// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. #include "test_precomp.hpp" //#define DEBUG_TEST #ifdef DEBUG_TEST #include #endif namespace opencv_test { namespace { //using namespace cv::tracking; #define TESTSET_NAMES testing::Values("david", "dudek", "faceocc2") const string TRACKING_DIR = "tracking"; const string FOLDER_IMG = "data"; const string FOLDER_OMIT_INIT = "initOmit"; #include "test_trackers.impl.hpp" //[TESTDATA] PARAM_TEST_CASE(DistanceAndOverlap, string) { string dataset; virtual void SetUp() { dataset = GET_PARAM(0); } }; TEST_P(DistanceAndOverlap, MIL) { TrackerTest test(TrackerMIL::create(), dataset, 30, .65f, NoTransform); test.run(); } TEST_P(DistanceAndOverlap, Shifted_Data_MIL) { TrackerTest test(TrackerMIL::create(), dataset, 30, .6f, CenterShiftLeft); test.run(); } /***************************************************************************************/ //Tests with scaled initial window TEST_P(DistanceAndOverlap, Scaled_Data_MIL) { TrackerTest test(TrackerMIL::create(), dataset, 30, .7f, Scale_1_1); test.run(); } TEST_P(DistanceAndOverlap, GOTURN) { std::string model = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.prototxt"); std::string weights = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.caffemodel", false); cv::TrackerGOTURN::Params params; params.modelTxt = model; params.modelBin = weights; TrackerTest test(TrackerGOTURN::create(params), dataset, 35, .35f, NoTransform); test.run(); } INSTANTIATE_TEST_CASE_P(Tracking, DistanceAndOverlap, TESTSET_NAMES); static bool checkIOU(const Rect& r0, const Rect& r1, double threshold) { int interArea = (r0 & r1).area(); double iouVal = (interArea * 1.0 )/ (r0.area() + r1.area() - interArea);; if (iouVal > threshold) return true; else { std::cout <<"Unmatched IOU: expect IOU val ("< the IOU threadhold ("<& tracker, double iouThreshold = 0.7) { // Template image Mat img0 = imread(findDataFile("tracking/bag/00000001.jpg"), 1); // Tracking image sequence. std::vector imgs; imgs.push_back(imread(findDataFile("tracking/bag/00000002.jpg"), 1)); imgs.push_back(imread(findDataFile("tracking/bag/00000003.jpg"), 1)); imgs.push_back(imread(findDataFile("tracking/bag/00000004.jpg"), 1)); imgs.push_back(imread(findDataFile("tracking/bag/00000005.jpg"), 1)); imgs.push_back(imread(findDataFile("tracking/bag/00000006.jpg"), 1)); cv::Rect roi(325, 164, 100, 100); std::vector targetRois; targetRois.push_back(cv::Rect(278, 133, 99, 104)); targetRois.push_back(cv::Rect(293, 88, 93, 110)); targetRois.push_back(cv::Rect(287, 76, 89, 116)); targetRois.push_back(cv::Rect(297, 74, 82, 122)); targetRois.push_back(cv::Rect(311, 83, 78, 125)); tracker->init(img0, roi); CV_Assert(targetRois.size() == imgs.size()); for (int i = 0; i < (int)imgs.size(); i++) { bool res = tracker->update(imgs[i], roi); ASSERT_TRUE(res); ASSERT_TRUE(checkIOU(roi, targetRois[i], iouThreshold)) << cv::format("Fail at img %d.",i); } } TEST(GOTURN, accuracy) { std::string model = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.prototxt"); std::string weights = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.caffemodel", false); cv::TrackerGOTURN::Params params; params.modelTxt = model; params.modelBin = weights; cv::Ptr tracker = TrackerGOTURN::create(params); // TODO! GOTURN have low accuracy. Try to remove this api at 5.x. checkTrackingAccuracy(tracker, 0.08); } TEST(DaSiamRPN, accuracy) { 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 = TrackerDaSiamRPN::create(params); checkTrackingAccuracy(tracker, 0.7); } TEST(NanoTrack, accuracy_NanoTrack_V1) { std::string backbonePath = cvtest::findDataFile("dnn/onnx/models/nanotrack_backbone_sim.onnx", false); std::string neckheadPath = cvtest::findDataFile("dnn/onnx/models/nanotrack_head_sim.onnx", false); cv::TrackerNano::Params params; params.backbone = backbonePath; params.neckhead = neckheadPath; cv::Ptr tracker = TrackerNano::create(params); checkTrackingAccuracy(tracker); } TEST(NanoTrack, accuracy_NanoTrack_V2) { std::string backbonePath = cvtest::findDataFile("dnn/onnx/models/nanotrack_backbone_sim_v2.onnx", false); std::string neckheadPath = cvtest::findDataFile("dnn/onnx/models/nanotrack_head_sim_v2.onnx", false); cv::TrackerNano::Params params; params.backbone = backbonePath; params.neckhead = neckheadPath; cv::Ptr tracker = TrackerNano::create(params); checkTrackingAccuracy(tracker, 0.69); } TEST(vittrack, accuracy_vittrack) { std::string model = cvtest::findDataFile("dnn/onnx/models/vitTracker.onnx"); cv::TrackerVit::Params params; params.net = model; cv::Ptr tracker = TrackerVit::create(params); // NOTE: Test threshold was reduced from 0.67 (libjpeg-turbo) to 0.66 (libjpeg 9f), // becase libjpeg and libjpeg-turbo produce slightly different images checkTrackingAccuracy(tracker, 0.66); } }} // namespace opencv_test::