accelerate lane evaluation by openmp with 20x

ERFNet-CULane-PyTorch/tools/lane_evaluation/Makefile

@@ -15,7 +15,7 @@ BUILD_DIR := build
 CXX ?= g++
 BUILD_DIR ?= ./build
 
-LIBRARIES += opencv_core opencv_highgui opencv_imgproc
+LIBRARIES += opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
 
 CXXFLAGS += $(COMMON_FLAGS) $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
 LDFLAGS +=  $(COMMON_FLAGS) $(foreach includedir,$(LIBRARY_DIRS),-L$(includedir)) $(foreach library,$(LIBRARIES),-l$(library))

ERFNet-CULane-PyTorch/tools/lane_evaluation/include/counter.hpp

@@ -5,6 +5,7 @@
 #include "hungarianGraph.hpp"
 #include <iostream>
 #include <algorithm>
+#include <tuple>
 #include <vector>
 #include <opencv2/core/core.hpp>
 
@@ -26,9 +27,12 @@ class Counter
 		long getTP(void);
 		long getFP(void);
 		long getFN(void);
+		void setTP(long);
+		void setFP(long);
+		void setFN(long);
 		// direct add tp, fp, tn and fn
 		// first match with hungarian
-		vector<int> count_im_pair(const vector<vector<Point2f> > &anno_lanes, const vector<vector<Point2f> > &detect_lanes);
+		tuple<vector<int>, long, long, long, long> count_im_pair(const vector<vector<Point2f> > &anno_lanes, const vector<vector<Point2f> > &detect_lanes);
 		void makeMatch(const vector<vector<double> > &similarity, vector<int> &match1, vector<int> &match2);
 
 	private:

ERFNet-CULane-PyTorch/tools/lane_evaluation/include/lane_compare.hpp

@@ -5,6 +5,7 @@
 #include <vector>
 #include <iostream>
 #include <opencv2/core/core.hpp>
+#include <opencv2/imgproc.hpp>
 
 using namespace std;
 using namespace cv;

ERFNet-CULane-PyTorch/tools/lane_evaluation/src/counter.cpp

@@ -43,20 +43,33 @@ long Counter::getFN(void)
 	return fn;
 }
 
-vector<int> Counter::count_im_pair(const vector<vector<Point2f> > &anno_lanes, const vector<vector<Point2f> > &detect_lanes)
+void Counter::setTP(long value) 
+{
+	tp = value;
+}
+
+void Counter::setFP(long value)
+{
+  fp = value;
+}
+
+void Counter::setFN(long value)
+{
+	fn = value;
+}
+
+tuple<vector<int>, long, long, long, long> Counter::count_im_pair(const vector<vector<Point2f> > &anno_lanes, const vector<vector<Point2f> > &detect_lanes)
 {
 	vector<int> anno_match(anno_lanes.size(), -1);
 	vector<int> detect_match;
 	if(anno_lanes.empty())
 	{
-		fp += detect_lanes.size();
-		return anno_match;
+		return make_tuple(anno_match, 0, detect_lanes.size(), 0, 0);
 	}
 
 	if(detect_lanes.empty())
 	{
-		fn += anno_lanes.size();
-		return anno_match;
+		return make_tuple(anno_match, 0, 0, 0, anno_lanes.size());
 	}
 	// hungarian match first
 
@@ -92,10 +105,7 @@ vector<int> Counter::count_im_pair(const vector<vector<Point2f> > &anno_lanes, c
 	}
 	int curr_fn = anno_lanes.size() - curr_tp;
 	int curr_fp = detect_lanes.size() - curr_tp;
-	tp += curr_tp;
-	fn += curr_fn;
-	fp += curr_fp;
-	return anno_match;
+	return make_tuple(anno_match, curr_tp, curr_fp, 0, curr_fn);
 }
 
 

ERFNet-CULane-PyTorch/tools/lane_evaluation/src/evaluate.cpp

@@ -130,12 +130,20 @@ int main(int argc, char **argv)
 
 	vector<int> anno_match;
 	string sub_im_name;
-	int count = 0;
-	while(getline(ifs_im_list, sub_im_name))
+  // pre-load filelist
+  vector<string> filelists;
+  while (getline(ifs_im_list, sub_im_name)) {
+    filelists.push_back(sub_im_name);
+  }
+  ifs_im_list.close();
+
+  vector<tuple<vector<int>, long, long, long, long>> tuple_lists;
+  tuple_lists.resize(filelists.size());
+
+#pragma omp parallel for
+	for (size_t i = 0; i < filelists.size(); i++)
 	{
-		count++;
-		if (count < frame)
-			continue;
+		auto sub_im_name = filelists[i];
 		string full_im_name = im_dir + sub_im_name;
 		string sub_txt_name =  sub_im_name.substr(0, sub_im_name.find_last_of(".")) + ".lines.txt";
 		string anno_file_name = anno_dir + sub_txt_name;
@@ -145,14 +153,25 @@ int main(int argc, char **argv)
 		read_lane_file(anno_file_name, anno_lanes);
 		read_lane_file(detect_file_name, detect_lanes);
 		//cerr<<count<<": "<<full_im_name<<endl;
-		anno_match = counter.count_im_pair(anno_lanes, detect_lanes);
+		tuple_lists[i] = counter.count_im_pair(anno_lanes, detect_lanes);
 		if (show)
 		{
+			auto anno_match = get<0>(tuple_lists[i]);
 			visualize(full_im_name, anno_lanes, detect_lanes, anno_match, width_lane);
 			waitKey(0);
 		}
 	}
-	ifs_im_list.close();
+
+	long tp = 0, fp = 0, tn = 0, fn = 0;
+  for (auto result: tuple_lists) {
+    tp += get<1>(result);
+    fp += get<2>(result);
+    // tn = get<3>(result);
+    fn += get<4>(result);
+  }
+	counter.setTP(tp);
+	counter.setFP(fp);
+	counter.setFN(fn);
 
 	double precision = counter.get_precision();
 	double recall = counter.get_recall();