# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1464728843 14400
# Node ID c5f98916297edef000a19d9e7d25bfa248a34a77
# Parent  a34ec862371fc053078d6e87b5b63f6bbc1fb481# Parent  2cade72d75ad95eda3d4de19113aca2621975120
merged with dev branch

diff -r a34ec862371f -r c5f98916297e c/feature-based-tracking.cpp
--- a/c/feature-based-tracking.cpp	Mon May 09 15:33:11 2016 -0400
+++ b/c/feature-based-tracking.cpp	Tue May 31 17:07:23 2016 -0400
@@ -62,10 +62,6 @@
 }
 
 void trackFeatures(const KLTFeatureTrackingParameters& params) {
-  // BriefDescriptorExtractor brief(32);
-  // const int DESIRED_FTRS = 500;
-  // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
-
   Mat homography = ::loadMat(params.homographyFilename, " ");
   Mat invHomography;
   if (params.display && !homography.empty())
@@ -174,8 +170,7 @@
 	break;
       }
     }
-
-      
+    
     cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
     
     if (!prevPts.empty()) {
@@ -188,9 +183,9 @@
       while (iter != featurePointMatches.end()) {
 	bool deleteFeature = false;
 	  
-	if (status[iter->pointNum]) {
+	if (status[iter->pointNum] && (mask.at<uchar>(static_cast<int>(round(currPts[iter->pointNum].y)), static_cast<int>(round(currPts[iter->pointNum].x))) != 0)) {
 	  iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography);
-
+	  
 	  deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement)
 	    || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound);
 	  if (deleteFeature)
@@ -219,14 +214,9 @@
       if (params.display) {
 	BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
 	  fp.feature->draw(frame, invHomography, Colors::red());
-	// object detection
-	// vector<Rect> locations;
-	// hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2);
-	// BOOST_FOREACH(Rect r, locations)
-	//   rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3);
       }
     }
-      
+    
     // adding new features, using mask around existing feature positions
     Mat featureMask = mask.clone();
     for (unsigned int n=0;n<currPts.size(); n++)
@@ -234,7 +224,7 @@
 	for (int i=MAX(0, currPts[n].y-params.minFeatureDistanceKLT); i<MIN(videoSize.height, currPts[n].y+params.minFeatureDistanceKLT+1); i++)
 	  featureMask.at<uchar>(i,j)=0;
     goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k);
-    BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
+    BOOST_FOREACH(Point2f p, newPts) {
       FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography));
       featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
       currPts.push_back(p);
diff -r a34ec862371f -r c5f98916297e python/cvutils.py
--- a/python/cvutils.py	Mon May 09 15:33:11 2016 -0400
+++ b/python/cvutils.py	Tue May 31 17:07:23 2016 -0400
@@ -218,31 +218,33 @@
         else:
             print('Video capture for {} failed'.format(filename))
 
-    def getImagesFromVideo(videoFilename, firstFrameNum = 0, nFrames = 1, saveImage = False, outputPrefix = 'image'):
+    def getImagesFromVideo(videoFilename, firstFrameNum = 0, lastFrameNum = 1, step = 1, saveImage = False, outputPrefix = 'image'):
         '''Returns nFrames images from the video sequence'''
         images = []
         capture = cv2.VideoCapture(videoFilename)
         if capture.isOpened():
             rawCount = capture.get(cv2.CAP_PROP_FRAME_COUNT)
             if rawCount < 0:
-                rawCount = firstFrameNum+nFrames+1
+                rawCount = lastFrameNum+1
             nDigits = int(floor(log10(rawCount)))+1
             ret = False
             capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum)
-            imgNum = 0
-            while imgNum<nFrames:
+            frameNum = firstFrameNum
+            while frameNum<=lastFrameNum and frameNum<rawCount:
                 ret, img = capture.read()
                 i = 0
                 while not ret and i<10:
                     ret, img = capture.read()
                     i += 1
-                if img.size>0:
+                if img is not None and img.size>0:
                     if saveImage:
-                        imgNumStr = format(firstFrameNum+imgNum, '0{}d'.format(nDigits))
-                        cv2.imwrite(outputPrefix+imgNumStr+'.png', img)
+                        frameNumStr = format(frameNum, '0{}d'.format(nDigits))
+                        cv2.imwrite(outputPrefix+frameNumStr+'.png', img)
                     else:
                         images.append(img)
-                    imgNum +=1
+                    frameNum +=step
+                    if step > 1:
+                        capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
             capture.release()
         else:
             print('Video capture for {} failed'.format(videoFilename))
diff -r a34ec862371f -r c5f98916297e python/traffic_engineering.py
--- a/python/traffic_engineering.py	Mon May 09 15:33:11 2016 -0400
+++ b/python/traffic_engineering.py	Tue May 31 17:07:23 2016 -0400
@@ -309,7 +309,11 @@
 
 def uniformDelay(cycleLength, effectiveGreen, saturationDegree):
     '''Computes the uniform delay'''
-    return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)/(1-float(effectiveGreen*saturationDegree)/cycleLength)
+    return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)**2/(1-float(effectiveGreen*saturationDegree)/cycleLength)
+
+def randomDelay(volume, saturationDegree):
+    '''Computes the random delay = queueing time for M/D/1'''
+    return saturationDegree**2/(2*volume*(1-saturationDegree))
 
 def incrementalDelay(T, X, c, k=0.5, I=1):
     '''Computes the incremental delay (HCM)