#include <getopt.h>
#include <libgen.h>
#include <unistd.h>
+#include <iomanip>
#include "kcf.h"
#include "vot.hpp"
+double calcAccuracy(std::string line, cv::Rect bb_rect, cv::Rect &groundtruth_rect)
+{
+ std::vector<float> numbers;
+ std::istringstream s(line);
+ float x;
+ char ch;
+
+ while (s >> x) {
+ numbers.push_back(x);
+ s >> ch;
+ }
+ double x1 = std::min(numbers[0], std::min(numbers[2], std::min(numbers[4], numbers[6])));
+ double x2 = std::max(numbers[0], std::max(numbers[2], std::max(numbers[4], numbers[6])));
+ double y1 = std::min(numbers[1], std::min(numbers[3], std::min(numbers[5], numbers[7])));
+ double y2 = std::max(numbers[1], std::max(numbers[3], std::max(numbers[5], numbers[7])));
+
+ groundtruth_rect = cv::Rect(x1, y1, x2 - x1, y2 - y1);
+
+ double rects_intersection = (groundtruth_rect & bb_rect).area();
+ double rects_union = (groundtruth_rect | bb_rect).area();
+ double accuracy = rects_intersection / rects_union;
+
+ return accuracy;
+}
+
int main(int argc, char *argv[])
{
//load region, images and prepare for output
std::string region, images, output;
- int visualize_delay = -1, fit_size = -1;
+ int visualize_delay = -1, fit_size_x = -1, fit_size_y = -1;
KCF_Tracker tracker;
while (1) {
{0, 0, 0, 0 }
};
- int c = getopt_long(argc, argv, "dhv::o:f:",
- long_options, &option_index);
+ int c = getopt_long(argc, argv, "dhv::f::o:", long_options, &option_index);
if (c == -1)
break;
<< argv[0] << " [options] <directory>\n"
<< argv[0] << " [options] <path/to/region.txt or groundtruth.txt> <path/to/images.txt> [path/to/output.txt]\n"
<< "Options:\n"
- << " --visualize | -v [delay_ms]\n"
- << " --output | -o <outout.txt>\n"
+ << " --visualize | -v[delay_ms]\n"
+ << " --output | -o <output.txt>\n"
<< " --debug | -d\n"
- << " --fit | -f [dimension_size]\n";
+ << " --fit | -f[WxH]\n";
exit(0);
break;
case 'o':
visualize_delay = optarg ? atol(optarg) : 1;
break;
case 'f':
- fit_size = optarg ? atol(optarg) : 128;
+ std::string sizes = optarg ? optarg : "128x128";
+ std::string delimiter = "x";
+ size_t pos = sizes.find(delimiter);
+ std::string first_argument = sizes.substr(0, pos);
+ sizes.erase(0, pos + delimiter.length());
+
+ fit_size_x = stol(first_argument);
+ fit_size_y = stol(sizes);
break;
}
}
}
VOT vot_io(region, images, output);
+ // if groundtruth.txt is used use intersection over union (IOU) to calculate tracker accuracy
+ std::ifstream groundtruth_stream;
+ if (region.compare("groundtruth.txt") == 0) {
+ groundtruth_stream.open(region.c_str());
+ std::string line;
+ std::getline(groundtruth_stream, line);
+ }
+
cv::Mat image;
//img = firts frame, initPos = initial position in the first frame
vot_io.outputBoundingBox(init_rect);
vot_io.getNextImage(image);
- tracker.init(image, init_rect, fit_size);
+ tracker.init(image, init_rect, fit_size_x, fit_size_y);
BBox_c bb;
- double avg_time = 0.;
+ cv::Rect bb_rect;
+ double avg_time = 0., sum_accuracy = 0.;
int frames = 0;
+
+ std::cout << std::fixed << std::setprecision(2);
+
while (vot_io.getNextImage(image) == 1){
double time_profile_counter = cv::getCPUTickCount();
tracker.track(image);
time_profile_counter = cv::getCPUTickCount() - time_profile_counter;
- std::cout << " -> speed : " << time_profile_counter/((double)cvGetTickFrequency()*1000) << "ms. per frame" << std::endl;
+ std::cout << " -> speed : " << time_profile_counter/((double)cvGetTickFrequency()*1000) << "ms per frame, "
+ "response : " << tracker.getFilterResponse();
avg_time += time_profile_counter/((double)cvGetTickFrequency()*1000);
frames++;
bb = tracker.getBBox();
- vot_io.outputBoundingBox(cv::Rect(bb.cx - bb.w/2., bb.cy - bb.h/2., bb.w, bb.h));
+ bb_rect = cv::Rect(bb.cx - bb.w/2., bb.cy - bb.h/2., bb.w, bb.h);
+ vot_io.outputBoundingBox(bb_rect);
+
+ if (groundtruth_stream.is_open()) {
+ std::string line;
+ std::getline(groundtruth_stream, line);
+
+ cv::Rect groundtruthRect;
+ double accuracy = calcAccuracy(line, bb_rect, groundtruthRect);
+ if (visualize_delay >= 0)
+ cv::rectangle(image, groundtruthRect, CV_RGB(255, 0,0), 1);
+ std::cout << ", accuracy: " << accuracy;
+ sum_accuracy += accuracy;
+ }
+
+ std::cout << std::endl;
if (visualize_delay >= 0) {
- cv::rectangle(image, cv::Rect(bb.cx - bb.w/2., bb.cy - bb.h/2., bb.w, bb.h), CV_RGB(0,255,0), 2);
+ cv::rectangle(image, bb_rect, CV_RGB(0,255,0), 2);
cv::imshow("output", image);
int ret = cv::waitKey(visualize_delay);
if (visualize_delay > 0 && ret != -1 && ret != 255)
// cv::imwrite(ss.c_str(), image, compression_params);
}
- std::cout << "Average processing speed " << avg_time/frames << "ms. (" << 1./(avg_time/frames)*1000 << " fps)" << std::endl;
+ std::cout << "Average processing speed: " << avg_time / frames << "ms (" << 1. / (avg_time / frames) * 1000 << " fps)";
+ if (groundtruth_stream.is_open()) {
+ std::cout << "; Average accuracy: " << sum_accuracy/frames << std::endl;
+ groundtruth_stream.close();
+ }
+ std::cout << std::endl;
return EXIT_SUCCESS;
}
projects / hercules2020 / kcf.git / blobdiff