
|
||||
tracker.py [<video_source>]
|
||||
tracker.py [-h] [--input INPUT] [--tracker_algo TRACKER_ALGO]
|
||||
[--goturn GOTURN] [--goturn_model GOTURN_MODEL]
|
||||
[--dasiamrpn_net DASIAMRPN_NET]
|
||||
[--dasiamrpn_kernel_r1 DASIAMRPN_KERNEL_R1]
|
||||
[--dasiamrpn_kernel_cls1 DASIAMRPN_KERNEL_CLS1]
|
||||
[--dasiamrpn_backend DASIAMRPN_BACKEND]
|
||||
[--dasiamrpn_target DASIAMRPN_TARGET]
|
||||
'''
|
||||
|
||||
# Python 2/3 compatibility
|
||||
@ -14,18 +28,37 @@ import sys
|
||||
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
import argparse
|
||||
|
||||
from video import create_capture, presets
|
||||
|
||||
class App(object):
|
||||
|
||||
def initializeTracker(self, image):
|
||||
def __init__(self, args):
|
||||
self.args = args
|
||||
|
||||
def initializeTracker(self, image, trackerAlgorithm):
|
||||
while True:
|
||||
if trackerAlgorithm == 'mil':
|
||||
tracker = cv.TrackerMIL_create()
|
||||
elif trackerAlgorithm == 'goturn':
|
||||
params = cv.TrackerGOTURN_Params()
|
||||
params.modelTxt = self.args.goturn
|
||||
params.modelBin = self.args.goturn_model
|
||||
tracker = cv.TrackerGOTURN_create(params)
|
||||
elif trackerAlgorithm == 'dasiamrpn':
|
||||
params = cv.TrackerDaSiamRPN_Params()
|
||||
params.model = self.args.dasiamrpn_net
|
||||
params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1
|
||||
params.kernel_r1 = self.args.dasiamrpn_kernel_r1
|
||||
tracker = cv.TrackerDaSiamRPN_create(params)
|
||||
else:
|
||||
sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn.".format(trackerAlgorithm))
|
||||
|
||||
print('==> Select object ROI for tracker ...')
|
||||
bbox = cv.selectROI('tracking', image)
|
||||
print('ROI: {}'.format(bbox))
|
||||
|
||||
tracker = cv.TrackerMIL_create()
|
||||
try:
|
||||
tracker.init(image, bbox)
|
||||
except Exception as e:
|
||||
@ -37,7 +70,8 @@ class App(object):
|
||||
return tracker
|
||||
|
||||
def run(self):
|
||||
videoPath = sys.argv[1] if len(sys.argv) >= 2 else 'vtest.avi'
|
||||
videoPath = self.args.input
|
||||
trackerAlgorithm = self.args.tracker_algo
|
||||
camera = create_capture(videoPath, presets['cube'])
|
||||
if not camera.isOpened():
|
||||
sys.exit("Can't open video stream: {}".format(videoPath))
|
||||
@ -48,7 +82,7 @@ class App(object):
|
||||
assert image is not None
|
||||
|
||||
cv.namedWindow('tracking')
|
||||
tracker = self.initializeTracker(image)
|
||||
tracker = self.initializeTracker(image, trackerAlgorithm)
|
||||
|
||||
print("==> Tracking is started. Press 'SPACE' to re-initialize tracker or 'ESC' for exit...")
|
||||
|
||||
@ -76,5 +110,24 @@ class App(object):
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(__doc__)
|
||||
App().run()
|
||||
parser = argparse.ArgumentParser(description="Run tracker")
|
||||
parser.add_argument("--input", type=str, default="vtest.avi", help="Path to video source")
|
||||
parser.add_argument("--tracker_algo", type=str, default="mil", help="One of three available tracking algorithms: mil, goturn, dasiamrpn")
|
||||
parser.add_argument("--goturn", type=str, default="goturn.prototxt", help="Path to GOTURN architecture")
|
||||
parser.add_argument("--goturn_model", type=str, default="goturn.caffemodel", help="Path to GOTERN model")
|
||||
parser.add_argument("--dasiamrpn_net", type=str, default="dasiamrpn_model.onnx", help="Path to onnx model of DaSiamRPN net")
|
||||
parser.add_argument("--dasiamrpn_kernel_r1", type=str, default="dasiamrpn_kernel_r1.onnx", help="Path to onnx model of DaSiamRPN kernel_r1")
|
||||
parser.add_argument("--dasiamrpn_kernel_cls1", type=str, default="dasiamrpn_kernel_cls1.onnx", help="Path to onnx model of DaSiamRPN kernel_cls1")
|
||||
parser.add_argument("--dasiamrpn_backend", type=int, default=0, help="Choose one of computation backends:\
|
||||
0: automatically (by default),\
|
||||
1: Halide language (http://halide-lang.org/),\
|
||||
2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit),\
|
||||
3: OpenCV implementation")
|
||||
parser.add_argument("--dasiamrpn_target", type=int, default=0, help="Choose one of target computation devices:\
|
||||
0: CPU target (by default),\
|
||||
1: OpenCL,\
|
||||
2: OpenCL fp16 (half-float precision),\
|
||||
3: VPU")
|
||||
args = parser.parse_args()
|
||||
App(args).run()
|
||||
cv.destroyAllWindows()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user