mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 01:39:10 +08:00
61359a5bd0
add cuda and vulkan backends to dnn samples
190 lines
6.2 KiB
C++
190 lines
6.2 KiB
C++
// 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 <iostream>
|
|
#include <cmath>
|
|
|
|
#include <opencv2/dnn.hpp>
|
|
#include <opencv2/imgproc.hpp>
|
|
#include <opencv2/highgui.hpp>
|
|
#include <opencv2/video.hpp>
|
|
|
|
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 | dasiamrpn_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: "
|
|
"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, "
|
|
"4: VKCOM, "
|
|
"5: CUDA },"
|
|
"{ target | 0 | Choose one of target computation devices: "
|
|
"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.
|
|
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.
|
|
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=<video> parameter to read from video file" << 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.", 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));
|
|
|
|
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;
|
|
}
|
|
}
|