OPENCV例子opencv-4.5.5\samples\dnn\dasiamrpn_tracker.cpp的代码分析
视觉目标跟踪算法:
- 基于SiamRPN，主要是提出更好的使用数据，利用更好的训练方式让tracker变得更鲁邦
- 有了更好的分数作为指导后，算法可以扩展到Long-term
示例run函数调用情况如下：
示例run函数流程图情况如下：
示例run函数UML逻辑图情况如下：
示例源代码如下：
// 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
#include
#include
#include
#include
#include
#include
using namespace cv;
using namespace cv::dnn;
const char *keys =
"{ help h | | Print help message }"
"{ input i | | Full path to input video folder, the specific camera index. (empty for camera 0) }"
"{ net | dasiamrpn_model.onnx | Path to onnx model of net}"
"{ kernel_cls1 | c'c_kernel_cls1.onnx | Path to onnx model of kernel_r1 }"
"{ kernel_r1 | dasiamrpn_kernel_r1.onnx | Path to onnx model of kernel_cls1 }"
"{ backend | 0 | Choose one of computation backends: "选择一个计算后端
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "卤化物语言
"2: Intel's Deep Learning Inference Engine英特尔的深度学习推理引擎(https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA },"
"{ target | 0 | Choose one of target computation devices: "选择目标计算设备之一
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
;
static
int run(int argc, char** argv)
{
// Parse command line arguments. 解析命令行参数
CommandLineParser parser(argc, argv, keys);
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
std::string inputName = parser.get<String>("input");
std::string net = parser.get<String>("net");
std::string kernel_cls1 = parser.get<String>("kernel_cls1");
std::string kernel_r1 = parser.get<String>("kernel_r1");
int backend = parser.get<int>("backend");
int target = parser.get<int>("target");
Ptr<TrackerDaSiamRPN> tracker;
try
{
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)
{
std::cerr << "Exception: " << ee.what() << std::endl;
std::cout << "Can't load the network by using the following files:" << std::endl;
std::cout << "siamRPN : " << net << std::endl;
std::cout << "siamKernelCL1 : " << kernel_cls1 << std::endl;
std::cout << "siamKernelR1 : " << kernel_r1 << std::endl;
return 2;
}
const std::string winName = "DaSiamRPN";
namedWindow(winName, WINDOW_AUTOSIZE);
// Open a video file or an image file or a camera stream. 打开视频文件或图像文件或摄像头流
VideoCapture cap;
if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1))
{
int c = inputName.empty() ? 0 : inputName[0] - '0';
std::cout << "Trying to open camera #" << c << " ..." << std::endl;
if (!cap.open(c))
{
std::cout << "Capture from camera #" << c << " didn't work. Specify -i= << std::endl;
return 2;
}
}
else if (inputName.size())
{
inputName = samples::findFileOrKeep(inputName);
if (!cap.open(inputName))
{
std::cout << "Could not open: " << inputName << std::endl;
return 2;
}
}
// Read the first image.
Mat image;
cap >> image;
if (image.empty())
{
std::cerr << "Can't capture frame!" << std::endl;
return 2;
}
Mat image_select = image.clone();
putText(image_select, "Select initial bounding box you want to track.", 选择要跟踪的初始边界框
Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
putText(image_select, "And Press the ENTER key.", Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0)); 并按 ENTER 键
Rect selectRect = selectROI(winName, image_select);
std::cout << "ROI=" << selectRect << std::endl;
tracker->init(image, selectRect);
TickMeter tickMeter;
for (int count = 0; ; ++count)
{
cap >> image;
if (image.empty())
{
std::cerr << "Can't capture frame " << count << ". End of video stream?" << std::endl;
break;
}
Rect rect;
tickMeter.start();
bool ok = tracker->update(image, rect);
tickMeter.stop();
float score = tracker->getTrackingScore();
std::cout << "frame " << count <<
": predicted score=" << score <<
" rect=" << rect <<
" time=" << tickMeter.getTimeMilli() << "ms" <<
std::endl;
Mat render_image = image.clone();
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);
tickMeter.reset();
int c = waitKey(1);
if (c == 27 /*ESC*/)
break;
}
std::cout << "Exit" << std::endl;
return 0;
}
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;
}
}