mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
189 lines
6.2 KiB
189 lines
6.2 KiB
// 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; |
|
} |
|
}
|
|
|