--- a/scripts/dltrack.py	Thu Feb 15 14:09:23 2024 -0500
+++ b/scripts/dltrack.py	Thu Feb 15 14:09:52 2024 -0500
@@ -26,14 +26,14 @@
 parser.add_argument('-m', dest = 'detectorFilename', help = 'name of the detection model file', required = True)
 parser.add_argument('-t', dest = 'trackerFilename', help = 'name of the tracker file', required = True)
 parser.add_argument('-o', dest = 'homographyFilename', help = 'filename of the homography matrix')
-#parser.add_argument('-k', dest = 'maskFilename', help = 'name of the mask file')
+parser.add_argument('-k', dest = 'maskFilename', help = 'name of the mask file')
 parser.add_argument('--undistort', dest = 'undistort', help = 'undistort the video', action = 'store_true')
 parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file')
 parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float)
 parser.add_argument('--display', dest = 'display', help = 'show the raw detection and tracking results', action = 'store_true')
 parser.add_argument('--no-image-coordinates', dest = 'notSavingImageCoordinates', help = 'not saving the raw detection and tracking results', action = 'store_true')
 parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int, default = 0)
-parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = float('Inf'))
+parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = inf)
 parser.add_argument('--conf', dest = 'confidence', help = 'object confidence threshold for detection', type = float, default = 0.25)
 parser.add_argument('--bike-prop', dest = 'bikeProportion', help = 'minimum proportion of time a person classified as bike or motorbike to be classified as cyclist', type = float, default = 0.2)
 parser.add_argument('--cyclist-iou', dest = 'cyclistIou', help = 'IoU threshold to associate a bike and ped bounding box', type = float, default = 0.15)
@@ -56,7 +56,13 @@
 elif args.configFilename is not None:
     lastFrameNum = params.lastFrameNum
 else:
-    lastFrameNum = inf
+    lastFrameNum = args.lastFrameNum
+if args.maskFilename is not None:
+    mask = cv2.imread(args.maskFilename, cv2.IMREAD_GRAYSCALE)
+elif params.maskFilename is not None:
+    mask = cv2.imread(params.maskFilename, cv2.IMREAD_GRAYSCALE)
+else:
+    mask = None
 
 # TODO use mask, remove short objects, smooth
 
@@ -86,32 +92,37 @@
     print('Input {} could not be read. Exiting'.format(args.videoFilename))
     import sys; sys.exit()
 
-results = model.track(frame, tracker=args.trackerFilename, classes=list(moving.cocoTypeNames.keys()), conf = args.confidence, persist=True, verbose=False)
+results = model.track(source=frame, tracker=args.trackerFilename, classes=list(moving.cocoTypeNames.keys()), conf=args.confidence, persist=True, verbose=False)
 while capture.isOpened() and success and frameNum <= lastFrameNum:
     result = results[0]
     if frameNum %10 == 0:
         print(frameNum, len(result.boxes), 'objects')
     for box in result.boxes:
-        if box.id is not None: # None are objects with low confidence
-            num = int(box.id.item())
-            if num in objects:
-                objects[num].timeInterval.last = frameNum
-                objects[num].features[0].timeInterval.last = frameNum
-                objects[num].features[1].timeInterval.last = frameNum
-                objects[num].bboxes[frameNum] = copy(box.xyxy)
-                objects[num].userTypes.append(moving.coco2Types[int(box.cls.item())])
-                objects[num].features[0].tmpPositions[frameNum] = moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item()) # min
-                objects[num].features[1].tmpPositions[frameNum] = moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item()) # max
-            else:
-                inter = moving.TimeInterval(frameNum, frameNum)
-                objects[num] = moving.MovingObject(num, inter)
-                objects[num].bboxes = {frameNum: copy(box.xyxy)}
-                objects[num].userTypes = [moving.coco2Types[int(box.cls.item())]]
-                objects[num].features = [moving.MovingObject(featureNum, copy(inter)), moving.MovingObject(featureNum+1, copy(inter))]
-                objects[num].featureNumbers = [featureNum, featureNum+1]
-                objects[num].features[0].tmpPositions = {frameNum: moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())}
-                objects[num].features[1].tmpPositions = {frameNum: moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())}
-                featureNum += 2
+        if box.id is not None:# None are objects with low confidence
+            xyxy = copy(box.xyxy)
+            minPoint = moving.Point(xyxy[0,0].item(), xyxy[0,1].item())
+            maxPoint = moving.Point(xyxy[0,2].item(), xyxy[0,3].item())
+            center = (minPoint+maxPoint).divide(2.).asint()
+            if mask is None or mask[center.y, center.x] > 0:
+                num = int(box.id.item())
+                if num in objects:
+                    objects[num].timeInterval.last = frameNum
+                    objects[num].features[0].timeInterval.last = frameNum
+                    objects[num].features[1].timeInterval.last = frameNum
+                    objects[num].bboxes[frameNum] = xyxy
+                    objects[num].userTypes.append(moving.coco2Types[int(box.cls.item())])
+                    objects[num].features[0].tmpPositions[frameNum] = minPoint # min
+                    objects[num].features[1].tmpPositions[frameNum] = maxPoint # max
+                else:
+                    inter = moving.TimeInterval(frameNum, frameNum)
+                    objects[num] = moving.MovingObject(num, inter)
+                    objects[num].bboxes = {frameNum: copy(xyxy)}
+                    objects[num].userTypes = [moving.coco2Types[int(box.cls.item())]]
+                    objects[num].features = [moving.MovingObject(featureNum, copy(inter)), moving.MovingObject(featureNum+1, copy(inter))]
+                    objects[num].featureNumbers = [featureNum, featureNum+1]
+                    objects[num].features[0].tmpPositions = {frameNum: minPoint}
+                    objects[num].features[1].tmpPositions = {frameNum: maxPoint}
+                    featureNum += 2
     if args.display:
         cvutils.cvImshow(windowName, result.plot()) # original image in orig_img
         key = cv2.waitKey()
@@ -119,7 +130,9 @@
             break
     frameNum += 1
     success, frame = capture.read()
-    results = model.track(frame, persist=True)
+    results = model.track(source=frame, persist=True)
+capture.release()
+cv2.destroyAllWindows()
 
 # classification
 for num, obj in objects.items():
@@ -221,10 +234,7 @@
     else:
         t = []
         for instant in obj.getTimeInterval():
-            points = []
-            for f in features:
-                if f.existsAtInstant(instant):
-                    points.append(f.getPositionAtInstant(instant))
+            points = [f.getPositionAtInstant(instant) for f in features if f.existsAtInstant(instant)]
             t.append(moving.Point.agg(points, np.mean).aslist())
         #t = sum([f.getPositions().asArray() for f in features])/len(features)
         #t = (moving.Trajectory.add(t1, t2)*0.5).asArray()