--- a/scripts/dltrack.py	Thu Jul 13 17:40:37 2023 -0400
+++ b/scripts/dltrack.py	Mon Aug 21 15:49:32 2023 -0400
@@ -12,6 +12,44 @@
 parser.add_argument('--display', dest = 'display', help = 'show the results (careful with long videos, risk of running out of memory)', action = 'store_true')
 args = parser.parse_args()
 
+# required functionality?
+# # filename of the video to process (can be images, eg image%04d.png)
+# video-filename = laurier.avi
+# # filename of the database where results are saved
+# database-filename = laurier.sqlite
+# # filename of the homography matrix
+# homography-filename = laurier-homography.txt
+# # filename of the camera intrinsic matrix
+# intrinsic-camera-filename = intrinsic-camera.txt
+# # -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816
+# distortion-coefficients = -0.11759321
+# distortion-coefficients = 0.0148536
+# distortion-coefficients = 0.00030756 
+# distortion-coefficients = -0.00020578 
+# distortion-coefficients = -0.00091816
+# # undistorted image multiplication
+# undistorted-size-multiplication = 1.31
+# # Interpolation method for remapping image when correcting for distortion: 0 for INTER_NEAREST - a nearest-neighbor interpolation; 1 for INTER_LINEAR - a bilinear interpolation (used by default); 2 for INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood; 3 for INTER_LANCZOS4
+# interpolation-method = 1
+# # filename of the mask image (where features are detected)
+# mask-filename = none
+# # undistort the video for feature tracking
+# undistort = false
+# # load features from database
+# load-features = false
+# # display trajectories on the video
+# display = false
+# # original video frame rate (number of frames/s)
+# video-fps = 29.97
+# # number of digits of precision for all measurements derived from video
+# # measurement-precision = 3
+# # first frame to process
+# frame1 = 0
+# # number of frame to process: 0 means processing all frames
+# nframes = 0
+
+# TODO add option to refine position with mask for vehicles
+
 # Load a model
 model = YOLO('/home/nicolas/Research/Data/classification-models/yolov8x.pt') # seg yolov8x-seg.pt
 # seg could be used on cropped image... if can be loaded and kept in memory
@@ -23,5 +61,6 @@
 else:
     results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(cocoTypeNames.keys()), stream=True)
     for result in results:
+        print(len(result.boxes))
         for box in result.boxes:
             print(box.xyxy)