Big batch does not work.
double calcAccuracy(std::string line, cv::Rect bb_rect, cv::Rect &groundtruth_rect)
{
std::vector<float> numbers;
- std::istringstream s( line );
+ std::istringstream s(line);
float x;
char ch;
- while (s >> x){
+ while (s >> x) {
numbers.push_back(x);
s >> ch;
}
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);
+ 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;
+ double accuracy = rects_intersection / rects_union;
return accuracy;
}
int main(int argc, char *argv[])
{
- //load region, images and prepare for output
+ // load region, images and prepare for output
std::string region, images, output;
int visualize_delay = -1, fit_size_x = -1, fit_size_y = -1;
KCF_Tracker tracker;
while (1) {
int option_index = 0;
- static struct option long_options[] = {
- {"debug", no_argument, 0, 'd' },
- {"help", no_argument, 0, 'h' },
- {"output", required_argument, 0, 'o' },
- {"visualize", optional_argument, 0, 'v' },
- {"fit", optional_argument, 0, 'f' },
- {0, 0, 0, 0 }
- };
-
- int c = getopt_long(argc, argv, "dhv::f::o:",
- long_options, &option_index);
- if (c == -1)
- break;
+ static struct option long_options[] = {{"debug", no_argument, 0, 'd'},
+ {"visualDebug", no_argument, 0, 'p'},
+ {"help", no_argument, 0, 'h'},
+ {"output", required_argument, 0, 'o'},
+ {"visualize", optional_argument, 0, 'v'},
+ {"fit", optional_argument, 0, 'f'},
+ {0, 0, 0, 0}};
+
+ int c = getopt_long(argc, argv, "dphv::f::o:", long_options, &option_index);
+ if (c == -1) break;
switch (c) {
case 'd':
tracker.m_debug = true;
break;
+ case 'p':
+ tracker.m_visual_debug = true;
+ visualize_delay = 500;
+ break;
case 'h':
- std::cerr << "Usage: \n"
- << argv[0] << " [options]\n"
- << 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 <output.txt>\n"
- << " --debug | -d\n"
- << " --fit | -f[WxH]\n";
+ std::cerr
+ << "Usage: \n"
+ << argv[0] << " [options]\n"
+ << 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 <output.txt>\n"
+ << " --debug | -d\n"
+ << " --visualDebug | -p\n"
+ << " --fit | -f[WxH]\n";
exit(0);
break;
case 'o':
sizes.erase(0, pos + delimiter.length());
fit_size_x = stol(first_argument);
- fit_size_y = stol(sizes);
+ fit_size_y = stol(sizes);
break;
}
}
case 0:
region = access("groundtruth.txt", F_OK) == 0 ? "groundtruth.txt" : "region.txt";
images = "images.txt";
- if (output.empty())
- output = "output.txt";
+ if (output.empty()) output = "output.txt";
break;
case 2:
// Fall through
cv::Mat image;
- //img = firts frame, initPos = initial position in the first frame
+ // img = firts frame, initPos = initial position in the first frame
cv::Rect init_rect = vot_io.getInitRectangle();
vot_io.outputBoundingBox(init_rect);
vot_io.getNextImage(image);
cv::Rect bb_rect;
double avg_time = 0., sum_accuracy = 0.;
int frames = 0;
- while (vot_io.getNextImage(image) == 1){
+ while (vot_io.getNextImage(image) == 1) {
double time_profile_counter = cv::getCPUTickCount();
tracker.track(image);
time_profile_counter = cv::getCPUTickCount() - time_profile_counter;
frames++;
bb = tracker.getBBox();
- bb_rect = 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()) {
cv::Rect groundtruthRect;
double accuracy = calcAccuracy(line, bb_rect, groundtruthRect);
- if (visualize_delay >= 0)
- cv::rectangle(image, groundtruthRect, CV_RGB(255, 0,0), 1);
+ 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, bb_rect, CV_RGB(0,255,0), 2);
+ cv::Point pt(bb.cx, bb.cy);
+ cv::Size size(bb.w, bb.h);
+ cv::RotatedRect rotatedRectangle(pt, size, bb.a);
+
+ cv::Point2f vertices[4];
+ rotatedRectangle.points(vertices);
+
+ for (int i = 0; i < 4; i++)
+ cv::line(image, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 2);
+ // 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);
+ std::string angle = std::to_string(bb.a);
+ angle.erase(angle.find_last_not_of('0') + 1, std::string::npos);
+ angle.erase(angle.find_last_not_of('.') + 1, std::string::npos);
+ cv::putText(image, "Frame: " + std::to_string(frames) + " " + angle + " angle",
+ cv::Point(0, image.rows - 1), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 0), 2);
cv::imshow("output", image);
int ret = cv::waitKey(visualize_delay);
- if (visualize_delay > 0 && ret != -1 && ret != 255)
- break;
+ if (visualize_delay > 0 && ret != -1 && ret != 255) break;
}
-// std::stringstream s;
-// std::string ss;
-// int countTmp = frames;
-// s << "imgs" << "/img" << (countTmp/10000);
-// countTmp = countTmp%10000;
-// s << (countTmp/1000);
-// countTmp = countTmp%1000;
-// s << (countTmp/100);
-// countTmp = countTmp%100;
-// s << (countTmp/10);
-// countTmp = countTmp%10;
-// s << (countTmp);
-// s << ".jpg";
-// s >> ss;
-// //set image output parameters
-// std::vector<int> compression_params;
-// compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
-// compression_params.push_back(90);
-// cv::imwrite(ss.c_str(), image, compression_params);
+ // std::stringstream s;
+ // std::string ss;
+ // int countTmp = frames;
+ // s << "imgs" << "/img" << (countTmp/10000);
+ // countTmp = countTmp%10000;
+ // s << (countTmp/1000);
+ // countTmp = countTmp%1000;
+ // s << (countTmp/100);
+ // countTmp = countTmp%100;
+ // s << (countTmp/10);
+ // countTmp = countTmp%10;
+ // s << (countTmp);
+ // s << ".jpg";
+ // s >> ss;
+ // //set image output parameters
+ // std::vector<int> compression_params;
+ // compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
+ // compression_params.push_back(90);
+ // cv::imwrite(ss.c_str(), image, compression_params);
}
std::cout << "Average processing speed: " << avg_time/frames << "ms (" << 1./(avg_time/frames)*1000 << " fps)";
IF(FFT STREQUAL "fftw")
target_link_libraries(kcf ${FFTW_LDFLAGS})
IF(OPENMP)
- target_link_libraries(kcf fftw3_omp)
+ target_link_libraries(kcf fftw3f_omp)
ELSEIF(NOT ASYNC)
- target_link_libraries(kcf fftw3_threads)
+ target_link_libraries(kcf fftw3f_threads)
ENDIF()
ENDIF() #FFTW
#include <omp.h>
#endif
-#if !defined(ASYNC) && !defined(OPENMP) && !defined(CUFFTW)
-#define FFTW_PLAN_WITH_THREADS() fftw_plan_with_nthreads(4);
+#if (defined(BIG_BATCH) && !defined(CUFFTW)) || (!defined(ASYNC) && !defined(OPENMP) && !defined(CUFFTW))
+#define FFTW_PLAN_WITH_THREADS() fftwf_plan_with_nthreads(4);
+#define FFTW_INIT_THREAD() fftwf_init_threads();
+#define FFTW_CLEAN_THREADS() fftwf_cleanup_threads();
#else
#define FFTW_PLAN_WITH_THREADS()
+#define FFTW_INIT_THREAD()
+#define FFTW_CLEAN_THREADS()
#endif
-Fftw::Fftw(){}
+Fftw::Fftw() {}
void Fftw::init(unsigned width, unsigned height, unsigned num_of_feats, unsigned num_of_scales, bool big_batch_mode)
{
m_num_of_scales = num_of_scales;
m_big_batch_mode = big_batch_mode;
-#if (!defined(ASYNC) && !defined(CUFFTW)) && defined(OPENMP)
- fftw_init_threads();
-#endif // OPENMP
-
#ifndef CUFFTW
std::cout << "FFT: FFTW" << std::endl;
#else
std::cout << "FFT: cuFFTW" << std::endl;
#endif
- fftwf_cleanup();
+
+ FFTW_INIT_THREAD();
+
// FFT forward one scale
{
cv::Mat in_f = cv::Mat::zeros(int(m_height), int(m_width), CV_32FC1);
ComplexMat out_f(int(m_height), m_width / 2 + 1, 1);
+
+ FFTW_PLAN_WITH_THREADS();
plan_f = fftwf_plan_dft_r2c_2d(int(m_height), int(m_width), reinterpret_cast<float *>(in_f.data),
reinterpret_cast<fftwf_complex *>(out_f.get_p_data()), FFTW_PATIENT);
}
fftwf_destroy_plan(plan_fw_all_scales);
fftwf_destroy_plan(plan_i_1ch_all_scales);
}
+ FFTW_CLEAN_THREADS();
}
#endif // OPENMP
#define DEBUG_PRINT(obj) \
- if (m_debug) { \
+ if (m_debug || m_visual_debug) { \
std::cout << #obj << " @" << __LINE__ << std::endl << (obj) << std::endl; \
}
#define DEBUG_PRINTM(obj) \
p_roi.width = p_windows_size.width / p_cell_size;
p_roi.height = p_windows_size.height / p_cell_size;
- p_num_of_feats = 31;
- if (m_use_color) p_num_of_feats += 3;
- if (m_use_cnfeat) p_num_of_feats += 10;
-
p_scales.clear();
- if (m_use_scale)
+ if (m_use_scale) {
for (int i = -int(p_num_scales) / 2; i <= int(p_num_scales) / 2; ++i)
p_scales.push_back(std::pow(p_scale_step, i));
- else
+ } else {
p_scales.push_back(1.);
+ p_num_scales = 1;
+ }
+
+ if (m_use_angle) {
+ for (int i = p_angle_min; i <= p_angle_max; i += p_angle_step)
+ p_angles.push_back(i);
+ } else {
+ p_angles.push_back(0);
+ p_num_angles = 1;
+ }
#ifdef CUFFT
if (p_roi.height * (p_roi.width / 2 + 1) > 1024) {
std::cerr << "Window after forward FFT is too big for CUDA kernels. Plese use -f to set "
"the window dimensions so its size is less or equal to "
<< 1024 * p_cell_size * p_cell_size * 2 + 1
- << " pixels . Currently the size of the window is: " << p_windows_size.width << "x" << p_windows_size.height
- << " which is " << p_windows_size.width * p_windows_size.height << " pixels. " << std::endl;
+ << " pixels . Currently the size of the window is: " << p_windows_size.width << "x"
+ << p_windows_size.height << " which is " << p_windows_size.width * p_windows_size.height
+ << " pixels. " << std::endl;
std::exit(EXIT_FAILURE);
}
std::cerr << "cuFFT supports only Gaussian kernel." << std::endl;
std::exit(EXIT_FAILURE);
}
+
CudaSafeCall(cudaSetDeviceFlags(cudaDeviceMapHost));
+
p_rot_labels_data = DynMem(p_roi.width * p_roi.height * sizeof(float));
p_rot_labels = cv::Mat(p_roi, CV_32FC1, p_rot_labels_data.hostMem());
-#else
- p_xf.create(p_roi.height, p_roi.height / 2 + 1, p_num_of_feats);
#endif
#if defined(CUFFT) || defined(FFTW)
p_yf.create(p_roi.height, width, 1);
p_xf.create(p_roi.height, width, p_num_of_feats);
- int max = m_use_big_batch ? 2 : p_num_scales;
- for (int i = 0; i < max; ++i) {
- if (m_use_big_batch && i == 1)
- p_threadctxs.emplace_back(p_roi, p_num_of_feats * p_num_scales, 1, p_num_scales);
- else
- p_threadctxs.emplace_back(p_roi, p_num_of_feats, p_scales[i], 1);
+ int max1 = m_use_big_batch ? 2 : p_num_scales;
+ int max2 = m_use_big_batch ? 1 : p_num_angles;
+ for (int i = 0; i < max1; ++i) {
+ for (int j = 0; j < max2; ++j) {
+ if (m_use_big_batch && i == 1)
+ p_threadctxs.emplace_back(p_roi, p_num_of_feats * p_num_scales * p_num_angles, 1, 0, p_num_scales,
+ p_num_angles);
+ else
+ p_threadctxs.emplace_back(p_roi, p_num_of_feats, p_scales[i], p_angles[j]);
+ }
}
p_current_scale = 1.;
p_output_sigma = std::sqrt(p_pose.w * p_pose.h) * p_output_sigma_factor / static_cast<double>(p_cell_size);
- fft.init(p_roi.width, p_roi.height, p_num_of_feats, p_num_scales, m_use_big_batch);
+ fft.init(p_roi.width, p_roi.height, p_num_of_feats, p_num_scales * p_num_angles, m_use_big_batch);
fft.set_window(cosine_window_function(p_roi.width, p_roi.height));
// window weights, i.e. labels
- fft.forward(
- gaussian_shaped_labels(p_output_sigma, p_roi.width, p_roi.height), p_yf,
- m_use_cuda ? p_rot_labels_data.deviceMem() : nullptr, p_threadctxs.front().stream);
+ fft.forward(gaussian_shaped_labels(p_output_sigma, p_roi.width, p_roi.height), p_yf,
+ m_use_cuda ? p_rot_labels_data.deviceMem() : nullptr, p_threadctxs.front().stream);
DEBUG_PRINTM(p_yf);
// obtain a sub-window for training initial model
p_threadctxs.front().patch_feats.clear();
- get_features(input_rgb, input_gray, p_pose.cx, p_pose.cy, p_windows_size.width, p_windows_size.height,
- p_threadctxs.front());
+
+ int size_x_scaled = floor(p_windows_size.width);
+ int size_y_scaled = floor(p_windows_size.height);
+
+ cv::Mat patch_gray = get_subwindow(input_gray, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_gray, p_windows_size.width, p_windows_size.height, 0, false);
+
+ cv::Mat patch_rgb = cv::Mat::zeros(size_y_scaled, size_x_scaled, CV_32F);
+ if ((m_use_color || m_use_cnfeat) && input_rgb.channels() == 3) {
+ patch_rgb = get_subwindow(input_rgb, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_rgb, p_windows_size.width, p_windows_size.height, 0, false);
+ }
+
+ get_features(patch_rgb, patch_gray, p_threadctxs.front());
fft.forward_window(p_threadctxs.front().patch_feats, p_model_xf, p_threadctxs.front().fw_all,
m_use_cuda ? p_threadctxs.front().data_features.deviceMem() : nullptr,
p_threadctxs.front().stream);
DEBUG_PRINTM(p_model_xf);
+
#if !defined(BIG_BATCH) && defined(CUFFT) && (defined(ASYNC) || defined(OPENMP))
p_threadctxs.front().model_xf = p_model_xf;
p_threadctxs.front().model_xf.set_stream(p_threadctxs.front().stream);
BBox_c tmp = p_pose;
tmp.w *= p_current_scale;
tmp.h *= p_current_scale;
+ tmp.a = p_current_angle;
if (p_resize_image) tmp.scale(1 / p_downscale_factor);
if (p_fit_to_pw2) {
void KCF_Tracker::track(cv::Mat &img)
{
- if (m_debug) std::cout << "NEW FRAME" << '\n';
+ if (m_debug || m_visual_debug) std::cout << "\nNEW FRAME" << std::endl;
cv::Mat input_gray, input_rgb = img.clone();
if (img.channels() == 3) {
cv::cvtColor(img, input_gray, CV_BGR2GRAY);
cv::resize(input_rgb, input_rgb, cv::Size(0, 0), p_scale_factor_x, p_scale_factor_y, cv::INTER_LINEAR);
}
}
-
max_response = -1.;
ThreadCtx *max = nullptr;
cv::Point2i *max_response_pt = nullptr;
});
for (auto const &it : p_threadctxs)
it.async_res.wait();
-
#else // !ASYNC
- // FIXME: Iterate correctly in big batch mode - perhaps have only one element in the list
NORMAL_OMP_PARALLEL_FOR
- for (uint i = 0; i < p_threadctxs.size(); ++i)
+ for (uint i = m_use_big_batch ? 1 : 0; i < p_threadctxs.size(); ++i)
scale_track(p_threadctxs[i], input_rgb, input_gray);
#endif
}
}
#else
- // FIXME: Iterate correctly in big batch mode - perhaps have only one element in the list
- for (uint j = 0; j < p_scales.size(); ++j) {
- if (p_threadctxs[0].max_responses[j] > max_response) {
- max_response = p_threadctxs[0].max_responses[j];
- max_response_pt = &p_threadctxs[0].max_locs[j];
- max_response_map = &p_threadctxs[0].response_maps[j];
- max = &p_threadctxs[0];
+ for (uint j = 0; j < p_num_scales; ++j) {
+ for (uint k = 0; k < p_num_angles; ++k) {
+ if (p_threadctxs.back().max_responses[j + k] > max_response) {
+ max_response = p_threadctxs.back().max_responses[j + k];
+ max_response_pt = &p_threadctxs.back().max_locs[j + k];
+ max_response_map = &p_threadctxs.back().response_maps[j + k];
+ }
}
}
+ max = &p_threadctxs.back();
#endif
+ if (m_visual_debug) {
+ cv::Mat all_responses(cv::Size(p_num_angles* p_debug_image_size, p_num_scales * p_debug_image_size),
+ p_debug_scale_responses[0].type(), cv::Scalar::all(0));
+ cv::Mat all_subwindows(cv::Size(p_num_angles* p_debug_image_size, p_num_scales* p_debug_image_size),
+ p_debug_subwindows[0].type(), cv::Scalar::all(0));
+ for (size_t i = 0; i < p_num_scales; ++i) {
+ for (size_t j = 0; j < p_num_angles; ++j) {
+ cv::Mat in_roi(all_responses, cv::Rect(j * p_debug_image_size, i * p_debug_image_size,
+ p_debug_image_size, p_debug_image_size));
+ p_debug_scale_responses[5 * i + j].copyTo(in_roi);
+ in_roi = all_subwindows(
+ cv::Rect(j * p_debug_image_size, i * p_debug_image_size, p_debug_image_size, p_debug_image_size));
+ p_debug_subwindows[5 * i + j].copyTo(in_roi);
+ }
+ }
+ cv::namedWindow("All subwindows", CV_WINDOW_AUTOSIZE);
+ cv::imshow("All subwindows", all_subwindows);
+ cv::namedWindow("All responses", CV_WINDOW_AUTOSIZE);
+ cv::imshow("All responses", all_responses);
+ cv::waitKey();
+ p_debug_scale_responses.clear();
+ p_debug_subwindows.clear();
+ }
DEBUG_PRINTM(*max_response_map);
DEBUG_PRINT(*max_response_pt);
new_location = sub_pixel_peak(*max_response_pt, *max_response_map);
DEBUG_PRINT(new_location);
+ if (m_visual_debug) std::cout << "Old p_pose, cx: " << p_pose.cx << " cy: " << p_pose.cy << std::endl;
+
p_pose.cx += p_current_scale * p_cell_size * double(new_location.x);
p_pose.cy += p_current_scale * p_cell_size * double(new_location.y);
+
+ if (m_visual_debug) std::cout << "New p_pose, cx: " << p_pose.cx << " cy: " << p_pose.cy << std::endl;
+
if (p_fit_to_pw2) {
if (p_pose.cx < 0) p_pose.cx = 0;
if (p_pose.cx > (img.cols * p_scale_factor_x) - 1) p_pose.cx = (img.cols * p_scale_factor_x) - 1;
if (p_current_scale < p_min_max_scale[0]) p_current_scale = p_min_max_scale[0];
if (p_current_scale > p_min_max_scale[1]) p_current_scale = p_min_max_scale[1];
+ p_current_angle = (p_current_angle + max->angle) < 0
+ ? -std::abs(p_current_angle + max->angle) % 360
+ : (p_current_angle + max->angle) % 360;
+
// obtain a subwindow for training at newly estimated target position
+ int size_x_scaled = floor(p_windows_size.width * p_current_scale);
+ int size_y_scaled = floor(p_windows_size.height * p_current_scale);
+
+ cv::Mat patch_gray = get_subwindow(input_gray, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_gray, p_windows_size.width, p_windows_size.height, p_current_angle, false);
+
+ cv::Mat patch_rgb = cv::Mat::zeros(size_y_scaled, size_x_scaled, CV_32F);
+ if ((m_use_color || m_use_cnfeat) && input_rgb.channels() == 3) {
+ patch_rgb = get_subwindow(input_rgb, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_rgb, p_windows_size.width, p_windows_size.height, p_current_angle, false);
+ }
+
p_threadctxs.front().patch_feats.clear();
- get_features(input_rgb, input_gray, p_pose.cx, p_pose.cy, p_windows_size.width, p_windows_size.height,
- p_threadctxs.front(), p_current_scale);
+ get_features(patch_rgb, patch_gray, p_threadctxs.front());
fft.forward_window(p_threadctxs.front().patch_feats, p_xf, p_threadctxs.front().fw_all,
m_use_cuda ? p_threadctxs.front().data_features.deviceMem() : nullptr, p_threadctxs.front().stream);
if (m_use_big_batch) {
vars.patch_feats.clear();
BIG_BATCH_OMP_PARALLEL_FOR
- for (uint i = 0; i < p_num_scales; ++i) {
- get_features(input_rgb, input_gray, this->p_pose.cx, this->p_pose.cy, this->p_windows_size.width,
- this->p_windows_size.height, vars, this->p_current_scale * this->p_scales[i]);
+ for (uint i = 0; i < this->p_scales.size(); ++i) {
+ for (uint j = 0; j < this->p_angles.size(); ++j) {
+ int size_x_scaled = floor(this->p_windows_size.width * this->p_current_scale * this->p_scales[i]);
+ int size_y_scaled = floor(this->p_windows_size.height * this->p_current_scale * this->p_scales[i]);
+
+ cv::Mat patch_gray =
+ get_subwindow(input_gray, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_gray, p_windows_size.width, p_windows_size.height,
+ p_current_scale * this->p_scales[i], p_current_angle + this->p_angles[j]);
+
+ cv::Mat patch_rgb = cv::Mat::zeros(size_y_scaled, size_x_scaled, CV_32F);
+ if ((m_use_color || m_use_cnfeat) && input_rgb.channels() == 3) {
+ patch_rgb =
+ get_subwindow(input_rgb, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_rgb, p_windows_size.width, p_windows_size.height,
+ p_current_scale * this->p_scales[i], p_current_angle + this->p_angles[j]);
+ }
+ get_features(patch_rgb, patch_gray, vars);
+ }
}
} else {
- get_features(input_rgb, input_gray, this->p_pose.cx, this->p_pose.cy, this->p_windows_size.width,
- this->p_windows_size.height, vars, this->p_current_scale * vars.scale);
+ int size_x_scaled = floor(this->p_windows_size.width * this->p_current_scale * vars.scale);
+ int size_y_scaled = floor(this->p_windows_size.height * this->p_current_scale * vars.scale);
+
+ cv::Mat patch_gray = get_subwindow(input_gray, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_gray, p_windows_size.width, p_windows_size.height, p_current_scale * vars.scale);
+
+ cv::Mat patch_rgb = cv::Mat::zeros(size_y_scaled, size_x_scaled, CV_32F);
+ if ((m_use_color || m_use_cnfeat) && input_rgb.channels() == 3) {
+ patch_rgb = get_subwindow(input_rgb, this->p_pose.cx, this->p_pose.cy, size_x_scaled, size_y_scaled);
+ geometric_transformations(patch_rgb, p_windows_size.width, p_windows_size.height, p_current_scale * vars.scale,
+ p_current_angle + vars.angle);
+ }
+ vars.patch_feats.clear();
+ get_features(patch_rgb, patch_gray, vars);
}
fft.forward_window(vars.patch_feats, vars.zf, vars.fw_all, m_use_cuda ? vars.data_features.deviceMem() : nullptr,
// ****************************************************************************
-void KCF_Tracker::get_features(cv::Mat &input_rgb, cv::Mat &input_gray, int cx, int cy, int size_x, int size_y,
- ThreadCtx &vars, double scale)
+void KCF_Tracker::get_features(cv::Mat &patch_rgb, cv::Mat &patch_gray, ThreadCtx &vars)
{
- int size_x_scaled = floor(size_x * scale);
- int size_y_scaled = floor(size_y * scale);
-
- cv::Mat patch_gray = get_subwindow(input_gray, cx, cy, size_x_scaled, size_y_scaled);
- cv::Mat patch_rgb = get_subwindow(input_rgb, cx, cy, size_x_scaled, size_y_scaled);
-
- // resize to default size
- if (scale > 1.) {
- // if we downsample use INTER_AREA interpolation
- cv::resize(patch_gray, patch_gray, cv::Size(size_x, size_y), 0., 0., cv::INTER_AREA);
- } else {
- cv::resize(patch_gray, patch_gray, cv::Size(size_x, size_y), 0., 0., cv::INTER_LINEAR);
- }
-
// get hog(Histogram of Oriented Gradients) features
vars.patch_feats = FHoG::extract(patch_gray, 2, p_cell_size, 9);
// get color rgb features (simple r,g,b channels)
std::vector<cv::Mat> color_feat;
- if ((m_use_color || m_use_cnfeat) && input_rgb.channels() == 3) {
- // resize to default size
- if (scale > 1.) {
- // if we downsample use INTER_AREA interpolation
- cv::resize(patch_rgb, patch_rgb, cv::Size(size_x / p_cell_size, size_y / p_cell_size), 0., 0.,
- cv::INTER_AREA);
- } else {
- cv::resize(patch_rgb, patch_rgb, cv::Size(size_x / p_cell_size, size_y / p_cell_size), 0., 0.,
- cv::INTER_LINEAR);
- }
- }
- if (m_use_color && input_rgb.channels() == 3) {
+ if (m_use_color && patch_rgb.channels() == 3) {
// use rgb color space
cv::Mat patch_rgb_norm;
patch_rgb.convertTo(patch_rgb_norm, CV_32F, 1. / 255., -0.5);
color_feat.insert(color_feat.end(), rgb.begin(), rgb.end());
}
- if (m_use_cnfeat && input_rgb.channels() == 3) {
+ if (m_use_cnfeat && patch_rgb.channels() == 3) {
std::vector<cv::Mat> cn_feat = CNFeat::extract(patch_rgb);
color_feat.insert(color_feat.end(), cn_feat.begin(), cn_feat.end());
}
if (x2 - x1 == 0 || y2 - y1 == 0)
patch = cv::Mat::zeros(height, width, CV_32FC1);
- else {
+ else
cv::copyMakeBorder(input(cv::Range(y1, y2), cv::Range(x1, x2)), patch, top, bottom, left, right,
cv::BORDER_REPLICATE);
- // imshow( "copyMakeBorder", patch);
- // cv::waitKey();
- }
// sanity check
assert(patch.cols == width && patch.rows == height);
return patch;
}
-void KCF_Tracker::gaussian_correlation(struct ThreadCtx &vars, const ComplexMat &xf, const ComplexMat &yf,
- double sigma, bool auto_correlation)
+void KCF_Tracker::geometric_transformations(cv::Mat &patch, int size_x, int size_y, int angle, bool allow_debug)
+{
+ if (m_use_angle) {
+ cv::Point2f center((patch.cols - 1) / 2., (patch.rows - 1) / 2.);
+ cv::Mat r = cv::getRotationMatrix2D(center, angle, 1.0);
+
+ cv::warpAffine(patch, patch, r, cv::Size(patch.cols, patch.rows), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
+ }
+
+ // resize to default size
+ if (patch.channels() != 3) {
+ if (patch.cols / size_x > 1.) {
+ // if we downsample use INTER_AREA interpolation
+ cv::resize(patch, patch, cv::Size(size_x, size_y), 0., 0., cv::INTER_AREA);
+ } else {
+ cv::resize(patch, patch, cv::Size(size_x, size_y), 0., 0., cv::INTER_LINEAR);
+ }
+ } else {
+ if (patch.cols / size_x > 1.) {
+ // if we downsample use INTER_AREA interpolation
+ cv::resize(patch, patch, cv::Size(size_x / p_cell_size, size_y / p_cell_size), 0., 0., cv::INTER_AREA);
+ } else {
+ cv::resize(patch, patch, cv::Size(size_x / p_cell_size, size_y / p_cell_size), 0., 0., cv::INTER_LINEAR);
+ }
+ if (m_visual_debug && allow_debug) {
+ cv::Mat input_clone = patch.clone();
+ cv::resize(input_clone, input_clone, cv::Size(p_debug_image_size, p_debug_image_size), 0., 0.,
+ cv::INTER_LINEAR);
+
+ std::string angle_string = std::to_string(p_current_angle + angle);
+
+ cv::putText(input_clone, angle_string, cv::Point(1, input_clone.rows - 5), cv::FONT_HERSHEY_COMPLEX_SMALL,
+ 0.5, cv::Scalar(0, 255, 0), 1);
+
+ p_debug_subwindows.push_back(input_clone);
+ }
+ }
+}
+
+void KCF_Tracker::gaussian_correlation(struct ThreadCtx &vars, const ComplexMat &xf, const ComplexMat &yf, double sigma,
+ bool auto_correlation)
{
#ifdef CUFFT
xf.sqr_norm(vars.xf_sqr_norm.deviceMem());
#include "threadctx.hpp"
#include "pragmas.h"
-struct BBox_c
-{
- double cx, cy, w, h;
+struct BBox_c {
+ double cx, cy, w, h, a;
inline void scale(double factor)
{
cx *= factor;
cy *= factor;
- w *= factor;
- h *= factor;
+ w *= factor;
+ h *= factor;
}
inline void scale_x(double factor)
{
cx *= factor;
- w *= factor;
+ w *= factor;
}
inline void scale_y(double factor)
{
cy *= factor;
- h *= factor;
- }
-
- inline cv::Rect get_rect()
- {
- return cv::Rect(int(cx-w/2.), int(cy-h/2.), int(w), int(h));
+ h *= factor;
}
+ inline cv::Rect get_rect() { return cv::Rect(int(cx - w / 2.), int(cy - h / 2.), int(w), int(h)); }
};
-class KCF_Tracker
-{
-public:
- bool m_debug {false};
- bool m_use_scale {true};
- bool m_use_color {true};
+class KCF_Tracker {
+ public:
+ bool m_debug{false};
+ bool m_visual_debug{false};
+ bool m_use_scale{true};
+ bool m_use_angle{false}; // Doesn't work with FFTW-BIG version
+ bool m_use_color{true};
#ifdef ASYNC
- bool m_use_multithreading {true};
+ bool m_use_multithreading{true};
#else
- bool m_use_multithreading {false};
-#endif //ASYNC
- bool m_use_subpixel_localization {true};
- bool m_use_subgrid_scale {true};
- bool m_use_cnfeat {true};
- bool m_use_linearkernel {false};
+ bool m_use_multithreading{false};
+#endif // ASYNC
+ bool m_use_subpixel_localization{true};
+ bool m_use_subgrid_scale{true};
+ bool m_use_cnfeat{true};
+ bool m_use_linearkernel{false};
#ifdef BIG_BATCH
- bool m_use_big_batch {true};
+ bool m_use_big_batch{true};
#else
- bool m_use_big_batch {false};
+ bool m_use_big_batch{false};
#endif
#ifdef CUFFT
- bool m_use_cuda {true};
+ bool m_use_cuda{true};
#else
- bool m_use_cuda {false};
+ bool m_use_cuda{false};
#endif
/*
output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
cell_size ... hog cell size (4)
*/
- KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
+ KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor,
+ int cell_size);
KCF_Tracker();
~KCF_Tracker();
// Init/re-init methods
- void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x, int fit_size_y);
- void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x, int fit_size_y);
- void updateTrackerPosition(BBox_c & bbox);
+ void init(cv::Mat &img, const cv::Rect &bbox, int fit_size_x, int fit_size_y);
+ void setTrackerPose(BBox_c &bbox, cv::Mat &img, int fit_size_x, int fit_size_y);
+ void updateTrackerPosition(BBox_c &bbox);
// frame-to-frame object tracking
- void track(cv::Mat & img);
+ void track(cv::Mat &img);
BBox_c getBBox();
double getFilterResponse() const; // Measure of tracking accuracy
-private:
+ private:
Fft &fft;
BBox_c p_pose;
double p_padding = 1.5;
double p_output_sigma_factor = 0.1;
double p_output_sigma;
- double p_kernel_sigma = 0.5; //def = 0.5
- double p_lambda = 1e-4; //regularization in learning step
- double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
- int p_cell_size = 4; //4 for hog (= bin_size)
+ double p_kernel_sigma = 0.5; // def = 0.5
+ double p_lambda = 1e-4; // regularization in learning step
+ double p_interp_factor = 0.02; // def = 0.02, linear interpolation factor for adaptation
+ int p_cell_size = 4; // 4 for hog (= bin_size)
cv::Size p_windows_size;
uint p_num_scales {7};
double p_scale_step = 1.02;
double p_current_scale = 1.;
double p_min_max_scale[2];
std::vector<double> p_scales;
+ int p_current_angle = 0;
+ uint p_num_angles {5};
+ int p_angle_min = -20, p_angle_max = 20;
+ int p_angle_step = 10;
+ std::vector<int> p_angles;
+
+ // for visual debug
+ int p_debug_image_size = 100;
+ int p_count = 0;
+ std::vector<cv::Mat> p_debug_scale_responses;
+ std::vector<cv::Mat> p_debug_subwindows;
//for big batch
- int p_num_of_feats;
+ int p_num_of_feats = 31 + (m_use_color ? 3 : 0) + (m_use_cnfeat ? 10 : 0);
cv::Size p_roi;
std::vector<ThreadCtx> p_threadctxs;
cv::Mat p_rot_labels;
DynMem p_rot_labels_data;
- //model
+ // model
ComplexMat p_yf;
ComplexMat p_model_alphaf;
ComplexMat p_model_alphaf_num;
void scale_track(ThreadCtx & vars, cv::Mat & input_rgb, cv::Mat & input_gray);
cv::Mat get_subwindow(const cv::Mat & input, int cx, int cy, int size_x, int size_y);
cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
- void gaussian_correlation(struct ThreadCtx &vars, const ComplexMat & xf, const ComplexMat & yf, double sigma, bool auto_correlation = false);
- cv::Mat circshift(const cv::Mat & patch, int x_rot, int y_rot);
+ void gaussian_correlation(struct ThreadCtx &vars, const ComplexMat &xf, const ComplexMat &yf, double sigma,
+ bool auto_correlation = false);
+ cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot);
cv::Mat cosine_window_function(int dim1, int dim2);
- void get_features(cv::Mat & input_rgb, cv::Mat & input_gray, int cx, int cy, int size_x, int size_y, ThreadCtx & vars, double scale = 1.);
- cv::Point2f sub_pixel_peak(cv::Point & max_loc, cv::Mat & response);
+ void get_features(cv::Mat &patch_rgb, cv::Mat &patch_gray, ThreadCtx &vars);
+ void geometric_transformations(cv::Mat &patch, int size_x, int size_y, int angle = 0, bool allow_debug = true);
+ cv::Point2f sub_pixel_peak(cv::Point &max_loc, cv::Mat &response);
double sub_grid_scale(uint index);
-
};
-#endif //KCF_HEADER_6565467831231
+#endif // KCF_HEADER_6565467831231
struct ThreadCtx {
public:
- ThreadCtx(cv::Size roi, uint num_of_feats, double scale, uint num_of_scales)
- : scale(scale)
+ ThreadCtx(cv::Size roi, uint num_of_feats, double scale, int angle, uint num_of_scales = 1, uint num_of_angles = 1)
+ : scale(scale), angle(angle)
{
- this->xf_sqr_norm = DynMem(num_of_scales * sizeof(float));
+ this->xf_sqr_norm = DynMem(num_of_scales * num_of_angles * sizeof(float));
this->yf_sqr_norm = DynMem(sizeof(float));
this->patch_feats.reserve(uint(num_of_feats));
#endif
#if defined(CUFFT) || defined(FFTW)
- this->gauss_corr_res = DynMem(cells_size * num_of_scales);
+ this->gauss_corr_res = DynMem(cells_size * num_of_scales * num_of_angles);
this->data_features = DynMem(cells_size * num_of_feats);
uint width_freq = roi.width / 2 + 1;
- this->in_all = cv::Mat(roi.height * num_of_scales, roi.width, CV_32F, this->gauss_corr_res.hostMem());
+ this->in_all = cv::Mat(roi.height * num_of_scales * num_of_angles, roi.width, CV_32F, this->gauss_corr_res.hostMem());
this->fw_all = cv::Mat(roi.height * num_of_feats, roi.width, CV_32F, this->data_features.hostMem());
#else
uint width_freq = roi.width;
#endif
this->data_i_features = DynMem(cells_size * num_of_feats);
- this->data_i_1ch = DynMem(cells_size * num_of_scales);
+ this->data_i_1ch = DynMem(cells_size * num_of_scales * num_of_angles);
this->ifft2_res = cv::Mat(roi, CV_32FC(num_of_feats), this->data_i_features.hostMem());
- this->response = cv::Mat(roi, CV_32FC(num_of_scales), this->data_i_1ch.hostMem());
+ this->response = cv::Mat(roi, CV_32FC(num_of_scales * num_of_angles), this->data_i_1ch.hostMem());
this->patch_feats.reserve(num_of_feats);
#ifdef CUFFT
- this->zf.create(roi.height, width_freq, num_of_feats, num_of_scales, this->stream);
- this->kzf.create(roi.height, width_freq, num_of_scales, this->stream);
- this->kf.create(roi.height, width_freq, num_of_scales, this->stream);
+ this->zf.create(roi.height, width_freq, num_of_feats, num_of_scales * num_of_angles, this->stream);
+ this->kzf.create(roi.height, width_freq, num_of_scales * num_of_angles, this->stream);
+ this->kf.create(roi.height, width_freq, num_of_scales * num_of_angles, this->stream);
#else
- this->zf.create(roi.height, width_freq, num_of_feats, num_of_scales);
- this->kzf.create(roi.height, width_freq, num_of_scales);
- this->kf.create(roi.height, width_freq, num_of_scales);
+ this->zf.create(roi.height, width_freq, num_of_feats, num_of_scales * num_of_angles);
+ this->kzf.create(roi.height, width_freq, num_of_scales * num_of_angles);
+ this->kf.create(roi.height, width_freq, num_of_scales * num_of_angles);
#endif
if (num_of_scales > 1) {
- this->max_responses.reserve(num_of_scales);
- this->max_locs.reserve(num_of_scales);
- this->response_maps.reserve(num_of_scales);
+ this->max_responses.reserve(num_of_scales * num_of_angles);
+ this->max_locs.reserve(num_of_scales * num_of_angles);
+ this->response_maps.reserve(num_of_scales * num_of_angles);
}
}
ThreadCtx(ThreadCtx &&) = default;
}
const double scale;
+ const int angle;
#ifdef ASYNC
std::future<void> async_res;
#endif
projects / hercules2020 / kcf.git / commitdiff