--- /dev/null
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc_c.h> // cvFindContours
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/objdetect/objdetect.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <iterator>
+#include <set>
+#include <cstdio>
+#include <iostream>
+
+// Function prototypes
+void subtractPlane(const cv::Mat& depth, cv::Mat& mask, std::vector<CvPoint>& chain, double f);
+
+std::vector<CvPoint> maskFromTemplate(const std::vector<cv::linemod::Template>& templates,
+ int num_modalities, cv::Point offset, cv::Size size,
+ cv::Mat& mask, cv::Mat& dst);
+
+void templateConvexHull(const std::vector<cv::linemod::Template>& templates,
+ int num_modalities, cv::Point offset, cv::Size size,
+ cv::Mat& dst);
+
+void drawResponse(const std::vector<cv::linemod::Template>& templates,
+ int num_modalities, cv::Mat& dst, cv::Point offset, int T);
+
+cv::Mat displayQuantized(const cv::Mat& quantized);
+
+// Copy of cv_mouse from cv_utilities
+class Mouse
+{
+public:
+ static void start(const std::string& a_img_name)
+ {
+ cvSetMouseCallback(a_img_name.c_str(), Mouse::cv_on_mouse, 0);
+ }
+ static int event(void)
+ {
+ int l_event = m_event;
+ m_event = -1;
+ return l_event;
+ }
+ static int x(void)
+ {
+ return m_x;
+ }
+ static int y(void)
+ {
+ return m_y;
+ }
+
+private:
+ static void cv_on_mouse(int a_event, int a_x, int a_y, int a_flags, void * a_params)
+ {
+ m_event = a_event;
+ m_x = a_x;
+ m_y = a_y;
+ }
+
+ static int m_event;
+ static int m_x;
+ static int m_y;
+};
+int Mouse::m_event;
+int Mouse::m_x;
+int Mouse::m_y;
+
+void help()
+{
+ printf("Usage: openni_demo [templates.yml]\n\n"
+ "Place your object on a planar, featureless surface. With the mouse,\n"
+ "frame it in the 'color' window and right click to learn a first template.\n"
+ "Then press 'l' to enter online learning mode, and move the camera around.\n"
+ "When the match score falls between 90-95%% the demo will add a new template.\n\n"
+ "Keys:\n"
+ "\t h -- This help page\n"
+ "\t l -- Toggle online learning\n"
+ "\t m -- Toggle printing match result\n"
+ "\t t -- Toggle printing timings\n"
+ "\t w -- Write learned templates to disk\n"
+ "\t [ ] -- Adjust matching threshold: '[' down, ']' up\n"
+ "\t q -- Quit\n\n");
+}
+
+// Adapted from cv_timer in cv_utilities
+class Timer
+{
+public:
+ Timer() : start_(0), time_(0) {}
+
+ void start()
+ {
+ start_ = cv::getTickCount();
+ }
+
+ void stop()
+ {
+ CV_Assert(start_ != 0);
+ int64 end = cv::getTickCount();
+ time_ += end - start_;
+ start_ = 0;
+ }
+
+ double time()
+ {
+ double ret = time_ / cv::getTickFrequency();
+ time_ = 0;
+ return ret;
+ }
+
+private:
+ int64 start_, time_;
+};
+
+// Functions to store detector and templates in single XML/YAML file
+cv::Ptr<cv::linemod::Detector> readLinemod(const std::string& filename)
+{
+ cv::Ptr<cv::linemod::Detector> detector = new cv::linemod::Detector;
+ cv::FileStorage fs(filename, cv::FileStorage::READ);
+ detector->read(fs.root());
+
+ cv::FileNode fn = fs["classes"];
+ for (cv::FileNodeIterator i = fn.begin(), iend = fn.end(); i != iend; ++i)
+ detector->readClass(*i);
+
+ return detector;
+}
+
+void writeLinemod(const cv::Ptr<cv::linemod::Detector>& detector, const std::string& filename)
+{
+ cv::FileStorage fs(filename, cv::FileStorage::WRITE);
+ detector->write(fs);
+
+ std::vector<std::string> ids = detector->classIds();
+ fs << "classes" << "[";
+ for (int i = 0; i < (int)ids.size(); ++i)
+ {
+ fs << "{";
+ detector->writeClass(ids[i], fs);
+ fs << "}"; // current class
+ }
+ fs << "]"; // classes
+}
+
+
+int main(int argc, char * argv[])
+{
+ // Various settings and flags
+ bool show_match_result = true;
+ bool show_timings = false;
+ bool learn_online = false;
+ int num_classes = 0;
+ int matching_threshold = 80;
+ /// @todo Keys for changing these?
+ cv::Size roi_size(200, 200);
+ int learning_lower_bound = 90;
+ int learning_upper_bound = 95;
+
+ // Timers
+ Timer extract_timer;
+ Timer match_timer;
+
+ // Initialize HighGUI
+ help();
+ cv::namedWindow("color");
+ cv::namedWindow("normals");
+ Mouse::start("color");
+
+ // Initialize LINEMOD data structures
+ cv::Ptr<cv::linemod::Detector> detector;
+ std::string filename;
+ if (argc == 1)
+ {
+ filename = "linemod_templates.yml";
+ detector = cv::linemod::getDefaultLINEMOD();
+ }
+ else
+ {
+ detector = readLinemod(argv[1]);
+
+ std::vector<std::string> ids = detector->classIds();
+ num_classes = detector->numClasses();
+ printf("Loaded %s with %d classes and %d templates\n",
+ argv[1], num_classes, detector->numTemplates());
+ if (!ids.empty())
+ {
+ printf("Class ids:\n");
+ std::copy(ids.begin(), ids.end(), std::ostream_iterator<std::string>(std::cout, "\n"));
+ }
+ }
+ int num_modalities = detector->getModalities().size();
+
+ // Open Kinect sensor
+ cv::VideoCapture capture( CV_CAP_OPENNI );
+ if (!capture.isOpened())
+ {
+ printf("Could not open OpenNI-capable sensor\n");
+ return -1;
+ }
+ capture.set(CV_CAP_PROP_OPENNI_REGISTRATION, 1);
+ double focal_length = capture.get(CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH);
+ //printf("Focal length = %f\n", focal_length);
+
+ // Main loop
+ cv::Mat color, depth;
+ while (true)
+ {
+ // Capture next color/depth pair
+ capture.grab();
+ capture.retrieve(depth, CV_CAP_OPENNI_DEPTH_MAP);
+ capture.retrieve(color, CV_CAP_OPENNI_BGR_IMAGE);
+
+ std::vector<cv::Mat> sources;
+ sources.push_back(color);
+ sources.push_back(depth);
+ cv::Mat display = color.clone();
+
+ if (!learn_online)
+ {
+ cv::Point mouse(Mouse::x(), Mouse::y());
+ int event = Mouse::event();
+
+ // Compute ROI centered on current mouse location
+ cv::Point roi_offset(roi_size.width / 2, roi_size.height / 2);
+ cv::Point pt1 = mouse - roi_offset; // top left
+ cv::Point pt2 = mouse + roi_offset; // bottom right
+
+ if (event == CV_EVENT_RBUTTONDOWN)
+ {
+ // Compute object mask by subtracting the plane within the ROI
+ std::vector<CvPoint> chain(4);
+ chain[0] = pt1;
+ chain[1] = cv::Point(pt2.x, pt1.y);
+ chain[2] = pt2;
+ chain[3] = cv::Point(pt1.x, pt2.y);
+ cv::Mat mask;
+ subtractPlane(depth, mask, chain, focal_length);
+
+ cv::imshow("mask", mask);
+
+ // Extract template
+ std::string class_id = cv::format("class%d", num_classes);
+ cv::Rect bb;
+ extract_timer.start();
+ int template_id = detector->addTemplate(sources, class_id, mask, &bb);
+ extract_timer.stop();
+ if (template_id != -1)
+ {
+ printf("*** Added template (id %d) for new object class %d***\n",
+ template_id, num_classes);
+ //printf("Extracted at (%d, %d) size %dx%d\n", bb.x, bb.y, bb.width, bb.height);
+ }
+
+ ++num_classes;
+ }
+
+ // Draw ROI for display
+ cv::rectangle(display, pt1, pt2, CV_RGB(0,0,0), 3);
+ cv::rectangle(display, pt1, pt2, CV_RGB(255,255,0), 1);
+ }
+
+ // Perform matching
+ std::vector<cv::linemod::Match> matches;
+ std::vector<std::string> class_ids;
+ std::vector<cv::Mat> quantized_images;
+ match_timer.start();
+ detector->match(sources, matching_threshold, matches, class_ids, quantized_images);
+ match_timer.stop();
+
+ int classes_visited = 0;
+ std::set<std::string> visited;
+
+ for (int i = 0; (i < (int)matches.size()) && (classes_visited < num_classes); ++i)
+ {
+ cv::linemod::Match m = matches[i];
+
+ if (visited.insert(m.class_id).second)
+ {
+ ++classes_visited;
+
+ if (show_match_result)
+ {
+ printf("Similarity: %5.1f%%; x: %3d; y: %3d; class: %s; template: %3d\n",
+ m.similarity, m.x, m.y, m.class_id.c_str(), m.template_id);
+ }
+
+ // Draw matching template
+ const std::vector<cv::linemod::Template>& templates = detector->getTemplates(m.class_id, m.template_id);
+ drawResponse(templates, num_modalities, display, cv::Point(m.x, m.y), detector->getT(0));
+
+ if (learn_online == true)
+ {
+ /// @todo Online learning possibly broken by new gradient feature extraction,
+ /// which assumes an accurate object outline.
+
+ // Compute masks based on convex hull of matched template
+ cv::Mat color_mask, depth_mask;
+ std::vector<CvPoint> chain = maskFromTemplate(templates, num_modalities,
+ cv::Point(m.x, m.y), color.size(),
+ color_mask, display);
+ subtractPlane(depth, depth_mask, chain, focal_length);
+
+ cv::imshow("mask", depth_mask);
+
+ // If pretty sure (but not TOO sure), add new template
+ if (learning_lower_bound < m.similarity && m.similarity < learning_upper_bound)
+ {
+ extract_timer.start();
+ int template_id = detector->addTemplate(sources, m.class_id, depth_mask);
+ extract_timer.stop();
+ if (template_id != -1)
+ {
+ printf("*** Added template (id %d) for existing object class %s***\n",
+ template_id, m.class_id.c_str());
+ }
+ }
+ }
+ }
+ }
+
+ if (show_match_result && matches.empty())
+ printf("No matches found...\n");
+ if (show_timings)
+ {
+ printf("Training: %.2fs\n", extract_timer.time());
+ printf("Matching: %.2fs\n", match_timer.time());
+ }
+ if (show_match_result || show_timings)
+ printf("------------------------------------------------------------\n");
+
+ cv::imshow("color", display);
+ cv::imshow("normals", quantized_images[1]);
+
+ cv::FileStorage fs;
+ char key = (char)cvWaitKey(10);
+ switch (key)
+ {
+ case 'h':
+ help();
+ break;
+ case 'm':
+ // toggle printing match result
+ show_match_result = !show_match_result;
+ printf("Show match result %s\n", show_match_result ? "ON" : "OFF");
+ break;
+ case 't':
+ // toggle printing timings
+ show_timings = !show_timings;
+ printf("Show timings %s\n", show_timings ? "ON" : "OFF");
+ break;
+ case 'l':
+ // toggle online learning
+ learn_online = !learn_online;
+ printf("Online learning %s\n", learn_online ? "ON" : "OFF");
+ break;
+ case '[':
+ // decrement threshold
+ matching_threshold = std::max(matching_threshold - 1, -100);
+ printf("New threshold: %d\n", matching_threshold);
+ break;
+ case ']':
+ // increment threshold
+ matching_threshold = std::min(matching_threshold + 1, +100);
+ printf("New threshold: %d\n", matching_threshold);
+ break;
+ case 'w':
+ // write model to disk
+ writeLinemod(detector, filename);
+ printf("Wrote detector and templates to %s\n", filename.c_str());
+ break;
+ case 'q':
+ return 0;
+ }
+ }
+ return 0;
+}
+
+void reprojectPoints(const std::vector<cv::Point3d>& proj, std::vector<cv::Point3d>& real, double f)
+{
+ real.resize(proj.size());
+ double f_inv = 1.0 / f;
+
+ for (int i = 0; i < (int)proj.size(); ++i)
+ {
+ double Z = proj[i].z;
+ real[i].x = (proj[i].x - 320.) * (f_inv * Z);
+ real[i].y = (proj[i].y - 240.) * (f_inv * Z);
+ real[i].z = Z;
+ }
+}
+
+void filterPlane(IplImage * ap_depth, std::vector<IplImage *> & a_masks, std::vector<CvPoint> & a_chain, double f)
+{
+ const int l_num_cost_pts = 200;
+
+ float l_thres = 4;
+
+ IplImage * lp_mask = cvCreateImage(cvGetSize(ap_depth), IPL_DEPTH_8U, 1);
+ cvSet(lp_mask, cvRealScalar(0));
+
+ std::vector<CvPoint> l_chain_vector;
+
+ float l_chain_length = 0;
+ float * lp_seg_length = new float[a_chain.size()];
+
+ for (int l_i = 0; l_i < (int)a_chain.size(); ++l_i)
+ {
+ float x_diff = a_chain[(l_i + 1) % a_chain.size()].x - a_chain[l_i].x;
+ float y_diff = a_chain[(l_i + 1) % a_chain.size()].y - a_chain[l_i].y;
+ lp_seg_length[l_i] = sqrt(x_diff*x_diff + y_diff*y_diff);
+ l_chain_length += lp_seg_length[l_i];
+ }
+ for (int l_i = 0; l_i < (int)a_chain.size(); ++l_i)
+ {
+ if (lp_seg_length[l_i] > 0)
+ {
+ int l_cur_num = l_num_cost_pts * lp_seg_length[l_i] / l_chain_length;
+ float l_cur_len = lp_seg_length[l_i] / l_cur_num;
+
+ for (int l_j = 0; l_j < l_cur_num; ++l_j)
+ {
+ float l_ratio = (l_cur_len * l_j / lp_seg_length[l_i]);
+
+ CvPoint l_pts;
+
+ l_pts.x = l_ratio * (a_chain[(l_i + 1) % a_chain.size()].x - a_chain[l_i].x) + a_chain[l_i].x;
+ l_pts.y = l_ratio * (a_chain[(l_i + 1) % a_chain.size()].y - a_chain[l_i].y) + a_chain[l_i].y;
+
+ l_chain_vector.push_back(l_pts);
+ }
+ }
+ }
+ std::vector<cv::Point3d> lp_src_3Dpts(l_chain_vector.size());
+
+ for (int l_i = 0; l_i < (int)l_chain_vector.size(); ++l_i)
+ {
+ lp_src_3Dpts[l_i].x = l_chain_vector[l_i].x;
+ lp_src_3Dpts[l_i].y = l_chain_vector[l_i].y;
+ lp_src_3Dpts[l_i].z = CV_IMAGE_ELEM(ap_depth, unsigned short, cvRound(lp_src_3Dpts[l_i].y), cvRound(lp_src_3Dpts[l_i].x));
+ //CV_IMAGE_ELEM(lp_mask,unsigned char,(int)lp_src_3Dpts[l_i].Y,(int)lp_src_3Dpts[l_i].X)=255;
+ }
+ //cv_show_image(lp_mask,"hallo2");
+
+ reprojectPoints(lp_src_3Dpts, lp_src_3Dpts, f);
+
+ CvMat * lp_pts = cvCreateMat(l_chain_vector.size(), 4, CV_32F);
+ CvMat * lp_v = cvCreateMat(4, 4, CV_32F);
+ CvMat * lp_w = cvCreateMat(4, 1, CV_32F);
+
+ for (int l_i = 0; l_i < (int)l_chain_vector.size(); ++l_i)
+ {
+ CV_MAT_ELEM(*lp_pts, float, l_i, 0) = lp_src_3Dpts[l_i].x;
+ CV_MAT_ELEM(*lp_pts, float, l_i, 1) = lp_src_3Dpts[l_i].y;
+ CV_MAT_ELEM(*lp_pts, float, l_i, 2) = lp_src_3Dpts[l_i].z;
+ CV_MAT_ELEM(*lp_pts, float, l_i, 3) = 1.0;
+ }
+ cvSVD(lp_pts, lp_w, 0, lp_v);
+
+ float l_n[4] = {CV_MAT_ELEM(*lp_v, float, 0, 3),
+ CV_MAT_ELEM(*lp_v, float, 1, 3),
+ CV_MAT_ELEM(*lp_v, float, 2, 3),
+ CV_MAT_ELEM(*lp_v, float, 3, 3)};
+
+ float l_norm = sqrt(l_n[0] * l_n[0] + l_n[1] * l_n[1] + l_n[2] * l_n[2]);
+
+ l_n[0] /= l_norm;
+ l_n[1] /= l_norm;
+ l_n[2] /= l_norm;
+ l_n[3] /= l_norm;
+
+ float l_max_dist = 0;
+
+ for (int l_i = 0; l_i < (int)l_chain_vector.size(); ++l_i)
+ {
+ float l_dist = l_n[0] * CV_MAT_ELEM(*lp_pts, float, l_i, 0) +
+ l_n[1] * CV_MAT_ELEM(*lp_pts, float, l_i, 1) +
+ l_n[2] * CV_MAT_ELEM(*lp_pts, float, l_i, 2) +
+ l_n[3] * CV_MAT_ELEM(*lp_pts, float, l_i, 3);
+
+ if (fabs(l_dist) > l_max_dist)
+ l_max_dist = l_dist;
+ }
+ //std::cerr << "plane: " << l_n[0] << ";" << l_n[1] << ";" << l_n[2] << ";" << l_n[3] << " maxdist: " << l_max_dist << " end" << std::endl;
+ int l_minx = ap_depth->width;
+ int l_miny = ap_depth->height;
+ int l_maxx = 0;
+ int l_maxy = 0;
+
+ for (int l_i = 0; l_i < (int)a_chain.size(); ++l_i)
+ {
+ l_minx = std::min(l_minx, a_chain[l_i].x);
+ l_miny = std::min(l_miny, a_chain[l_i].y);
+ l_maxx = std::max(l_maxx, a_chain[l_i].x);
+ l_maxy = std::max(l_maxy, a_chain[l_i].y);
+ }
+ int l_w = l_maxx - l_minx + 1;
+ int l_h = l_maxy - l_miny + 1;
+ int l_nn = a_chain.size();
+
+ CvPoint * lp_chain = new CvPoint[l_nn];
+
+ for (int l_i = 0; l_i < l_nn; ++l_i)
+ lp_chain[l_i] = a_chain[l_i];
+
+ cvFillPoly(lp_mask, &lp_chain, &l_nn, 1, cvScalar(255, 255, 255));
+
+ delete[] lp_chain;
+
+ //cv_show_image(lp_mask,"hallo1");
+
+ std::vector<cv::Point3d> lp_dst_3Dpts(l_h * l_w);
+
+ int l_ind = 0;
+
+ for (int l_r = 0; l_r < l_h; ++l_r)
+ {
+ for (int l_c = 0; l_c < l_w; ++l_c)
+ {
+ lp_dst_3Dpts[l_ind].x = l_c + l_minx;
+ lp_dst_3Dpts[l_ind].y = l_r + l_miny;
+ lp_dst_3Dpts[l_ind].z = CV_IMAGE_ELEM(ap_depth, unsigned short, l_r + l_miny, l_c + l_minx);
+ ++l_ind;
+ }
+ }
+ reprojectPoints(lp_dst_3Dpts, lp_dst_3Dpts, f);
+
+ l_ind = 0;
+
+ for (int l_r = 0; l_r < l_h; ++l_r)
+ {
+ for (int l_c = 0; l_c < l_w; ++l_c)
+ {
+ float l_dist = l_n[0] * lp_dst_3Dpts[l_ind].x + l_n[1] * lp_dst_3Dpts[l_ind].y + lp_dst_3Dpts[l_ind].z * l_n[2] + l_n[3];
+
+ ++l_ind;
+
+ if (CV_IMAGE_ELEM(lp_mask, unsigned char, l_r + l_miny, l_c + l_minx) != 0)
+ {
+ if (fabs(l_dist) < std::max(l_thres, (l_max_dist * 2.0f)))
+ {
+ for (int l_p = 0; l_p < (int)a_masks.size(); ++l_p)
+ {
+ int l_col = (l_c + l_minx) / (l_p + 1.0);
+ int l_row = (l_r + l_miny) / (l_p + 1.0);
+
+ CV_IMAGE_ELEM(a_masks[l_p], unsigned char, l_row, l_col) = 0;
+ }
+ }
+ else
+ {
+ for (int l_p = 0; l_p < (int)a_masks.size(); ++l_p)
+ {
+ int l_col = (l_c + l_minx) / (l_p + 1.0);
+ int l_row = (l_r + l_miny) / (l_p + 1.0);
+
+ CV_IMAGE_ELEM(a_masks[l_p], unsigned char, l_row, l_col) = 255;
+ }
+ }
+ }
+ }
+ }
+ cvReleaseImage(&lp_mask);
+ cvReleaseMat(&lp_pts);
+ cvReleaseMat(&lp_w);
+ cvReleaseMat(&lp_v);
+}
+
+void subtractPlane(const cv::Mat& depth, cv::Mat& mask, std::vector<CvPoint>& chain, double f)
+{
+ mask = cv::Mat::zeros(depth.size(), CV_8U);
+ std::vector<IplImage*> tmp;
+ IplImage mask_ipl = mask;
+ tmp.push_back(&mask_ipl);
+ IplImage depth_ipl = depth;
+ filterPlane(&depth_ipl, tmp, chain, f);
+}
+
+std::vector<CvPoint> maskFromTemplate(const std::vector<cv::linemod::Template>& templates,
+ int num_modalities, cv::Point offset, cv::Size size,
+ cv::Mat& mask, cv::Mat& dst)
+{
+ templateConvexHull(templates, num_modalities, offset, size, mask);
+
+ const int OFFSET = 30;
+ cv::dilate(mask, mask, cv::Mat(), cv::Point(-1,-1), OFFSET);
+
+ CvMemStorage * lp_storage = cvCreateMemStorage(0);
+ CvTreeNodeIterator l_iterator;
+ CvSeqReader l_reader;
+ CvSeq * lp_contour = 0;
+
+ cv::Mat mask_copy = mask.clone();
+ IplImage mask_copy_ipl = mask_copy;
+ cvFindContours(&mask_copy_ipl, lp_storage, &lp_contour, sizeof(CvContour),
+ CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
+
+ std::vector<CvPoint> l_pts1; // to use as input to cv_primesensor::filter_plane
+
+ cvInitTreeNodeIterator(&l_iterator, lp_contour, 1);
+ while ((lp_contour = (CvSeq *)cvNextTreeNode(&l_iterator)) != 0)
+ {
+ CvPoint l_pt0;
+ cvStartReadSeq(lp_contour, &l_reader, 0);
+ CV_READ_SEQ_ELEM(l_pt0, l_reader);
+ l_pts1.push_back(l_pt0);
+
+ for (int i = 0; i < lp_contour->total; ++i)
+ {
+ CvPoint l_pt1;
+ CV_READ_SEQ_ELEM(l_pt1, l_reader);
+ /// @todo Really need dst at all? Can just as well do this outside
+ cv::line(dst, l_pt0, l_pt1, CV_RGB(0, 255, 0), 2);
+
+ l_pt0 = l_pt1;
+ l_pts1.push_back(l_pt0);
+ }
+ }
+ cvReleaseMemStorage(&lp_storage);
+
+ return l_pts1;
+}
+
+// Adapted from cv_show_angles
+cv::Mat displayQuantized(const cv::Mat& quantized)
+{
+ cv::Mat color(quantized.size(), CV_8UC3);
+ for (int r = 0; r < quantized.rows; ++r)
+ {
+ const uchar* quant_r = quantized.ptr(r);
+ cv::Vec3b* color_r = color.ptr<cv::Vec3b>(r);
+
+ for (int c = 0; c < quantized.cols; ++c)
+ {
+ cv::Vec3b& bgr = color_r[c];
+ switch (quant_r[c])
+ {
+ case 0: bgr[0]= 0; bgr[1]= 0; bgr[2]= 0; break;
+ case 1: bgr[0]= 55; bgr[1]= 55; bgr[2]= 55; break;
+ case 2: bgr[0]= 80; bgr[1]= 80; bgr[2]= 80; break;
+ case 4: bgr[0]=105; bgr[1]=105; bgr[2]=105; break;
+ case 8: bgr[0]=130; bgr[1]=130; bgr[2]=130; break;
+ case 16: bgr[0]=155; bgr[1]=155; bgr[2]=155; break;
+ case 32: bgr[0]=180; bgr[1]=180; bgr[2]=180; break;
+ case 64: bgr[0]=205; bgr[1]=205; bgr[2]=205; break;
+ case 128: bgr[0]=230; bgr[1]=230; bgr[2]=230; break;
+ case 255: bgr[0]= 0; bgr[1]= 0; bgr[2]=255; break;
+ default: bgr[0]= 0; bgr[1]=255; bgr[2]= 0; break;
+ }
+ }
+ }
+
+ return color;
+}
+
+// Adapted from cv_line_template::convex_hull
+void templateConvexHull(const std::vector<cv::linemod::Template>& templates,
+ int num_modalities, cv::Point offset, cv::Size size,
+ cv::Mat& dst)
+{
+ std::vector<cv::Point> points;
+ for (int m = 0; m < num_modalities; ++m)
+ {
+ for (int i = 0; i < (int)templates[m].features.size(); ++i)
+ {
+ cv::linemod::Feature f = templates[m].features[i];
+ points.push_back(cv::Point(f.x, f.y) + offset);
+ }
+ }
+
+ std::vector<cv::Point> hull;
+ cv::convexHull(points, hull);
+
+ dst = cv::Mat::zeros(size, CV_8U);
+ const int hull_count = hull.size();
+ const cv::Point* hull_pts = &hull[0];
+ cv::fillPoly(dst, &hull_pts, &hull_count, 1, cv::Scalar(255));
+}
+
+void drawResponse(const std::vector<cv::linemod::Template>& templates,
+ int num_modalities, cv::Mat& dst, cv::Point offset, int T)
+{
+ static const cv::Scalar COLORS[5] = { CV_RGB(0, 0, 255),
+ CV_RGB(0, 255, 0),
+ CV_RGB(255, 255, 0),
+ CV_RGB(255, 140, 0),
+ CV_RGB(255, 0, 0) };
+
+ for (int m = 0; m < num_modalities; ++m)
+ {
+ // NOTE: Original demo recalculated max response for each feature in the TxT
+ // box around it and chose the display color based on that response. Here
+ // the display color just depends on the modality.
+ cv::Scalar color = COLORS[m];
+
+ for (int i = 0; i < (int)templates[m].features.size(); ++i)
+ {
+ cv::linemod::Feature f = templates[m].features[i];
+ cv::Point pt(f.x + offset.x, f.y + offset.y);
+ cv::circle(dst, pt, T / 2, color);
+ }
+ }
+}
projects / platform / upstream / opencv.git / commitdiff